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Haptic Feedback in Needle Insertion Modeling and Simulation: Review

May 2017
IEEE Reviews in Biomedical Engineering PP(99)

May 2017
PP(99)

DOI:10.1109/RBME.2017.2706966

Authors:

Gourishetti Ravali

Max Planck Institute for Intelligent Systems

Manivannan Muniyandi

Indian Institute of Technology Madras

Needle insertion being the most basic skill in medical care, training has to be imparted not only for physicians but also for nurses and paramedics. In most needle insertion procedures haptic feedback from the needle is the main stimulus that novices are to be trained in. For better patient safety, the classical methods of training the haptic skills have to be replaced with simulators based on new robotic and graphics technologies. This paper reviews the current advances in needle insertion modeling. It is classified into three sections: needle insertion models, tissue deformation models, and needle-tissue interaction models. Although understated in the literature, the classical and dynamic friction models which are critical for needle insertion modeling are also discussed. The experimental setup or the needle simulators which have been developed to validate the models are described. The need of psychophysics for needle simulators and psychophysical parameter analysis of human perception in needle insertion are discussed, which are completely ignored in the literature.

Schematic of needle insertion simulation

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Forces involved during needle insertion procedures [43] .

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Angles of bending and twisting between two needle segments [45] .

…

Schematic indication of needle insertion forces and tissue deformation [20] .

…

Several needle path solutions for the same tip position with different tip inclinations [36]

…

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in Biomedical Engineering

Haptic Feedback in Needle Insertion Modeling and

Simulation: Review

Gourishetti Ravali, Muniyandi Manivannan∗

Abstract—Needle insertion being the most basic skill in medical

care, training has to be imparted not only for physicians but also

for nurses and paramedics. In most needle insertion procedures

haptic feedback from the needle is the main stimulus that novices

are to be trained in. For better patient safety, the classical

methods of training the haptic skills have to be replaced with

simulators based on new robotic and graphics technologies.

This paper reviews the current advances in needle insertion

modeling. It is classiﬁed into three sections: needle insertion

models, tissue deformation models, and needle-tissue interaction

models. Although understated in the literature, the classical and

dynamic friction models which are critical for needle insertion

modeling are also discussed. The experimental set-up or the

needle simulators which have been developed to validate the

models are described. The need of psychophysics for needle

simulators and psychophysical parameter analysis of human

perception in needle insertion are discussed, which are completely

ignored in the literature.

Index Terms—haptic feedback; minimally invasive procedure;

needle insertion procedures; needle insertion modeling; psy-

chophysics

I. INT ROD UC TI ON

MINIMALLY invasive procedures have become impor-

tant medical techniques in many modern clinical prac-

tices due to their advantage of having small incisions compared

to traditional surgical procedures with large openings. Needle

insertion procedure is a vital element in most minimally

invasive procedures [1]. The various applications of needle

insertion in minimally invasive procedures would include

needle biopsies [2], [3]; regional anesthesia [4], [5]; injections

[6]; angiography [7]; laparoscopy which requires veress needle

insertion [8], [9]; endoscopy [10]; brachytherapy cancer treat-

ment [11]–[13]; spine and neurosurgery [14], [15]. Therefore,

training the novice with needle insertion plays a major role

in the current practice of healthcare. Needle insertion is one

of the basic skills in medical care that all clinicians have

to be trained in to have good efﬁciency and high accuracy.

During the training process of clinicians, long period of

time is allocated for developing psychomotor skills such as

needle insertion and establishing an intravenous drip [16]. It

is considered that the success rate is directly proportional to

the skills and the experience of the physician or clinician [17].

Needle insertion procedures involve three basic steps where

physicians can make mistakes: determination of the insertion

point, needle orientation, and needle movement into soft

tissues. Besides these, the physician may get deﬂected from

M.Manivannan∗is with Haptics Lab, Biomedical Engineering Group, Ap-

plied Mechanics Department, Indian Institute of Technology Madras, Chennai-

600036. (corresponding author email id: mani@iitm.ac.in).

the target because of tissue deformation [18] and motion

artifacts of the patient. The needle’s path may traverse some

sensitive tissues such as nerves, bones, arteries, or organs.

Adverse damage to these tissues may lead to many side

effects. Therefore, it is important that the needle does not

cause any damage to these crucial tissues [19]. Other causes of

inaccuracy in needle procedures are due to the differences in

tissue type involved in each procedure, physiological changes

in the organ between the planning and treatment phases,

differences in mechanical properties of healthy and diseased

tissue or dead tissue (cadavers), and the variability in the

properties of soft tissue for different patients [20].

A. Problems in needle insertion training

Traditionally, physicians are trained with cadavers, porcine,

or bovine samples and then with anesthetized animals. How-

ever, the material properties of the tissues would vary between

cadavers and live patients [20]. Thus, these traditional tech-

niques may lead to several inaccuracies with live patients,

resulting in a lack of representative materials for accurate

training. The current method of training novice for needle

procedures in medical schools is by apprenticeship method

in which the patient’s safety is at risk [21]. Even traditionally

trained doctors who performed needle procedures have shown

high failure rates as shown in a recent survey [22]. This

survey was performed at the University of Washington Medical

Center on Acute Pain and they found a failure rate of 32%

for thoracic needle procedures and 27% for lumbar epidural

anesthesia procedures. They have also classiﬁed the causes

of these failures (their failure rates) into ﬁve categories as

dislodged catheter (17%), not in epidural space (11%), one-

sided block (7%), develop a leak (7%), uncertain cause (58%).

With increased focus on patient safety, new technological

training solutions are essential for reducing errors during the

needle procedures.

B. Medical simulation as a technological solution

Medical simulation is one of the widely used technologies

in clinical education with the goal of training the novice

in medical skills and improving the learners’ efﬁciency and

conﬁdence in the corresponding tasks. Medical simulators

have multiple beneﬁts over traditional training methods such as

patients’ safety, physicians get to trained in different scenarios

by changing the properties of the tissues in an administered

environment that make them familiar with both common and

rare cases [23], and ﬂexible training opportunities as trainees

do not require a direct supervision of trained clinicians [24].

in Biomedical Engineering

The major advantage of medical simulators is the quantiﬁca-

tion of performance during the training session which will help

the trainees with steeper learning curves, eventually reducing

their failure rates [25]. Since accurate force feedback is the key

factor in needle simulators, it is necessary that the simulators

provide better haptic feedback to train the physicians [17].

C. Haptic Feedback in Medical Simulators

Many medical procedures often involve the use of the force

feedback to manipulate organs or tissues by using special

tools [26]. Some of the medical procedures such as palpation,

surgical interventions, phlebotomy and other interventional

procedures are examples where the force feedback is of great

importance [27]. In minimally invasive procedures, the force

feedback has reduced compared to open surgery where the

clinicians rely more on the feeling of net forces resulting from

tool-tissue interactions and thereby there is a high require-

ment for training to have a successful surgery. The training

for minimally invasive procedures depends on the education

and improvement of the trainee’s haptic sensorimotor system

[28]. In order to enhance the training applications, medical

simulators are equipped with haptic feedback [29]. The need

of haptic feedback starts with the basic physical examination

technique, palpation which is a supplementary interaction

technique to diagnose a disease or illness [30]. Therefore,

the haptic feedback is the basic parameter for the palpation

simulators.

During the needle insertion procedures, doctors rely on both

visual and haptic feedback. The visual feedback (to determine

the position of the needle tip) is acquired from the segment of

the needle remaining outside the body. Physicians also rely on

their mental 3D visualization of the anatomical structures [31].

Haptic feedback has great signiﬁcance as the insertion forces

vary depending on the depth of penetration and the type of

tissue through which the needle penetrates. The detection of

changes in tissue properties at different depths during needle

insertion is important for needle localization and detection of

subsurface structures. However, changes in tissue mechanical

properties deep inside the tissue are difﬁcult for the novice to

sense because the relatively large friction force between the

needle shaft and the surrounding tissue masks the smaller tip

forces [32]. This method of predicting the location of a needle

inside the body is possible only with in vivo experience, which

is a great challenge for the novice and therefore, the need

of training with force feedback is substantially increased and

thereby the haptic feedback is included as a key parameter

during the development of needle simulators.

D. Survey Method

The survey includes papers that discussed needle deﬂection

models, tissue deformation models, needle-tissue interaction

force models, friction models, details of experiments related

to needle insertion and also few papers related to need of psy-

chophysics for medical simulators. The papers for the review

were chosen from Google Scholar, the Scitopia.org search

engine, and the Medline/PubMed database. The sections in

the review have been organized based on the topics mentioned

above.

E. Scope of this Review

This paper reviews the current state-of-the-art needle in-

sertion simulations. It also elaborates the need for frictional

models which are neglected in the literature and introduces

the need for psychophysics in the design of needle insertion

simulators towards the improvement in accuracy.

II. OVE RVIEW OF NEEDLE INSERTION MODELIN G

The needle insertion simulation procedures would include

three major modeling stages as shown in Fig. 1: modeling of

needle deﬂection, modeling of tissue deformation, and model-

ing of needle-tissue interaction forces. The various techniques

available for modeling these three phases are:

•Finite Element Methods (FEM) based models.

•Beam based models.

•Mass Damper models (mass is neglected).

Fig. 1: Schematic of needle insertion simulation

Using Finite Element Modeling (FEM), the deformations of

soft tissues with the force applied on the needle are computed

for both static and dynamic behaviors of soft tissues by

considering displacement, velocity, and acceleration at each

nodal point [19], [33], [34].

In beam-based modeling, the needle insertion forces are

computed. Apart from this, the bending and twisting of the

needle inside the tissues which occur due to the reactive forces

of the tissues are studied [35]–[37] as shown in Fig. 2.

Fig. 2: The distributed tissue reaction force acting along the

needle shaft to the portion of the needle that is inserted into

the tissue. FRis the reaction force at the needle base [37]

Spring-Damper models are mainly used for tissue defor-

mation modeling where the deformation forces have been

considered as pre-puncture and post-puncture forces [18], [38],

[39].

Hing et al. [17] identiﬁed that quantiﬁcation of the insertion

forces was an important factor in developing a simulator which

gives an accurate force measurement and provides an accurate

input for modeling the needle-tissue interaction. A 6 Degree

of Freedom (DOF) force sensor was used to measure the total

interaction force, which is a summation of the cutting and

frictional forces. The cutting force was measured to be the

difference of the total interaction force and the frictional force.

in Biomedical Engineering

The frictional force was measured during the withdrawal of the

needle where no cutting of the tissues was involved.

Brett et al. [40] developed an experimental simulator with

a 1 DOF force measuring arrangement. They studied the

force variation corresponding to the depth of penetration for

different velocities and found that the ligamentum ﬂavum is

the tissue which requires the highest amount of force (15

N) for its puncture. They also found the force required at

the supraspinous ligament (around 15 N) and also muscle

ﬁbers (5 N). Naemura et al. [41] developed an epidural

simulator similar to Brett’s and validated their models using

the literature data and they could ﬁnd two peaks in the process

of puncture. Their results prove that their simulator has forces

very comparable to those of a conventional simulator.

Gerwen et al. [42] provide an overview of research related to

the force measurement data obtained during needle insertion.

They have clearly classiﬁed various papers into a set of four

categories based on experimental data, statistical analysis, and

sample size. They also studied the effect of needle characteris-

tics, tissue characteristics and insertion method on axial forces.

This data can be used for needle modeling, tissue modeling,

and needle-tissue interaction modeling.

III. MO DE LI NG O F NE ED LE I NSERTION FORCES

During the needle insertion procedures, needle orientation,

needle bending, and tissue deformation are the three effects

which lead to inaccurate positioning of the needle. Reaching

the target location in a needle insertion procedures require

high skill, training, and experience. Therefore, accurate models

of needles are required to train a novice. The total reaction

force during a needle insertion procedures involves forces such

as cutting force, kinetic friction, static friction, compression

force, and torque as shown in Fig. 3.

