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Electronic design and validation of Powered Knee Orthosis system embedded with wearable sensors

Authors:
Electronic
Design and Validation of Powered Knee
Orthosis System embedded with Wearable Sensors
Paulo Félix, Joana Figueiredo, and Cristina P. Santos
Center for MicroElectroMechanical Systems (CMEMS)
University of Minho
Guimarães, Portugal
Juan C. Moreno
Neural Rehabilitation Group
Cajal Institute, Spanish National Research Council
Madrid, Spain
Abstract The development of new architectures for orthotic
devices has been playing a major role in the rehabilitation of gait
disorders. This paper proposes a new electronic and control
architecture for a powered orthosis, particularly, a knee orthosis.
The system was designed to be modular, being composed of the
orthosis and biomedical wearable sensors, such as inertial
measurement units, force sensitive resistors, and
electromyography. For each component, robust hardware and
software interfaces were designed and validated, plus two tracking
control strategies, namely, position control, that imposes a
trajectory based on the angle measured in the joint, and torque
control to act mostly as a passive component, using the measured
user-orthosis interaction torque. The whole system was validated
with healthy subjects walking in level-ground on a treadmill at
different speeds. The main results show that the system is
functional. The interfaces created as well as the assistive control
techniques were successfully validated. Moreover, the system
allows an efficient inclusion of other devices, given the modularity
achieved in its design.
Keywords Gait Rehabilitation; Powered Knee Orthosis;
Werable Sensors; Real-time Control
I. INTRODUCTION
In the last six decades, powered lower-extremity
exoskeletons and active orthoses have been widely addressed in
the fields of rehabilitation, assistive, and empowering devices
[1]. Such devices have been developed with different types of
mechanical structure, actuators, interfaces, and assistive
strategies to increase the physical performance of the wearer and
to provide assistance to the motion, in a wide range of
applications [1], [2]. Regarding the active lower limb orthoses,
these devices act in parallel with the human limb, being mostly
designed to assist subjects with lower limb pathologies [3].
Several orthotic devices have been developed for specific
application to the knee (e.g., climbing stairs [4], squatting [4]
[6]; stand-to-sit and sit-to-stand tasks [5], [7], [8], and running
[9]). In general, they rely on electric actuators, such as DC [6],
[9][12] and AC servo [13] motors, series elastic actuators [7],
[13], and pneumatic actuators [5], [8]. Additionally, these
systems are embedded with sensors of several kinds to measure
system variables fundamental in the assistive techniques and
evaluation of the user’s performance, e.g., encoders [4], [6], [7],
[13], potentiometers [9], [11], Hall effect sensors [10], and
Inertial Measurements Units (IMUs) [11], [13] for angle and
velocity determination and gait phase estimation; load cells [4],
[12], foot-switches and Force Sensitive Resistors (FSRs) [9],
[11] for ground contact detection; force sensors [10], [13] and
motor current-measurement [6] for motor torque calculation;
and electromyography (EMG) surface electrodes [5], [10] for
muscle activity measurements. Furthermore, distinct types of
assistive strategies have been applied (e.g. model-based control
[4], [6], [13], predefined gait trajectory control [5], [11], [12],
and predefined action based on gait pattern [8], [9]) to assist the
users in a set of activities and therapies.
The primary aim of this work compromises the development
of a new electronic and real-time control architecture for a
Powered Knee Orthosis (PKO). The system is formed by an
orthotic device and external wearable sensors, such as IMUs,
FSRs and EMG modules, enabling the development of smart
rehabilitation tools and motion assistive techniques. This paper
discloses modular hardware and software interfaces, as well two
tracking control strategies (position- and torque-based trajectory
control) for assistance in ground-level walking. Moreover, an
adaptive gait event detector is also included for gait pattern
analysis, using the data recorded from one gyroscope mounted
in the instep of the foot. The detector stands out by identifying
six human gait events, i.e. Heel Strike (HS), Flat Foot (FF),
Middle Mid-Stance (MMST), Heel-Off (HO), Toe-Off (TO),
and Middle Mid-Swing (MMSW) with high detection rate.
Concerning the validation of the whole system, trials with
healthy subjects in level-ground walking were conducted, for
both assistive strategies at different speeds. Overall, the major
contribution outlined in this papers focuses on the technical
description, design and validation of a novel modular
architecture for a PKO and its embedded sensory system, which
stands out by the ability of monitor the assisted human gait in
terms of kinematic, kinetic and physiologic parameters.
II. SYSTEM DESIGN
A. System Overview
The presented hard real-time system is centered on a
microcontroller (MCU) connected to an actuation device (PKO)
and wearable sensors (IMUs, FSRs, and EMGs). As presented
in Fig. 1, the PKO has embedded an electronic actuator (DC
brushless motor) and sensors (e.g., potentiometer and strain
gauges), all directly used in control techniques. Moreover, the
system is equipped with external sensors to supplement the
information provided by the PKO. Their combination with the
orthotic device provides a way for a more complex set-up that
goes behind the orthosis domain, and extends to the analysis
This work was supported by Fundação para a Ciência e Tecnologia (FCT)
with the scholarship references SFRH/BD/108309/2015 and
SFRH/BD/102659/2014, and on th e scope of project LIACC with reference
PEstC/EEI/UI0027/2015; by Fundo Europeu de Desenvolvimento Regional
(FEDER); by Programa Operacional Factores de Competitividade (POFC)
COMPETE. Also, this work was partially funded by FCT with the reference
project UID/EEA/04436/2013, and by FEDER funds through the COMPETE
2020 with the reference project POCI-01-0145-FEDER-006941, and by grant
RYC-2014-16613 by Spanish Ministry of Economy and Competitiveness.
978-1-5090-6234-8/17/$31.00 ©2017 European Union
and monitoring of the gait pattern (e.g., foot plantar pressure
analysis, gait segmentation, and intention recognition), and
evaluation of the user performing distinct tasks (e.g., evaluation
of disability level, effort and progression among therapies).
The proposed system has been designed under a modular
approach, to allow for further inclusion of other wearable
sensors and orthoses. Although a functional version is only
implemented to the right leg, its design intends to target both
legs similarly. Heretofore, one PKO is mounted in the right
knee joint, one IMU was mounted on the instep of the right foot,
two FSR were placed in the right heel and toe, and EMG
electrodes (six channels) were attached to the surface of the
main lower limb muscles involved in the joint movements.
Moreover, these devices present distinct interfaces (digital and
analog) with the MCU, as illustrated in Fig. 1. The PKO and the
IMU are prepared to communicate with the same digital
protocol, i.e. a Control Area Network (CAN), while the FSRs
and EMGs have standard analog interfaces for the acquisition
of their output signals, i.e. Analog-to-Digital Converters
(ADCs) available in the MCU. The system stands for using
CAN, given its strict determinism, data collision avoidance,
optimized data transfer