Fig. 3: Forces involved during needle insertion procedures [43]

A. Constraints of Needle Modeling

The main constraints for making an accurate model of

insertion forces are the peak force during the insertion pro-

cedure, delay in the force changes (i.e. real-time detection of

force changes), and identiﬁcation of various parameters such

as stiffness which is the cutting force on the tissue, friction

during the sliding of the needle, and damping force [20]. Fig.

5 shows the schematic of needle insertion forces and also the

indentation caused to the tissue during the needle insertion

procedures.

B. Needle Models

The basic methods of needle modeling are as follows.

1) FEM using triangular elements: They consider the non-

linear inelastic behavior of needle forces as a function of

displacement which leads to a set of nonlinear algebraic

equations which are solved using numerical methods

[44].

2) FEM using nonlinear beam elements: They consider

the Euler-Bernoulli beam element which uses cubic

and linear interpolation functions for the transverse and

axial strain/displacement respectively. The equilibrium

equation for the axial force, lateral force, and torque are

solved using some iterative methods.

3) Angular spring model: The authors used this method to

model a needle as a beam connected at one end, i.e. a

cantilever beam. It is considered to have a number of

rigid rods connected with springs which have rotational

DOF. The nonlinear equations between the force and

the joint angles by the rotational springs are solved by

simple vector algebraic equations [45].

Goksel et al. [45] modeled a needle as a discrete structure

composed of a ﬁnite number of rigid rods. The bending and

twisting deformations in the needle structure would apply

some internal reaction forces to restore its original conﬁg-

uration. To study the effect of these deformations on the

overall insertion forces, these torques were modeled using

two rotational springs, one for unbending and the other for

untwisting the needle segment as shown in Fig. 4.

Fig. 4: Angles of bending and twisting between two needle

segments [45]

Kataoka et al. [46] modeled a needle based on the Euler-

Bernoulli beam theory that calculates the relationship between

the beam deﬂection and the applied load. This model uses

a distributed load along the needle shaft to evaluate the

deﬂection of the needle tip. However, this resulted in an

offset from the estimated needle tip deﬂection. Abolhassani

et al. [20] also studied the needle model based on the Euler-

Bernoulli beam theory as shown in Fig. 5.

Lehmann et al. [37] modeled a needle as a Euler-Bernoulli

beam structure which is considered as a cantilever beam since

it is ﬁxed only at the base. They simulated the needle insertion

forces based on the static deﬂection of the beam. These needle

in Biomedical Engineering

Fig. 5: Schematic indication of needle insertion forces and

tissue deformation [20]

insertion forces on the beam and the beam deﬂection are

given by the Euler-Bernoulli beam theory. They considered

the location of the resultant force in the space as the area

centroid of the distributed load. They formulated the force-

displacement relation by considering the force applied on the

needle base (FR)as the summation of the load per unit length

of the needle, which is given as

FR=Fq=ZL

L−l0

q(z)dz

where qrepresents the load gradient, l0represents the length

section of needle outside the tissue and Lrepresents the section

of the needle inside the tissue.

Asadian et al. [44] modeled a needle as a beam-based

model which comprises two connected beams, one beam at

the section of the needle outside the tissue, and the other at

the section of the needle inside the tissue. This representation

gives the mental visualization of the location of the needle

tip inside the tissues along with the typical haptic feedback

from the simulators. Khadem et al. [47] extended the beam

theory to develop a needle model with a novel approach by

considering velocity as an input.

Okamura et al. [48] measured the needle insertion forces

with the help of experimentation using a robotic assisted set-

up during percutaneous insertion by separation of the total

interaction forces as the cutting force and frictional force. The

cutting force is caused due to the fracture of the tissue during

needle insertion and, the frictional force is due to the sliding of

the needle shaft through the tissues. They studied the needle

bending effects and also the effect of insertion forces with

various types of needles.

Misra et al. [49] considered tissue as a nonlinear hyperelas-

tic material and modeled needle steering as an energy based

formulation. This model has the advantage of deﬂections in

2 DOF, i.e. lateral and axial deﬂection of the needle. This

model also studied the needle tip forces and the needle base

forces that are applied by the trainee. However, the effects

of frictional forces during needle insertion and the effects of

needle insertion velocity were not considered.

Mahvash and Dupont [50] studied the effect of insertion

velocity on tissue deformation and needle insertion force. They

classiﬁed the needle insertion procedures into several events

such as loading deformation, rupture, cutting, and unloading

deformation events. They considered a nonlinear visco-elastic

Kelvin model for the analysis of the relationship between

rupture force and deformation of the tissue.

Farber et al. [51] modeled a virtual needle as a rod which

provided the information on needle tip forces and needle body

forces. The needle tip forces include the pre-puncture force,

cutting force, and friction force. Needle body forces include

the restriction forces by the transversal and rotational motion

of the needle in soft tissues.

IV. MOD EL IN G OF T IS SU E DE FO RM ATIO N FO RC ES

The study of tissue deformation forces and thereby of

tissue modeling has great importance in the development of

needle simulators. This study is mainly focused on the skin,

vessels, muscles, brain, and heart tissues. Tissue modeling is

always a challenge because of various tissue properties such

as inhomogeneity, non-linearity, anisotropic layeredness, and

visco-elasticity [52]. Among these various tissue properties,

layeredness is very relevant to needle insertion. Each layer of

the tissue is modeled differently based on the corresponding

properties for particular applications. For example, in percuta-

neous therapies, when the needle punctures different tissues

such as skin, muscle, adipose tissue, and some connecting

tissues [53], each layer requires a different force to puncture

it. The force not only varies for different layers but also varies

for subjects based on their age, gender, body weight and so

on [54].

A. Constraints of Tissue Modeling

For tissue modeling in any simulator, tissue deformation,

and computation time complexity are the two main constraints.

The accuracy of tissue deformation is the main criterion in

training the novice in surgical procedures and computation

time is the main criterion in surgical planning tasks [55].

In this paper, we consider the ﬁrst criterion where training is

the primary task. Therefore, the accuracy of tissue deformation

is the primary constraint to be considered.

B. Tissue Models

The basic methods of tissue modeling are classiﬁed

into three categories: phenomenological, structural, and

structurally-based phenomenological models [56].

Phenomenological models consider tissue as a homoge-

neous material for determining the deformation mechanics,

in Biomedical Engineering

neglecting the structural properties of the tissues. The me-

chanical properties of these models are written as a constitu-

tive equation that relates stress and strain. Structural models

consider tissue as a composite material: they are described by

nonlinear algebraic equations between the stress and strain.

The third category of the model which is a combination of

the above two models can incorporate the particular structural

arrangements and deformation mechanics.

DiMaio et al. [31] and Goksel et al. [57] modeled tissue

using FEM with triangular elements. DiMaio considered tissue

as homogeneous, linear, and elastostatic material that gives

the deformations in two dimensions: axial, and transverse

direction. The tissue deformation forces are formulated using

the total strain energy Estrain phenomenon as a function of

force and displacement, given by

Estrain =1

2ZΩ

T(x)σ(x)dx

where Ωis the area of the solid body, σis stress and is

strain.

Lepiller et al. [58] modeled tissue using a novel FEM

known as Eulerian hydrocode which was developed against

large deformation problems such as plastic forming. Tissue

deformation was achieved by a drift of neighbor elements

like ﬂuid according to both Euler equation and constitutive

equations for solid.

Glozman and Shoham [36] presented the needle as a set of

virtual springs. They modeled the tissue deformation forces

as a function of needle penetration. The deeper the needle

penetrates into the tissue, the greater is the number of springs,

hence the forces are calculated considering the new set of

springs at the tip, as shown in Fig. 6.

Fig. 6: Several needle path solutions for the same tip position

with different tip inclinations [36]

Hing et al. [17] conducted an experiment to quantitatively

measure the tissue deformation forces during needle insertion.

These forces were used to model liver tissue in 3D FEM.

Tissue deformation was modeled using the mesh locations.

Xu et al. [59] modeled both the tissue and the needle as

a set of nodes and established the relation between external

force and the tissue deformation by total potential energy.

They considered the total potential energy as the difference

between the strain energy of the tissue and work done by

external forces. The tissue deformation is computed from the

external forces and nodal displacements.

Barb et al. [60] proposed an algorithm to estimate forces

in real time involved in needle insertion procedures. They

approached the model based on Diolaiti et al. [61] who

modeled soft tissues both as visco-elastic linear and nonlinear

for estimation of real-time interaction forces of robots. Barb

and Bayle used recursive parameter estimation algorithms for

the real-time estimation of forces. They also considered the

needle insertion procedures in different phases which were

mentioned in the paper earlier: ﬁrst, the needle pre-puncturing

phase which gave the details of visco-elastic properties of the

soft tissues; second, the puncturing phase which included the

force required to puncture the tissues and also the effects of

frictional force by the tissues on the shaft of the needle; and

ﬁnally, the needle retraction phase which included the fric-

tional force in the opposite direction of the puncturing phase.

The soft tissues for stiffness measurement were modeled using

two basic models, the Kelvin-Voigt model (KV model), and

the Hunt-Crossley model (HC model).

Wang et al. [62] modeled frictional force with the consid-

eration of relative velocity and contact length. The tissue is

modeled using one of the basic models, the KV model. They

considered the contact area along the length of the needle

shaft by separating the lengths into small regions dl as shown

in Fig. 7 and the total frictional force as the integral of the

entire length of the needle as

Ffriction =Z0

lc(t)

2πrRf(v∗)dl

where the co-efﬁcient of frictional force Rfis deﬁned as

Rf(v∗) = Ff riction

2πrLc

where r is the radius of the needle, lc(t)is the length of the

needle, Lcis the tissue thickness, and Ff riction is the frictional

force with respect to relative velocity.

Models of speciﬁc tissues such as arteries, ligaments,

and skin were also reported in the literature. Gasser Ogden

Holzapfel (GOH) formulated the relationship between force

and displacement that represents the orthotropic hyperelastic

behavior of arterial tissues [63]. Weiss et al. [64] modeled the

ligaments of the knee joint and formulated the transversely

isotropic hyperelastic behavior of ligaments. They considered

the strain energy function for force analysis. For soft tissues,

Limbert and Middleton [65] developed an anisotropic strain

energy function. Limbert [66] developed a novel invariant

based multiscale constitutive framework to characterize soft

tissues as transversely isotropic and with orthotropic elastic

behavior. The mechanical formulations were customized to

model skin [56].

in Biomedical Engineering

Fig. 7: Scheme of friction model during needle insertion

procedures [62]

V. MODELING NEEDLE-TISSUE INTERACT IO N FORCES

The study of needle-tissue interaction forces which are

also known as total interaction forces includes both needle

insertion and tissue deformation forces. The formulation of

total interaction forces is the major challenge in developing a

needle simulator.

With the complexities involved in needle-tissue interactions,

it is not recommended to use a single combined mesh [67].

Therefore, needle-tissue interaction simulation involves two

separate measures for tissue and needle modeling.

Golzman and Shoham [36] developed a robotic needle

simulator for needle insertion and its movement control in soft

tissues. They modeled interaction forces using a simpliﬁed vir-

tual spring model where they considered a ﬂexible needle as a

linear cantilever beam supported by a series of virtual springs.

They used forward kinematics which helps in computing the

position of the end effector to enable needle insertion path

planning and, inverse kinematics which helps in computing

the joint parameters to enable path correction in real time.

For small displacements, Simone assumed the tissue force

response on the needle shaft to be linear [39]. He modeled total

interaction force as a summation of the lateral forces along the

needle shaft in the virtual springs and frictional forces parallel

to the needle axial direction.

Yan et al. [35] proposed a spring-beam damper system by

considering tissue as nonlinear and elastic material as shown

in Fig. 8. Their model formulates the system dynamics during

force application on the needle end using Hamilton’s principle.

The ﬂexible needle is assumed to follow the Bernoulli-Euler

beam model.

Limitations of this model considered by Yan and Ng are:

1) The needle has only 2 DOF, one in the insertion direction

and the other in the steering direction where as the third

DOF during needle insertion is neglected.

2) Only lateral compression of the beam is possible and

not the longitudinal compression.

3) An assumption of constant spring and damper coefﬁ-

cients.

Fig. 8: Mechanism of needle insertion procedures [35]

Barb et al. [60] modeled the viscoelastic phase of the

insertion forces using KV and HC models. The viscoelastic

forces using KV model are expressed as

F=−(Kp +Bv)if p > 0

0if p ≤0

where F represents the force exerted on the tissue, vand p

are the velocity and position of the needle tip respectively,

Kand Bare stiffness and damping coefﬁcients of the model

respectively.

The viscoelastic forces using HC model are expressed as

F=−(µpn+λpnv)if p > 0

0if p ≤0

where n and λare material parameters.

With comparative tests between the HC and KV models,

they concluded that the HC model is more appropriate for

computation in visco-elastic cases. The force reconstruction

has an error and absolute mean of 0.0194 N and a standard

deviation of 0.0125 N.

According to the HC model, the total interaction force on

the needle is expressed as

Ftot =−µpn

s−λpn

s(vs−v) + Ff+Fc

where Ffis the dry frictional force and Fcis the cutting force

applied on the needle. Since the parameters λand n can’t be

same, they have identiﬁed a simple model and named it as

KV generalized model. This model has parameter K, B and

they are time varying which are dependent on position and

velocity.

Asadian et al. [44] modeled the needle insertion forces in

two phases: one, the force during insertion and the other

during retraction. They modeled these forces based on the

LuGre model wherein they assume insertion velocity to be

constant and, that these forces are dependent on the depth

of penetration of the needle. They modeled the needle-tissue

interaction forces using nonlinear dynamics based on the

LuGre model. They implemented an identiﬁcation procedure

which enables the tuning of model parameters according to the

tissue properties. Asadian [68] has further studied the effect

on tissue behavior and the possible friction models.

in Biomedical Engineering

Kikuuwe et al. [69] modeled needle insertion and with-

drawal using the Coulomb friction model. They initially

designed a 1D model and extended it to a 3D model by

considering the motion and forces acting in the axial direction.

This work is an extension of their previous work where they

studied the frictional force corresponding to the change in

the direction and depth of the needle [70]. Fukushima et al.

[53] studied the frictional force during needle insertion and

estimated the needle tip force as the difference between the

total insertion force and the frictional force.

A novel shape matching method was used for tissue model-

ing by Tian et al. [71]. This method has the advantage of

numerical stability. The needle is modeled with a ﬂexible

virtual needle, and the forces are formulated as the gradient

of potential energy. They modeled the forces for both needle

insertion and retraction.

VI. FR IC TI ON MO DE LS

The needle-tissue interaction force includes cutting force,

stiffness force, and frictional force. The frictional force is the

major component of these three forces because the other two

forces are acting as point loads whereas the frictional force is

a distributed load. It acts along the length of the needle shaft

during the entire process of needle insertion and also during

withdrawal.

Friction: Friction is the resisting force to the relative motion

between two surfaces in contact. One classiﬁcation of different

types of friction is dry friction, ﬂuid friction, internal friction

and so on. Dry friction which is a resistive force between

two solid surfaces in contact is the combination of static and

kinetic friction.

A. Static models

Classical models: Classical friction models consist of dif-

ferent basic models, most of them considering only a single

aspect of frictional force [72].

1) Coulomb Friction: The most basic friction model is the

Coulomb friction model. The main assumption of this model

is that the magnitude of friction is independent of the sliding

velocity and contact area. According to this, friction has two

regimes: static, and kinetic. Static friction at zero velocity

is when the force can take any of a range of values and,

it opposes the impending motion. Kinetic friction occurs for

nonzero velocities, and it opposes the actual motion as shown

in Fig. 9 [73]. It is constant in magnitude and equal to the

normal force multiplied by a constant coefﬁcient times the

sign of the velocity. It is described as

F=Fcsgn(v)

where Fcis frictional force which is proportional to normal

load (FN) i.e., Fc=µFNand µis the proportionality

constant.

Coulomb’s model accurately portrays the kinetic friction

but, the transition from static to kinetic friction is unrealistic

as the discontinuity at zero displacement implies an inﬁnite

rate of change of force that is not physically possible.

The major drawback of the Coulomb friction model is the

uncertainity at zero velocity [74].

Fig. 9: Coulomb friction [73]

2) Viscous Friction: Viscous friction is one of the classical

models which has viscous frictional force as proportional to

the sliding velocity [75] as shown in Fig. 10:

when

v(t)6=0:Ff(t) = −Fvv(t)

Fig. 10: Viscous friction [73]

3) Static Friction: Static friction represents the frictional

force at rest or zero sliding velocity. Lee et al. [76] studied

the magnitude of frictional force at different velocities and

observed that frictional force at rest is higher than coulomb

frictional force at nonzero velocity, as shown in Fig. 11. Static

friction has a threshold force value above which the object

starts moving as shown in Fig. 12. The friction force at rest

is a function of external forces but, not the sliding velocity of

the object. This can be modeled as

When v(t)=0:

Ff(t) = Fe|Fe|< Fs

Fssgn(Fe)|Fe|> Fs

where Fsis the static frictional force, Feis the external force.

4) Stribeck Effect: Stribeck noticed a negatively-sloped and

nonlinear friction-velocity characteristic at low velocities for

more lubricated and some dry contacts. This negatively-sloped

curve gives an important contribution to stick and slip. Because

of dynamic friction effects, instantaneous friction is not only

a function of instantaneous velocity but also a function of the

history of the velocity [77]. The friction at steady state velocity

gives the Stribeck curve, as shown in Fig.13.

in Biomedical Engineering

Fig. 11: Static and kinetic friction [76]

Fig. 12: Static friction with the combination of viscous and

coulomb friction [73]

Ff(t) = 





F(v)if v 6= 0

Feif v = 0 and |Fe|< Fs

Fssgn(Fe)otherwise

A common form of non-linearity is

F(v) = Fc+ (Fs−Fc)e−|v

Vσ||δσ+Fvv

Vσis called the Stribeck velocity.

Fig. 13: Stribeck curve characteristics [76]

The main drawback of the Coulomb, Viscous, and Stribeck

friction models is the uncertainty of the friction force at zero

velocity [78].

5) Karnopp model (derived from coulomb’s model): In this

model, Karnopp [79] has rectiﬁed the frictional force uncer-

tainty by considering a pseudo-velocity p(t) for an interval of

±Dvat the neighborhood of zero velocity where the velocity is

assumed to be zero. The pseudo-velocity p(t) is not explicitly a

velocity parameter but, considered as momentum by Karnopp.

The rate of change of p(t) is the difference between the

modeled frictional force and the force acting on the system.

˙p(t)=[Fa(t)−Fm(t)]

where Fais described as the force applied to the system, Fm

is described as modeled frictional force.

The velocity parameter of the model is formulated as:

v(t) = 0|p(t))|< Dv

Mp(t)|p(t))| ≥ Dv

where Mis described as the mass of the entire system.

The friction law is given as:

Fm(v(t), Fa(t)) =

−sgn[fa(t)]max[|Fa(t)|,(Fc+Fs)] |v(t)|< Dv

−sgn[v(t)]Fc+Fvv(t)|v(t)| ≥ Dv

where Fc,Fs, and Fvare described as Coulomb, static, and

viscous frictional force respectively.

Romano and Garcia [80] applied Karnopp model for the

control of the valve. The main drawbacks of Karnopp model

are consideration of pseudo velocity which is not applicable

for system-level studies and its applications, it is not appro-

priate for real-time systems and, the external force given to

the system is not always deﬁnite.

B. Dynamic friction

1) Pre-sliding Displacement (ﬁrst studied by Dahl): It is the

displacement of rolling or sliding contacts prior to true sliding.

It arises due to the plastic and/or elastic deformation of the

contacting asperities. It is the initial 1–50 µm displacement.

Ff(x) = −Ktx

where Ff(x)is described as the frictional force in terms

of pre-sliding displacement, Ktis a constant, the stiffness

coefﬁcient of the material and xis displacement.

2) Rising Static Friction and dwell time: At breakaway, the

magnitude of static friction, λb, is not constant. When there is

no motion, the static friction builds with time from a reduced

value to its ultimate steady state value which is also known

as stiction force, as shown in Fig. 11 (It depends on the time

spent at zero velocity).

The Armstrong model which follows the above phenomenon

is:

Fs,pn(γ, td) = FS,pn−1+ (Fs,∞−FS,pn−1

(td+γ))

where Fs,pnis the magnitude of static friction at the break-

away i.e., the start of nth interval of slip, FS,p(n−1) is the

magnitude of static friction at the start of (n−1)th interval

of slip, Fs,∞is the maximum static friction possible, γis an

empirical parameter, static friction rise time.

in Biomedical Engineering

3) Frictional Memory: It is the time delay observed be-

tween changes in frictional force to that of changes in the ve-

locity or the normal load [81], [82]. Rabinowicz [83] proposed

the time delay of frictional response as memory dependent

with the consideration of the hysteresis loop generated during

the increasing and decreasing velocities, as shown in Fig. 14.

Instantaneous friction is a function of sliding velocity and

load. It is modeled as a time lag:

Ff(t) = Fvel (v(t−δt))

where Ff(t)is the frictional force at an instant, Fvel(·)is

friction in terms of steady state velocity and δt is the delay

term.

Fig. 14: Hysteresis in the frictional force for relative velocity

[84]

4) Seven parameter model: This is the ﬁrst model which

includes the seven different basic friction elements, viz.

coulomb, viscous, static, stribeck, dwell time, pre-sliding

displacement, and frictional memory [66]. The pre-sliding

displacement element is modeled using a separate equation,

thereby having two equations, one for the stiction phenomenon

where velocity is zero and, the other for the sliding phe-

nomenon where velocity is nonzero. The transition between

these two phenomena is governed by a certain mechanism.

The frictional phenomenon is modeled as:

when sticking,

F(x) = σ0x

when sliding,

F(v, t)=(Fc+Fs(γ , td)1

(1+(v(t−τe)

vs)2)sgn(v) + Fvv

where

Fs(γ, td) = FS,a + (Fs,∞−FS,a

(td+γ))

FS,a is the Stribeck friction at an instant ’a’.

tdis dwell time, which is the time spent at a certain stage

of the process.

The main drawback of this model is that the switching

mechanism between the stiction phase to the sliding phase

is not speciﬁed. In this model, the Stribeck friction parameter

is considered with a velocity after a certain time, γ.

5) Dahl Model: The main advantage of this model over

other dynamic models is the ability to perform control system

simulations. Dahl proposed an analogy between frictional

behavior and the stress-strain property.

According to Dahl [85], objects subjected to small and

large displacements are compared to the elastic and plastic

deformation respectively; maximum stress in a stress-strain

curve is compared to stiction force. The stress-strain curve for

the ductile material is compared to coulomb friction.

With his analogy, the stress-strain curve is modeled using a

differential equation as

dx =σ(1 −F

Fcsgn(v))α

where Fis the frictional force, Fcis the coulomb force, xis

the displacement, σis the stiffness constant and αdetermines

the slope of the stress-strain curve.

This model is an extension of the Coulomb friction model,

where the frictional force is not only a function of displace-

ment but also a function of velocity [86]. The above Dahl

model is modiﬁed as

dt =dF

dx v=σ(1 −F

Fcsgn(v))αv

Bliman [87] studied the model of Dahl mathematically and

proved the link between the Dahl model and Coulomb’s model.

He also coupled the equation of motion with Dahl’s model.

Dahl’s model captures the pre-displacement and hysteresis

phenomena in a dynamic model but, fails to simulate the

Stribeck effect, stiction, and the stick-slip phenomenon.

6) LuGre Model: The LuGre model is a collaborative

work of Lund and Grenoble which is an extension of the

Dahl model [88]. It overcomes one of the disadvantages

of the Dahl model by simulating the Stribeck effect. This

model is an integrated dynamic friction model which is not

possible in the seven-parameter model. It is a six-parameter

model which includes the static model elements like coulomb,

static, viscous, stribeck friction, frictional memory, and pre-

sliding displacement [89]. In the LuGre model friction induces

hysteresis in the spring damper system. The instantaneous

friction Ffis modeled as:

Ff(t) = σ0(t) + σ1(dz(t)/dt) + Fvv(t)

where σ0is a characteristic stiffness for spring like behavior

for small displacements, and σ1is a damping parameter.

The variable z(t) represents the average deﬂection between

two surfaces at an instant twhich is the friction state. The

formulation parameters gets updated according to:

dz(t))/dt =v(t)−σ0/g(v(t))z(t)|v(t)|

where

g(v(t)) = Fc+Fse(−(v

vs)2

Fv,Fsand Fcare static, Stribeck and Coulomb friction

respectively. The steady state friction is as follows:

Fss(t) = g(v(t)) + Fvv(t)

The main advantage of this model is that its formulation

can be valid for both rotational and linear coordinates.

in Biomedical Engineering

Piatkowski [90] studied the effect of the Dahl and LuGre

models on pre-sliding displacement. He also studied the effect

of velocity on the frictional hysteresis loop. Ashwini [38]

studied the impact of the LuGre model on friction-induced

hysteresis in the system. Her experimental set-up is modeled

based on the Dahl, LuGre, and Maxwell slip models.

Fukushima et al. [53] studied the characteristics of needle

tip forces during the needle insertion and designed frictional

force estimation method. They estimated the needle tip force

by measuring the frictional force during needle insertion. They

evaluated the needle tip force as the difference between total

insertion force and the frictional force. They modeled the total

insertion force and also the frictional force for a single layer

and validated with the experimental results.

VII. EXP ER IM EN TATION OF NEEDLE S IM UL ATOR S

Hing and Brooks [17] conducted an experiment to get a

quantitative measure of the needle forces in 6 DOF. The pa-

rameters such as duration of needle insertion, various internal

events, and the start of kinetic friction during the withdrawal

of the needle were extracted from the two c-arm ﬂuoroscopy

set-up. They followed a novel approach for measuring the 3D

movement of the beads being placed in brachytherapy in real

time. They placed forty 1-mm diameter stainless steel beads

which easily show up in X-ray imaging. The ﬂuoroscope set-

up would help to track the ﬁducial markers and the needle

during insertion.

Barb et al. [60] designed an instrumented needle as shown in

Fig. 15 with a force sensor of 1 DOF along with the sensible

phantom. They used these quantitative measures to validate

their models described in the earlier sections.

Fig. 15: Instrumented Needle along with with a 1-DOF force

sensor [60]

Vidal et al. [91] developed a real-time and robust needle

simulator with two Phantom Omni. A hybrid volume haptic

rendered ultrasound transducer and a volume haptic model

were developed for needle puncture. Force measurements were

made on real tissues, and these forces are used for modeling.

Along with the force data, they used data from CT scan of a

particular patient for a patient speciﬁc model.

Lehmann et al. [37] conducted an experiment of a needle

insertion procedures using a robotic system developed by

them as shown in Fig. 16. It is designed with 2 DOF, the

translational motion is along the needle axis and, rotational

motion is about the needle axis. This system also helps in

conducting a manual insertion procedure. The main advantage

of their system is its high precision and low friction. Force

sensors in 6 DOF (3 DOF forces and 3 DOF torques) are used

to measure the insertion forces during needle insertion in the

tissue samples. They performed various experiments for man-

ual and automated needle insertion at different velocities for

two homogeneous tissue samples. They studied the effect of

the rotation of a needle by 1800by performing an experiment

with the help of a virtual sensor.

Fig. 16: Instrumental needle setup for both automated and

manual insertion [37]

Wei et al. [92] experimented on the needle insertion forces

for different depths as shown in Fig. 17a and, they also

studied the effect of insertion depth on needle deﬂection as

shown in Fig. 17b. Their experimental set-up includes a needle

which is mounted on a digital force–measuring gauge that

measures force and torques in 1 DOF. They also used an X-

ray ﬂuoroscopic system for observing the mounting conditions

in the system. Their study concluded that the difference in the

insertion force in two samples is due to internal structures such

as blood vessels. Their study also concluded that the needle

gets deﬂected to less than 10after a certain depth is attained.

(a) Insertion force (b) Needle deﬂection

Fig. 17: Experimental results for porcine samples [92]

Daykin and Bacon [93] designed and constructed an epidu-

ral simulator for teaching the skills to trainees. They also

incorporated the loss of resistance to injection in the simulator

which is a major challenge for the novice. Resistance to

needle movement is simulated with two spring loaded friction

plates. The major disadvantage of this simulator is that it can

operate in only 1 DOF. Tran et al. [94] studied the injection

in Biomedical Engineering

ﬂow rate for different layers during needle insertion, such as

muscle, interspinous ligament, and ligamentum ﬂavum. Shin

et al. [95] developed a 2 DOF needle simulator with high-

quality haptic feedback with no power loss. Dang et al. [96]

used a 3 DOF force feedback device, Phantom, to develop

an epidural simulator. They incorporated an additional DOF

by simulating the loss of resistance in the simulator. Bibin et

al. [97] developed a regional anesthesia simulator in a virtual

environment with realistic 3D anatomical models. Grottke et

al. [98] developed a virtual reality–based regional anesthesia

training simulator with the consideration of advantages such

as improper feedback from the tissue layers, inter-patient

variability, and repeated use over manikin-based simulators.

Vaughan et al. [99] have reviewed the available epidural

and lumbar simulators and also discussed the strengths and

weaknesses of these simulators. Many other researchers have

performed various experiments on needle insertion procedures

such as the study of needle diameter [49], [100]; insertion

depth; insertion velocity [50]; insertion forces; and needle

deﬂection [35]. Some of the experiments were performed to

validate the models and to measure the tissue stiffness [101].

VIII. PSY CH OP HY SI CS

As a human is involved in the process of manual needle

insertion, the forces she encounters during the insertion are

the physical stimulus and the sensation of these forces which

she is experiencing need to be measured because she is

controlling the forces. As there is no literature on needle

insertion including on humans as a part of the system, in

this section we brieﬂy introduce psychophysics and explain

how psychophysics is useful in needle insertion, experiments,

modeling, and simulation for training.

The study of the relationship between the stimulus (a

physical phenomenon) and sensation (an attribute of the

sensory system) is psychophysics. Therefore, the problems

of psychophysics represent the basic problems of modern

psychology [102]. All the human senses (sight [103], [104];

hearing [105], [106]; taste [107], [108]; smell [109], [110];

touch [111]–[115]; and also sense of time) have been studied

in the correspondence of psychophysical parameters. The

experiments that are implemented using psychophysics relate

to the two parameters, the sensation, and physical stimulus,

wherein the subject would experience the sensation based

on the stimulus speciﬁcations provided for that experiment.

Lawrence et al. [116] studied the effect of friction that is

inherent in haptic interfaces on human perception. Despite

the study of the sensory system, there are three other main

areas of study: absolute threshold, discrimination thresholds,

and scaling, which result in the detection, discrimination, and

scaling of the stimuli respectively [117].

One of the applications of this psychophysical study is to

quantify the clinical skills. The clinical skills invariably re-

quire hand-eye coordination, which involves the human haptic

system (both human sensory system and motor system). The

psychophysical study of both the sensory and motor systems

can provide us with the limitations on sensing and controlling

the forces during needle insertion and thereby designing a

better training system. In this section, we cite some of the

related psychophysical experiments in medical simulators that

may be indirectly used to quantify needle insertion skills.

Neil and Sarah [113] did a comparative study on identiﬁ-

cation of stiffness by veterinarians and novices. They con-

sidered a virtually rendered stiffness of ﬁve levels in the

range of 0.2–0.5 N/mm. Their results prove that veterinarians

performed appreciably (100%) better than novices. They also

suggest that quantifying the skills of clinicians would help

in improving the training methods for the novices. Therefore,

similar psychophysical experiments can be performed to exam-

ine the performance level of novices after the training. Nisky

and Pressman [118] studied the effect of delay in telesurgery

where stiffness needs to be controlled. They explored the

interaction between the device (Phantom Premium 1.5) and

generated a force ﬁeld that approaches the properties of real

tissues. They performed the experiment in four phases: null

training, base training, task training, and a test phase where

the task of the experiment is to differentiate the stiffness of the

force ﬁeld. Nisky et al. have also explored similar experiments

and studied the perception and action on teleoperated needle

insertion procedures [119]. Brelstaff et al. [120] have demon-

strated psychophysical experiments for both visual and haptic

effects. Their experiment performed on the virtual bone with

the incorporation of the elastic and erosion properties of real

bone addressed both surface probing and interior drilling tasks.

Their study was to explore the skills which were trained using

their bone burring simulator. Gerovich et al. [121] studied the

effect of visual and haptic feedback on human performance

by conducting an experiment using a 1 DOF needle simulator

and concluded that force feedback might not be necessary

until the visual feedback is limited. Klymenko and Pizer [103]

explored psychophysical experiments on the subjects’ perfor-

mance evaluation, particularly to the observers’ sensitivity to

visual parameters. The experiment was to detect the sensitivity

to medical imaging parameters (visual threshold).

Since it is uncommon in medical simulators to have sen-

sors connected to the subject to quantitatively measure all

the required thresholds and differential thresholds for each

parameter, it is a good practice to perform psychophysical

experiments that improve the level of training.

In the case of the needle simulators, the psychophysical

experiments help to study various parameters, and some of the

psychophysical parameters in literature relevant to the needle

insertion procedures are:

1) The threshold forces applied by the doctors at different

layers of the insertion procedure,

•Typical range of force for single ﬁnger is 1 N to 10

N, maximum controllable range is up to 100 N, Control

resolution 0.05 to 0.5 N, range of force during grasping

is 50 N to 100 N [122].

•Contact force resolution is 5-15 % of the reference value

[123].

•Force detection threshold between index, middle, pinkie

and multi-ﬁnger interaction are 33.5, 32.1, 33.5, 28.9 mN

respectively but less sensitivity with the ring ﬁnger (mean

threshold of 43.6 mN) [124]

in Biomedical Engineering

•JND for pinching motions between ﬁnger and thumb is

5-10 % with a constant resisting force of 2.5 N to 10 N

[125].

•Force JND for left and right index ﬁnger is approximately

10 % of the reference value [126].

•Bandwidth for sensory system is 20 to 30 Hz [127].

•Bandwidth for limb motions depends on the mode of

operation [127]:

–Bandwidth for periodic signals 2-5 Hz.

–Bandwidth for internally generated or known trajec-

tories is up to 5 Hz.

–Bandwidth for unexpected signals is 1-2 Hz. and

–Bandwidth for reﬂex actions is about 10 Hz.

2) Tissue stiffness at each layer,

3) Just noticeable difference (JND) in terms of the orienta-

tion of the needle during insertion, and

•The JND of human sensitivity to rotation or in orientation

sensing is [122]

1) Resolution for ﬁnger joints is 2.50

2) Resolution for the wrist and elbow joints is 20, and

3) Resolution for the shoulder joint is about 0.80.

4) Threshold velocity with which the needle is being in-

serted into the tissues.

•Velocity JND or resolution at ﬁngertip and wrist are 0.1

m/s and 1 m/s respectively [122].

This psychophysical parameter analysis would help in im-

proving the design of simulators and simplify the models

[128]. The parameters mentioned above such as human force

resolutions, force thresholds, bandwidth, and velocity can be

used to design a model whereas the Just Noticeable Difference

(JND) of joint rotations and range of motion can be used to

develop the simulator. Thus, to improve human performance,

haptic devices have to be designed by considering quantitative

human studies [129] and training methods and help the trainees

with a better experience. Jones and Tan [130] reviewed the

applications of psychophysical parameters on haptics.

IX. SU MM ARY

In this paper, we have described state-of-the-art needle

insertion modeling in soft tissues. Modeling of needle insertion

is classiﬁed into needle insertion forces, tissue deformation,

and needle-tissue interaction and each of the models was

discussed in great detail. In particular, the classical friction

models which are neglected in the literature were also em-

phasized. Several experimental techniques or simulators for

needle insertion procedures were described, psychophysical

analysis of the human sensations during the operation of a

needle insertion simulator which is the future of research in

needle insertion procedures were also discussed in detail.

The study of different models found that the total interaction

forces were the combination of cutting force, friction force,

and sliding force. The consideration of forward and backward

kinematics made them enable both needle path planning and

path correction. It is also found that most of the models

assumed constant velocity during the needle insertion pro-

cedures. Frictional models (both classical or static models

and dynamic frictional models) which were neglected in the

literature were also discussed. It is well known that human

tissues have nonlinear and viscoelastic properties. Therefore,

classical or static models are not accurate in incorporating

all the mechanical properties of biological tissues. In the

literature, only a few dynamic models are used for modeling

needle procedures in human tissues, like the LuGre model. A

few experiments in the literature to study the effect of delay

in perception and action in teleoperated needle procedures and

teleoperated surgeries have found that delay causes inaccurate

estimation of tissue stiffness. It is shown that these psy-

chophysical experiments are necessary to enhance the training

methods by improving the accuracy of the models and thereby

the skills of the trainee.

REF ER EN CE S

[1] R. Alterovitz, A. Lim, K. Goldberg, G. S. Chirikjian, and A. M.

Okamura, “Steering ﬂexible needles under Markov motion uncertainty,”

2005 IEEE/RSJ International Conference on Intelligent Robots and

Systems, IROS, pp. 120–125, 2005.

[2] J. B. Ra, S. M. Kwon, J. K. Kim, J. Yi, K. H. Kim, H. W. Park,

K. U. Kyung, D. S. Kwon, H. S. Kang, S. T. Kwon, L. Jiang, J. Zeng,

K. Cleary, and S. K. Mun, “Spine needle biopsy simulator using visual

and force feedback,” Computer Aided Surgery, vol. 7, no. 6, pp. 353–

363, 2002.

[3] E. H. Smith, “Complications of percutaneous abdominal ﬁne-needle

biopsy. review.,” Radiology, vol. 178, no. 1, pp. 253–258, 1991.

[4] S. Ullrich, O. Grottke, R. Rossaint, M. Staat, T. M. Deserno, and

T. Kuhlen, “Virtual needle simulation with haptics for regional anaes-

thesia,” Proc. of the IEEE Virtual Reality 2010, Workshop on Medical

Virtual Environments, Waltham, MA, USA, March 21, 2010, pp. 1–3,

2010.

[5] Y. Auroy, D. Benhamou, L. Bargues, C. Ecoffey, B. Falissard,

F. Mercier, H. Bouaziz, and K. Samii, “Major Complications of

Regional Anesthesia in France The SOS Regional Anesthesia Hotline

Service,” Anesthesiology, vol. 97, no. 5, pp. 1274–80, 2002.

[6] C. Konrad, G. Sch¨

upfer, M. Wietlisbach, and H. Gerber, “Learning

manual skills in anesthesiology: Is there a recommended number of

cases for anesthetic procedures?,” Anesthesia and analgesia, vol. 86,

no. 3, pp. 635–639, 1998.

[7] C. Hellekant, “Vascular complications following needle puncture of

the liver. Clinical angiography.,” Acta radiologica: diagnosis, vol. 17,

pp. 209–22, mar 1976.

[8] C. Basdogan, S. De, J. Kim, M. Manivannan, H. Kim, and M. A.

Srinivasan, “Haptics in minimally invasive surgical simulation and

training,” IEEE computer graphics and applications, vol. 24, no. 2,

pp. 56–64, 2004.

[9] M. S. Raghu Prasad, M. Manivannan, and S. M. Chandramohan, “Ef-

fects of laparoscopic instrument and ﬁnger on force perception: a ﬁrst

step towards laparoscopic force-skills training,” Surgical Endoscopy

and Other Interventional Techniques, pp. 1927–1943, 2014.

[10] C. Cope, “Needle endoscopy in special procedures.,” Radiology,

vol. 168, pp. 353–8, aug 1988.

[11] M. Marchal, E. Promayon, and J. Troccaz, “Comparisons of needle

insertion in brachytherapy protocols using a soft tissue discrete model,”

in Surgetica 2007, pp. pp–153, Sauramps Medical, 2007.

[12] S. J. Damore, A. N. Syed, A. A. Puthawala, and A. Sharma, “Needle

displacement during hdr brachytherapy in the treatment of prostate can-

cer,” International Journal of Radiation Oncology* Biology* Physics,

vol. 46, no. 5, pp. 1205–1211, 2000.

[13] P. D. Grimm, “Precision implant needle and method of using same in

seed implant treatment of prostate cancer,” Aug. 17 1999. US Patent

5,938,583.

[14] R. D. Miller, L. I. Eriksson, L. A. Fleisher, J. P. Wiener-Kronish, N. H.

Cohen, and W. L. Young, Miller’s anesthesia. Elsevier Health Sciences,

2014.

[15] C. W. Kim, K. Siemionow, D. G. Anderson, and F. M. Phillips, “The

current state of minimally invasive spine surgery,” J Bone Joint Surg

Am, vol. 93, no. 6, pp. 582–596, 2011.

[16] W. J. Dyche, J. H. Walsh, and J. A. Nelson, “An acls laboratory rotation

for undergraduate medical students,” Annals of emergency medicine,

vol. 12, no. 4, pp. 208–211, 1983.

in Biomedical Engineering

[17] J. T. Hing, a. D. Brooks, and J. P. Desai, “Reality-based needle insertion

simulation for haptic feedback in prostate brachytherapy,” Proceedings

2006 IEEE International Conference on Robotics and Automation 2006

ICRA 2006, no. May, pp. 619–624, 2006.

[18] M. Torabi, K. Hauser, R. Alterovitz, V. Duindam, and K. Gold-

berg, “Guiding medical needles using single-point tissue manipula-

tion,” Proceedings - IEEE International Conference on Robotics and

Automation, pp. 2705–2710, 2009.

[19] N. Chentanez, R. Alterovitz, D. Ritchie, L. Cho, K. K. Hauser, K. Gold-

berg, J. R. Shewchuk, and J. F. O’Brien, “Interactive simulation of

surgical needle insertion and steering,” ACM Transactions on Graphics,

vol. 28, no. 3, p. 1, 2009.

[20] N. Abolhassani, R. Patel, and M. Moallem, “Needle insertion into soft

tissue: A survey,” Medical Engineering and Physics, vol. 29, no. 4,

pp. 413–431, 2007.

[21] R. S. Haluck, W. Murraya, R. Webster, N. Mohler, and M. Melkonian,

“A haptic lumbar puncture simulator,” Proceedings of Medicine Meets

Virtual Reality (MMVR’2000), 2000.

[22] L. B. Ready, “Acute pain: Lessons learned from 25,000 patients,”

Regional Anesthesia and Pain Medicine, vol. 24, no. 6, pp. 499–505,

1999.

[23] R. Taschereau, J. Pouliot, J. Roy, and D. Tremblay, “Seed misplacement

and stabilizing needles in transperineal permanent prostate implants,”

Radiotherapy and Oncology, vol. 55, no. 1, pp. 59–63, 2000.

[24] C. Sutherland, K. Hashtrudi-Zaad, R. Sellens, P. Abolmaesumi, and

P. Mousavi, “An augmented reality haptic training simulator for spinal

needle procedures,” IEEE Transactions on Biomedical Engineering,

vol. 60, no. 11, pp. 3009–3018, 2013.

[25] M. M. Hammoud, F. S. Nuthalapaty, A. R. Goepfert, P. M. Casey,

S. Emmons, E. L. Espey, J. M. Kaczmarczyk, N. T. Katz, J. J. Neutens,

E. G. Peskin, et al., “To the point: medical education review of the role

of simulators in surgical training,” American journal of obstetrics and

gynecology, vol. 199, no. 4, pp. 338–343, 2008.

[26] D. Escobar-Castillejos, J. Noguez, L. Neri, A. Magana, and B. Benes,

“A review of simulators with haptic devices for medical training,”

Journal of medical systems, vol. 40, no. 4, pp. 1–22, 2016.

[27] P.-J. Fager, “The use of haptics in medical applications,” The

International Journal of Medical Robotics and Computer Assisted

Surgery, vol. 1, no. 1, pp. 36–42, 2004.

[28] C. Basdogan, S. De, J. Kim, M. Muniyandi, H. Kim, and M. A.

Srinivasan, “Haptics in minimally invasive surgical simulation and

training,” IEEE computer graphics and applications, vol. 24, no. 2,

pp. 56–64, 2004.

[29] T. R. Coles, D. Meglan, and N. W. John, “The role of haptics in medical

training simulators: A survey of the state of the art,” IEEE Transactions

on haptics, vol. 4, no. 1, pp. 51–66, 2011.

[30] S. Ullrich and T. Kuhlen, “Haptic palpation for medical simulation

in virtual environments,” IEEE Transactions on Visualization and

Computer Graphics, vol. 18, no. 4, pp. 617–625, 2012.

[31] S. DiMaio and S. Salcudean, “Needle insertion modeling and sim-

ulation,” IEEE Transactions on Robotics and Automation, vol. 19,

pp. 864–875, oct 2003.

[32] D. De Lorenzo, Y. Koseki, E. De Momi, K. Chinzei, and A. M.

Okamura, “Coaxial needle insertion assistant with enhanced force

feedback,” IEEE Transactions on Biomedical Engineering, vol. 60,

no. 2, pp. 379–389, 2013.

[33] O. Goksel, K. Sapchuk, and S. E. Salcudean, “Haptic simulator for

prostate brachytherapy with simulated needle and probe interaction,”

IEEE Transactions on Haptics, vol. 4, pp. 188–198, May 2011.

[34] S. P. DiMaio and S. E. Salcudean, “Interactive simulation of needle

insertion models,” IEEE Transactions on Biomedical Engineering,

vol. 52, no. 7, pp. 1167–1179, 2005.

[35] K. Yan, W. S. Ng, K. V. Ling, Y. Yu, T. Podder, T. I. Liu, and C. Cheng,

“Needle steering modeling and analysis using unconstrained modal

analysis,” Proceedings of the First IEEE/RAS-EMBS International

Conference on Biomedical Robotics and Biomechatronics, 2006,

BioRob 2006, vol. 2006, pp. 87–92, 2006.

[36] D. Glozman and M. Shoham, “Flexible needle steering for percuta-

neous therapies.,” Computer aided surgery : ofﬁcial journal of the

International Society for Computer Aided Surgery, vol. 11, no. 4,

pp. 194–201, 2006.

[37] T. Lehmann, M. Tavakoli, N. Usmani, and R. Sloboda, “Force-sensor-

based estimation of needle tip deﬂection in brachytherapy,” Journal of

Sensors, vol. 2013, no. Figure 2, 2013.

[38] A. K. Padthe, J. Oh, and D. S. Bernstein, “On the lugre model and

friction-induced hysteresis,” in Proceedings of the 2006 American

Control Conference, Minneapolis, Minnesota, USA, vol. 3247, p. 3252,

2006.

[39] C. Simone and A. Okamura, “Modeling of needle insertion forces

for robot-assisted percutaneous therapy,” Proceedings 2002 IEEE

International Conference on Robotics and Automation, vol. 2, no. May,

pp. 2085–2091, 2002.

[40] P. N. Brett, T. J. Parker, A. J. Harrison, T. A. Thomas, and

A. Carr, “Simulation of resistance forces acting on surgical needles.,”

Proceedings of the Institution of Mechanical Engineers. Part H, Journal

of engineering in medicine, vol. 211, no. 4, pp. 335–47, 1997.

[41] K. Naemura, A. Sakai, T. Hayashi, and H. Saito, “Epidural insertion

simulator of higher insertion resistance & drop rate after puncture.,”

Conference proceedings : Annual International Conference of the IEEE

Engineering in Medicine and Biology Society. IEEE Engineering in

Medicine and Biology Society. Conference, vol. 2008, pp. 3249–52,

2008.

[42] D. J. van Gerwen, J. Dankelman, and J. J. van den Dobbelsteen,

“Needle-tissue interaction forces - A survey of experimental data,”

Medical Engineering and Physics, vol. 34, no. 6, pp. 665–680, 2012.

[43] V. N. Dubey, M. Y. Wee, N. Vaughan, and R. Isaacs, Biomedical

Engineering in Epidural Anaesthesia Research. INTECH Open Access

Publisher, 2013.

[44] A. Asadian, M. R. Kermani, and R. V. Patel, “A compact dynamic

force model for needle-tissue interaction.,” Conference proceedings : ...

Annual International Conference of the IEEE Engineering in Medicine

and Biology Society. IEEE Engineering in Medicine and Biology

Society. Annual Conference, vol. 2010, pp. 2292–5, 2010.

[45] O. Goksel, E. Dehghan, and S. E. Salcudean, “Modeling and simulation

of ﬂexible needles,” Medical Engineering and Physics, vol. 31, no. 9,

pp. 1069–1078, 2009.

[46] H. Kataoka, T. Washio, M. Audette, and K. Mizuhara, “A model

for relations between needle deﬂection, force, and thickness on

needle penetration,” Lecture Notes in Computer Science (including

subseries Lecture Notes in Artiﬁcial Intelligence and Lecture Notes

in Bioinformatics), vol. 2208, pp. 966–974, 2001.

[47] M. Khadem, B. Fallahi, C. Rossa, R. S. Sloboda, N. Usmani, and

M. Tavakoli, “A Mechanics-based Model for Simulation and Control

of Flexible Needle Insertion in Soft Tissue,” pp. 2264–2269, 2015.

[48] A. M. Okamura, C. Simone, and M. D. O’Leary, “Force modeling for

needle insertion into soft tissue.,” IEEE transactions on bio-medical

engineering, vol. 51, no. 10, pp. 1707–16, 2004.

[49] S. Misra, K. B. Reed, B. W. Schafer, K. Ramesh, and A. M. Okamura,

“Mechanics of ﬂexible needles robotically steered through soft tissue,”

The International journal of robotics research, 2010.

[50] M. Mahvash and P. E. Dupont, “Mechanics of dynamic needle in-

sertion into a biological material,” IEEE Transactions on Biomedical

Engineering, vol. 57, no. 4, pp. 934–943, 2010.

[51] M. Farber, F. Hummel, C. Gerloff, and H. Handels, “Virtual Real-

ity Simulator for the Training of Lumbar Punctures,” Methods of

Information in Medicine, vol. 48, pp. 493–501, may 2009.

[52] Y.-c. Fung, Biomechanics: mechanical properties of living tissues.

Springer Science & Business Media, 2013.

[53] Y. Fukushima and K. Naemura, “Estimation of the friction force during

the needle insertion using the disturbance observer and the recursive

least square,” ROBOMECH Journal, vol. 1, p. 14, dec 2014.

[54] J. C. Teoh and T. Lee, “The effect of gender, age, bodyweight, height

and body mass index on plantar soft tissue stiffness,” Journal of Foot

and Ankle Research, vol. 7, no. 1, pp. 1–2, 2014.

[55] H. Delingette, “Toward realistic soft-tissue modeling in medical simu-

lation,” Proceedings of the IEEE, vol. 86, pp. 512–523, mar 1998.

[56] B. Querleux, Computational Biophysics of the Skin. CRC Press, 2016.

[57] O. Goksel, S. E. Salcudean, and S. P. Dimaio, “3D simulation of

needle-tissue interaction with application to prostate brachytherapy.,”

Computer aided surgery : ofﬁcial journal of the International Society

for Computer Aided Surgery, vol. 11, no. 6, pp. 279–88, 2006.

[58] D. Lepiller, M. Sermesant, M. Pop, H. Delingette, G. A. Wright,

and N. Ayache, “Medical Image Computing and Computer-Assisted

Intervention – MICCAI 2008,” Lecture Notes in Computer Science

(including subseries Lecture Notes in Artiﬁcial Intelligence and Lecture

Notes in Bioinformatics), vol. 5241, no. PART 1, pp. 678–685, 2008.

[59] J. Xu, L. Wang, K. C. L. Wong, and P. Shi, “A Meshless Framework

For Bevel-tip Flexible Needle Insertion Through Soft Tissue,” pp. 753–

758, 2010.

[60] L. Barb´

e, B. Bayle, M. de Mathelin, and A. Gangi, “Needle in-

sertions modeling: Identiﬁability and limitations,” Biomedical Signal

Processing and Control, vol. 2, pp. 191–198, jul 2007.

in Biomedical Engineering

[61] N. Diolaiti, C. Melchiorri, and S. Stramigioli, “Contact impedance es-

timation for robotic systems,” IEEE Transactions on Robotics, vol. 21,

no. 5, pp. 925–935, 2005.

[62] L. Wang, Z. Wang, and S. Hirai, “Modeling and simulation of friction

forces during needle insertion using Local Constraint Method,” IEEE

International Conference on Intelligent Robots and Systems, pp. 4926–

4932, 2012.

[63] T. C. Gasser, R. W. Ogden, and G. A. Holzapfel, “Hyperelastic

modelling of arterial layers with distributed collagen ﬁbre orientations,”

Journal of The Royal Society Interface, vol. 3, no. 6, pp. 15–35, 2006.

[64] J. A. Weiss, B. N. Maker, and S. Govindjee, “Finite element im-

plementation of incompressible, transversely isotropic hyperelasticity,”

Computer Methods in Applied Mechanics and Engineering, vol. 135,

no. 1, pp. 107 – 128, 1996.

[65] G. Limbert and J. Middleton, “A polyconvex anisotropic strain en-

ergy function. application to soft tissue mechanics,” ASME Summer

Bioengineering Conference, Vali, 2005.

[66] G. Limbert, “A mesostructurally-based anisotropic continuum model

for biological soft tissues–decoupled invariant formulation.,” Journal

of the mechanical behavior of biomedical materials, vol. 4, pp. 1637–

57, nov 2011.

[67] E. Dehghan, O. Goksel, and S. E. Salcudean, Medical Image

Computing and Computer-Assisted Intervention – MICCAI 2006: 9th

International Conference, Copenhagen, Denmark, October 1-6, 2006.

Proceedings, Part I, ch. A Comparison of Needle Bending Models,

pp. 305–312. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006.

[68] A. Asadian, R. V. Patel, and M. R. Kermani, “Dynamics of translational

friction in needle-tissue interaction during needle insertion,” Annals of

Biomedical Engineering, vol. 42, no. 1, pp. 73–85, 2014.

[69] R. Kikuuwe, Y. Kobayashi, and H. Fujimoto, “Coulomb-friction-based

needle insertion/withdrawal model and its discrete-time implementa-

tion,” in Proceedings of EuroHaptics, pp. 207–212, 2006.

[70] R. Kikuuwe, N. Takesue, A. Sano, H. Mochiyama, and H. Fujimoto,

“Fixed-step friction simulation: from classical coulomb model to mod-

ern continuous models,” in 2005 IEEE/RSJ International Conference

on Intelligent Robots and Systems, pp. 1009–1016, IEEE, 2005.

[71] Y. Tian, “Haptic Simulation of Needle-tissue Interaction Based on

Shape Matching,” 2014.

[72] P. Korondi, J. Halas, K. Samu, A. Bojtos, and P. Tamas, Robot

Applications. BME MOGI, 2014.

[73] K. Seeler, System Dynamics, ch. An Introduction for Mechanical

Engineers, pp. 0–667. Berlin, Heidelberg: Springer-Verlag New York,

2014.

[74] M. Linderoth, A. Stolt, A. Robertsson, and R. Johansson, “Robotic

force estimation using motor torques and modeling of low velocity

friction disturbances,” in 2013 IEEE/RSJ International Conference on

Intelligent Robots and Systems, pp. 3550–3556, Nov 2013.

[75] V. van Geffen, “A study of friction models and friction compensation,”

DCT, vol. 118, p. 24, 2009.

[76] C.-H. Lee and A. a. Polycarpou, “Static Friction Experiments and

Veriﬁcation of an Improved Elastic-Plastic Model Including Roughness

Effects,” Journal of Tribology, vol. 129, no. 4, p. 754, 2007.

[77] D. K. Peter, H. Janos, D. S. Krisztian, B. Attila, and D. T. Peter, Robot

Applications. BME MOGI, 2014.

[78] S. Andersson, A. S¨

oderberg, and S. Bj¨

orklund, “Friction models

for sliding dry, boundary and mixed lubricated contacts,” Tribology

International, vol. 40, pp. 580–587, apr 2007.

[79] D. Karnopp, “Computer Simulation of Stick-Slip Friction in Mechan-

ical Dynamic Systems,” Journal of Dynamic Systems, Measurement,

and Control, vol. 107, no. 1, p. 100, 1985.

[80] R. A. Romano and C. Garcia, “Karnopp Friction Model Identiﬁcation

for a Real Control Valve,” in IFAC Proceedings Volumes, vol. 41,

pp. 14906–14911, 2008.

[81] B. Armstrong-Helouvry, “Frictional memory in servo control,” in

Proceedings of 1994 American Control Conference - ACC ’94, vol. 2,

pp. 1786–1790, IEEE, 1994.

[82] H. Olsson, K. J. Astrom, C. C. De Wit, M. Gafvert, and P. Lischinsky,

“Friction models and friction compensation,” European journal of

control, vol. 4, no. 3, pp. 176—-195, 1998.

[83] E. Rabinowicz, “The Intrinsic Variables affecting the Stick-Slip Pro-

cess,” Proceedings of the Physical Society, vol. 71, no. 4, p. 668, 1958.

[84] J. Wojewoda, A. Stefa´

nski, M. Wiercigroch, and T. Kapitaniak,

“Hysteretic effects of dry friction: modelling and experimental stud-

ies,” Philosophical Transactions of the Royal Society of London A:

Mathematical, Physical and Engineering Sciences, vol. 366, no. 1866,

pp. 747–765, 2008.

[85] P. R. Dahl, “Solid Friction Damping of Mechanical Vibrations,” AIAA

Journal, vol. 14, no. 12, pp. 1675–1682, 1976.

[86] B. Armstrong-H´

elouvry, P. Dupont, and C. C. D. Wit, “A survey of

models, analysis tools and compensation methods for the control of

machines with friction,” Automatica, vol. 30, no. 7, pp. 1083 – 1138,

1994.

[87] P.-A. J.Bliman, “Mathematical study of the Dahl ’ s friction model,”

European Journal of Mechanics. A/Solids, vol. 11(6), no. June,

pp. 835–848, 1992.

[88] K. Johanastrom and C. Canudas-De-Wit, “Revisiting the lugre friction

model,” IEEE control Systems, vol. 28, no. 6, pp. 101–114, 2008.

[89] E. Berger, “Friction modeling for dynamic system simulation,” Applied

Mechanics Reviews, vol. 55, no. 6, p. 535, 2002.

[90] T. Piatkowski, “Dahl and LuGre dynamic friction models: The analysis

of selected properties,” Mechanism and Machine Theory, vol. 73,

pp. 91–100, 2014.

[91] F. P. Vidal, N. W. John, A. E. Healey, and D. A. Gould, “Simulation of

ultrasound guided needle puncture using patient speciﬁc data with 3D

textures and volume haptics,” Computer Animation and Virtual Worlds,

vol. 19, no. 2, pp. 111–127, 2008.

[92] Ka Wei Ng, Jin Quan Goh, Soo Leong Foo, Poh Hua Ting and T. K.

Lee, “Needle Insertion Forces Studies for Optimal Surgical Modeling,”

Ijbbb, vol. 3, no. 6, pp. 187–191, 2013.

[93] R. J. Bacon, “An epidural injection simulator,” vol. 45, no. June 1989,

pp. 235–236, 1990.

[94] D. Tran, K.-W. Hor, A. A. Kamani, V. A. Lessoway, and R. N. Rohling,

“Instrumentation of the loss-of-resistance technique for epidural needle

insertion,” IEEE Transactions on Biomedical Engineering, vol. 56,

no. 3, pp. 820–827, 2009.

[95] S. Shin, W. Park, H. Cho, S. Park, and L. Kim, “Needle Insertion Simu-

lator with Haptic Feedback,” Human-Computer Interaction: Interaction

Techniques and Environments, Pt Ii, vol. 6762, pp. 119–124, 2011.

[96] T. Dang, T. M. Annaswamy, and M. A. Srinivasan, “Development

and evaluation of an epidural injection simulator with force feedback

for medical training,” Studies in Health Technology and Informatics,

pp. 97–102, 2001.

[97] L. Bibin, L. Anatole, M. Bonnet, A. Delbos, and C. Dillon, “SAILOR:

a 3-D medical simulator of loco-regional anaesthesia based on desktop

virtual reality and pseudo-haptic feedback,” ACM Sysmposium on

Virtual Reality Software and Technology (VRST), pp. 97–100, 2008.

[98] O. Grottke, A. Ntouba, S. Ullrich, W. Liao, E. Fried, A. Prescher, T. M.

Deserno, T. Kuhlen, and R. Rossaint, “Virtual reality-based simulator

for training in regional anaesthesia,” British Journal of Anaesthesia,

vol. 103, no. 4, pp. 594–600, 2009.

[99] N. Vaughan, V. N. Dubey, M. Y. Wee, and R. Isaacs, “A review of

epidural simulators: Where are we today?,” Medical engineering &

physics, vol. 35, no. 9, pp. 1235–1250, 2013.

[100] X. He, Y. Chen, and L. Tang, “Haptic simulation of ﬂexible nee-

dle insertion,” 2007 IEEE International Conference on Robotics and

Biomimetics, ROBIO, pp. 607–611, 2008.

[101] R. J. Roesthuis, Y. R. Van Veen, A. Jahya, and S. Misra, “Mechanics of

needle-tissue interaction,” in 2011 IEEE/RSJ international conference

on intelligent robots and systems, pp. 2557–2563, IEEE, 2011.

[102] G. A. Gescheider, Psychophysics: the fundamentals. Psychology Press,

2013.

[103] V. Klymenko, S. M. Pizer, and R. E. Johnston, “Visual Psychophysics

and Medical Imaging: Nonparametric Adaptive Method for Rapid

Threshold Estimation in Sensitivity Experiments,” IEEE Transactions

on Medical Imaging, vol. 9, no. 4, pp. 353–365, 1990.

[104] B. Wu, R. L. Klatzky, D. Shelton, and G. D. Stetten, “Psychophysical

evaluation of in-situ ultrasound visualization,” IEEE Transactions on

Visualization and Computer Graphics, vol. 11, no. 6, pp. 684–693,

2005.

[105] I. J. Hirsh and C. S. Watson, “Auditory psychophysics and perception.,”

Annual review of psychology, vol. 47, pp. 461–84, 1996.

[106] R. S. Bernstein and J. S. Gravel, “A method for determining hearing

sensitivity in infants: The interweaving staircase procedure (isp),”

Journal of the American Academy of Audiology, vol. 1, no. 3, pp. 138–

145, 1990.

[107] J. Huber, “The psychophysics of taste: Perceptions of bitterness and

sweetness in iced tea,” NA-Advances in Consumer Research Volume

01, 1974.

[108] L. M. Bartoshuk, “The psychophysics of taste.,” The American Journal

of Clinical Nutrition, vol. 31, no. 6, pp. 1068–1077, 1978.

[109] B. Johnson, R. M. Khan, and N. Sobel, “Human Olfactory Psy-

chophysics,” The Senses: A Comprehensive Reference, vol. 4, pp. 823–

857, 2010.

in Biomedical Engineering

[110] R. Henkin, “Evaluation and treatment of human olfactory dysfunction,”

Otolaryngology, vol. 2, pp. 1–86, 1993.

[111] S. J. Biggs and M. A. Srinivasan, “Haptic interfaces,” Handbook of

virtual Environments, pp. 93–116, 2002.

[112] S. McKnight, N. Melder, A. L. Barrow, W. S. Harwin, and J. P. Wann,

“Psychophysical Size Discrimination using Multi-ﬁngered Haptic Inter-

faces,” Proceedings of 4th International Conference Eurohaptics 2004,

pp. 274–281, 2004.

[113] N. Forrest, S. Baillie, and H. Z. Tan, “Haptic stiffness identiﬁcation

by veterinarians and novices: a comparison,” Proceedings - 3rd Joint

EuroHaptics Conference and Symposium on Haptic Interfaces for

Virtual Environment and Teleoperator Systems, World Haptics 2009,

pp. 646–651, 2009.

[114] G. Donlin, J. Dosher, and B. Hannaford, “Psychophysics of Multiﬁnger

Touch and Haptic Interfaces,” p. 713028, 2008.

[115] L. A. Baumgart, G. J. Gerling, and E. J. Bass, “Psychophysical detec-

tion of inclusions with the bare ﬁnger amidst softness differentials,”

2010 IEEE Haptics Symposium, HAPTICS 2010, pp. 17–20, 2010.

[116] D. A. Lawrence, L. Y. Pao, A. M. Dougherty, Y. Pavlou, S. W. Brown,

and S. A. Wallace, “Human perception of friction in haptic interfaces,”

in Proc. ASME Dynamic Systems and Control Division, pp. 287–294,

1998.

[117] A. K. Frederick and P. Nicolaas, Psychophysics: A Practical

Introduction. Elsevier, 2010.

[118] I. Nisky, A. Pressman, C. M. Pugh, F. A. Mussa-Ivaldi, and A. Karniel,

“Perception and action in simulated telesurgery,” Lecture Notes in

Computer Science (including subseries Lecture Notes in Artiﬁcial

Intelligence and Lecture Notes in Bioinformatics), vol. 6191 LNCS,

no. PART 1, pp. 213–218, 2010.

[119] I. Nisky, A. Pressman, C. M. Pugh, F. A. Mussa-Ivaldi, and

A. Karniel, “Perception and action in teleoperated needle insertion,”

IEEE Transactions on Haptics, vol. 4, no. 3, pp. 155–166, 2011.

[120] G. Brelstaff, M. Agus, A. Giachetti, E. Gobbetti, G. Zanetti, A. Zor-

colo, B. Picasso, and S. S. Franceschini, “Towards a psychophysical

evaluation of a surgical simulator for bone-burring,” Proceedings of the

2nd symposium on Appied perception in graphics and visualization -

APGV ’05, p. 139, 2005.

[121] O. Gerovich, P. Marayong, and A. M. Okamura, “The effect of visual

and haptic feedback on computer-assisted needle insertion.,” Computer

aided surgery : ofﬁcial journal of the International Society for Computer

Aided Surgery, vol. 9, no. 6, pp. 243–249, 2004.

[122] H. Z. Tan, M. a. Srinivasan, B. Eberman, and B. Cheng, “Human factors

for the design of force-reﬂecting haptic interfaces,” ASME Dynamic

Systems and Control (DSC), vol. 55, no. 1, pp. 353–359, 1994.

[123] L. A. Jones, “Matching forces: constant errors and differential thresh-

olds,” Perception, vol. 18, no. 5, pp. 681–687, 1989.

[124] H. H. King, R. Donlin, and B. Hannaford, “Perceptual thresholds

for single vs. multi-ﬁnger haptic interaction,” 2010 IEEE Haptics

Symposium, HAPTICS 2010, pp. 95–99, 2010.

[125] X.-D. Pang, H. Z. Tan, and N. I. Durlach, “Manual discrimination of

force using active ﬁnger motion,” Perception & psychophysics, vol. 49,

no. 6, pp. 531–540, 1991.

[126] M. R. Prasad, S. Purswani, and M. Manivannan, “Force jnd for

right index ﬁnger using contra lateral force matching paradigm,” in

ICoRD’13, pp. 365–375, Springer, 2013.

[127] T. L. Brooks, “Telerobotic response requirements,” in Systems, Man

and Cybernetics, 1990. Conference Proceedings., IEEE International

Conference on, pp. 113–120, IEEE, 1990.

[128] L. Batteau, A. Liu, and J. Maintz, “A study on the perception of haptics

in surgical simulation,” Medical Simulation, vol. 3078, pp. 185–192,

2004.

[129] M. H. Zadeh, D. Wang, and E. Kubica, “Human Factors for Designing

a Haptic Interface for Interaction with a Virtual Environments,” IEEE

International Workshop on Haptic Audio Visual Environments and their

Applications, no. October, pp. 12–14, 2007.

[130] L. A. Jones and H. Z. Tan, “Application of psychophysical techniques

to haptic research,” IEEE Transactions on Haptics, vol. 6, no. 3,

pp. 268–284, 2013.

Gourishetti Ravali received the BTech degree in

electronics and instrumentation from Kakatiya Uni-

versity, Warangal and the MTech degree in biomedi-

cal engineering from MNNIT Allahabad, Allahabad

in 2012 and 2014 respectively. She joined Haptics

Lab, IIT Madras, as a research scholar in 2014.

Muniyandi Manivannan received the ME and PhD

degrees from the Indian Institute of Science, Banga-

lore. He received post-doctoral training in Haptics at

MIT, Cambridge. Before MIT, he received another

post-doctoral training in CAD standards and sensors

network at the National Institute of Standards and

Technology, Maryland. He served as a chief software

architect of Yantric Inc. before joining IIT Madras.

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Nov 2023

Needle insertion into soft biological tissues has been of interest to researchers in the recent decade due to its minimal invasiveness in diagnostic and therapeutic medical procedures. This paper presents a review of the finite-element (FE) modelling of the interaction of needle/microneedles with soft biological tissues or tissue phantoms. The reviewed models laid a solid foundation for developing more efficient novel medical technologies. This paper encompasses FE models for both invasive and non-invasive needle-tissue interactions. The former focuses on tissue and needle deformation without employing any damage mechanism, whereas the latter incorporates algorithms that enable crack propagation with a damage mechanism. Invasive FE models are presented in five categories, namely nodal separation, element failure/deletion, cohesive zone (CZ), arbitrary Lagrangian–Eulerian (ALE), and coupled Eulerian–Lagrangian (CEL) methods. In each section, the most important aspects of modelling, challenges, and novel techniques are presented. Furthermore, the application of FE modelling in real-time haptic devices and a survey on some of the most important studies in this area are presented. At the end of the paper, the importance and strength of the reviewed studies are discussed and the remaining limitations for future studies are highlighted.

A Data Driven Recurrent Neural Network Approach for Reproduction of Variable Visuo-Haptic Force Feedback in Surgical Tool Insertion

Article

Oct 2023
EXPERT SYST APPL

Analysis of Robotically Controlled Percutaneous Needle Insertion into Ex Vivo Kidney Tissue for Minimally Invasive Percutaneous Nephrolithotomy (PCNL) Surgery

Chapter

Aug 2023

Percutaneous nephrolithotomy or PCNL is a standard renal microsurgery method in modern urology. However, this is a challenging discipline for urologists throughout the surgical procedure. They should accomplish a manner with limited sensible and flexibility of invasive apparatuses. Presently in modern medical science especially in surgery, minimally invasive procedures achieved huge popularity where needle insertion plays an important part. In this experimental investigation, we have investigated the facilities and investigational implication of a bevel geometrical hypo-dermic needle piloting over studies in ex vivo duck kidneys, and also, evaluated the cutting forces, particularly the force developed in the axial direction during the insertion process. The fresh ex vivo duck kidney was used within 12 h of death as experimental material. The test data were saved by an adjusted measurement instrument (force). A set of insertion analyses with individual insertion speeds (15, 35, and 5 mm/s) in combination with different frequencies of vibration (450, 45, and 110 Hz) was deliberated for assessment to observe the impacts of these two factors on ex vivo perforation force. It is observed that the insertion force at 450 Hz gets up and around 19.3% is linked with the force at 45 Hz. In both the robotically and manual insertion methods, the force increases gradually, but most importantly, in the robotic insertion method, the nature of the insertion force is steady, and in the case of the manual insertion method, the nature of the insertion force fluctuates rapidly.

Visuo-Haptic VR and AR Guidance for Dental Nerve Block Education

Article

Mar 2024

The inferior alveolar nerve block (IANB) is a dental anesthetic injection that is critical to the performance of many dental procedures. Dental students typically learn to administer an IANB through videos and practice on silicone molds and, in many dental schools, on other students. This causes significant stress for both the students and their early patients. To reduce discomfort and improve clinical outcomes, we created an anatomically informed virtual reality headset-based educational system for the IANB. It combines a layered 3D anatomical model, dynamic visual guidance for syringe position and orientation, and active force feedback to emulate syringe interaction with tissue. A companion mobile augmented reality application allows students to step through a visualization of the procedure on a phone or tablet. We conducted a user study to determine the advantages of preclinical training with our IANB simulator. We found that in comparison to dental students who were exposed only to traditional supplementary study materials, dental students who used our IANB simulator were more confident administering their first clinical injections, had less need for syringe readjustments, and had greater success in numbing patients.

Variable Curvature Path Planning for Robot-Assisted Flexible Needle Insertion Based on Improved Bi-RRT Algorith

Article

Jan 2024

Flexible-needle percutaneous puncture is a widely used medical technique in clinical practice and is known for its advantages of minimal patient, fast recovery, and ease of operation. However, ensuring accuracy and avoiding obstacles during the puncture process are challenging because of the non-holonomic motion of the needle tip and the presence of obstacles and sensitive organs. To address this issue, this study proposed a novel path planning algorithm for robot-assisted flexible needle punctures. The algorithm is based on a bidirectional rapidly exploring random tree and incorporates target-oriented and rapid-search strategies. This provides a feasible approach for the clinical application of flexible-needle puncture, outperforming commonly used algorithms in terms of computational speed, path representation, and search capabilities. Additionally, this study applies a duty-cycling control strategy for a flexible needle, enabling proportional control of the curvature of the needle trajectory during soft tissue insertion. The effectiveness of the proposed path planning algorithm was validated through experiments using a duty-cycling strategy, and an experimental error within 4 mm was deemed adequate for most clinical needle-insertion applications.

Modelling and real-time dynamic simulation of flexible needles for prostate biopsy and brachytherapy

Article

Jan 2023

A comprehensive review of haptic feedback in minimally invasive robotic liver surgery: Advancements and challenges

Article

Full-text available

Dec 2023
INT J MED ROBOT COMP

Background Liver medical procedures are considered one of the most challenging because of the liver's complex geometry, heterogeneity, mechanical properties, and movement due to respiration. Haptic features integrated into needle insertion systems and other medical devices could support physicians but are uncommon. Additional training time and safety concerns make it difficult to implement in robot‐assisted surgery. The main challenges of any haptic device in a teleoperated system are the stability and transparency levels required to develop a safe and efficient system that suits the physician's needs. Purpose The objective of the review article is to investigate whether haptic‐based teleoperation potentially improves the efficiency and safety of liver needle insertion procedures compared with insertion without haptic feedback. In addition, it looks into haptic technology that can be integrated into simulators to train novice physicians in liver procedures. Methods This review presents the physician's needs during liver interventions and the consequent requirements of haptic features to help the physician. This paper provides an overview of the different aspects of a teleoperation system in various applications, especially in the medical field. It finally presents the state‐of‐the‐art haptic technology in robot‐assisted procedures for the liver. This includes 3D virtual models of the liver and force measurement techniques used in haptic rendering to estimate the real‐time position of the surgical instrument relative to the liver. Results Haptic feedback technology can be used to navigate the surgical tool through the desired trajectory to reach the target accurately and avoid critical regions. It also helps distinguish between various textures of liver tissue. Conclusion Haptic feedback can complement the physician's experience to compensate for the lack of real‐time imaging during Computed Tomography guided (CT‐guided) liver procedures. Consequently, it helps the physician mitigate the destruction of healthy tissues and takes less time to reach the target.

A Review of Design and Mechanics of Surgical Needle for Minimally Invasive Procedures

Preprint

Aug 2023

Accurate needle insertion is critical during minimally invasive procedures such as biopsy, drug delivery, tumor ablation, and brachytherapy. Various factors affect needle position precision: insertion force, tissue deformation, tissue damage, image-guided tools, surgeons' technique, and obstacles on the insertion path. This paper reviews the current state of needle designs for percutaneous procedures, including mechanics of needle-tissue interactions and steering techniques. Bioinspired needles and coated needles focus on the mechanics of needle-tissue interactions, leading to reductions in insertion-extraction force, tissue deformation, and tissue damage. Different steering techniques: wasp's ovipositor-inspired, pre-curved, tendon-actuated, shape memory alloy (SMA)-actuated are developed to improve maximum curvature, deflection, and targeting accuracy. Quantified results as proof of needle performance advancements are presented if applicable.

Cognitive models for mentally visualizing a sharp instrument in a blind procedure

Article

Aug 2023

Purpose: Our objective was to understand the cognitive strategies used by surgeons to mentally visualize navigation of a surgical instrument through blind space. Methods: We conducted semi-structured interviews with 15 expert and novice surgeons following simulated retropubic trocar passage on 3D-printed models of pelvises segmented from preop MRIs. Midurethral sling surgery involves blind passage of a trocar among the urethra, bladder, iliac vessels, and bowel while relying primarily on haptic feedback from the suprapubic bone (SPB) for guidance. Our conceptual foundation was based on Lahav’s study on blind people's mental mapping of spaces using haptic cues. Participants detailed how they mentally pictured the trocar’s location relative to vital anatomy. We coded all responses and used constant comparative analysis to generate themes, confirmed with member checking. Results: Expert and novice participants utilized multiple cognitive strategies combined with haptic feedback to accomplish safe trocar passage. Some used a step-by-step route strategy, visualizing sequential 2D axial images of anatomy adjacent to the SPB. Others used a map strategy, forming global 3D pictures. Although these mental pictures vanished when they were “lost,” a safe zone could be reestablished by touching the SPB. Experts were more likely to relate their body position to the trocar path and rely on minor variations in resistance. Novices were more inclined toward backtracking of the trocar. Conclusions: Our findings may be extended to any blind surgical procedure. Teaching visualization strategies and incorporating tactile feedback can be used intraoperatively to help learners navigate their instrument safely around vital organs.

A Review of Simulators with Haptic Devices for Medical Training

Article

Full-text available

Feb 2016
J MED SYST

Medical procedures often involve the use of the tactile sense to manipulate organs or tissues by using special tools. Doctors require extensive preparation in order to perform them successfully; for example, research shows that a minimum of 750 operations are needed to acquire sufficient experience to perform medical procedures correctly. Haptic devices have become an important training alternative and they have been considered to improve medical training because they let users interact with virtual environments by adding the sense of touch to the simulation. Previous articles in the field state that haptic devices enhance the learning of surgeons compared to current training environments used in medical schools (corpses, animals, or synthetic skin and organs). Consequently, virtual environments use haptic devices to improve realism. The goal of this paper is to provide a state of the art review of recent medical simulators that use haptic devices. In particular we focus on stitching, palpation, dental procedures, endoscopy, laparoscopy, and orthopaedics. These simulators are reviewed and compared from the viewpoint of used technology, the number of degrees of freedom, degrees of force feedback, perceived realism, immersion, and feedback provided to the user. In the conclusion, several observations per area and suggestions for future work are provided.

Haptic simulation of needle-tissue interaction based on shape matching

Article

Full-text available

Nov 2014

Simulating needle-tissue interaction in a haptic-enabled environment is an essential component in many virtual surgical training procedures, where the trainee would practice needle insertion and retraction repeatedly. Efficiency and stability are of major importance for the corresponding visual and haptic rendering. In this paper, we present a novel system for a real-time and robust simulation of needle-tissue interaction with haptic devices. The soft tissue is modeled using the classic shape matching method for its excellent numerical stability. It can interact with a one dimensional inextensible rigid/flexible virtual needle freely. The feedback force from the needle is formulated as the gradient of the potential energy of the soft tissue based on particle constraint. Under the framework of shape matching, the feedback force can be efficiently evaluated and smoothly rendered through haptic devices. Our model can also support various material properties so that tissues of different stiffness can be well handled.

Psychophysics

Book

Jun 2013

George A Gescheider

Mechanics of needle-tissue interaction

Article

Jan 2011

Biomechanics. Mechanical Properties of Living Tissues

Article

Jun 1982

Erratum: Biophysics and computational biology

Article

Nov 2014

Special issue on medical image computing and computer-assisted intervention - MICCAI 2002

Article

Dec 2003
MED IMAGE ANAL

System Dynamics: An Introduction for Mechanical Engineers

Book

Jan 2014

Karl A. Seeler

This unique textbook takes the student from the initial steps in modeling a dynamic system through development of the mathematical models needed for feedback control. The generously-illustrated, student-friendly text focuses on fundamental theoretical development rather than the application of commercial software. Practical details of machine design are included to motivate the non-mathematically inclined student.

Acute Pain

Article

Nov 1999

Brian L. Ready

Human Olfactory Psychophysics

Article

Jan 2010

The field of psychophysics deals with describing the relationship between physical stimuli and their resultant perceptual phenomena within a rigorous, quantifiable framework. Here we report on the state-of-the-art in human olfactory psychophysics. Olfactory perception can be described in functionally hierarchical terms as consisting of a basic behavior of olfactory detection, followed by the more demanding task of olfactory discrimination which calls upon short-term memory, and culminating in olfactory identification that consists of not only detecting and discriminating the stimulus, but also pairing it with verbal-semantic labels stored in long-term memory. Here, we use this functional hierarchy as an organizational guide. Regarding olfactory detection thresholds, the existing data is typified by extreme variance, both within and across laboratories. This variance is primarily the result of differences in methods of odorant delivery and the application of statistical criteria. Despite this variance, there is general agreement that human olfactory detection thresholds are very low, or in other words, the human nose is a first-rate chemical detector. For example, if we were to add just three drops of ethyl mercaptan into one of two Olympic swimming pools, an average person would be able to detect which pool had the added odor.Regarding olfactory discrimination, humans can discriminate very small differences in concentration or molecular structure between olfactory stimuli. For example, increases in odorant concentration are perceived as increases in odorant intensity. When using a suprathreshold concentration of n-butyl alcohol as a standard, humans can discriminate changes in concentration as small as 7%. This fraction is similar to those found in vision and audition. Similarly, the smallest of changes in odorant molecular structure, such as the addition of a single carbon to a carbon chain, results in clearly discriminable stimuli... Regarding olfactory identification, perception of odor mixtures suggests that the olfactory system identifies odor objects, whose formation depends on previous associations. The rules underlying the link between particular structural odorant properties and odorant identity have been difficult to establish, due in part to variability in the use of language to describe odor. Understanding these rules remains the biggest question in olfactory psychophysics, and arguably in olfaction in general, and we outline several methods that are being used in an effort to solve this mystery.

Haptic Feedback in Needle Insertion Modeling and Simulation: Review

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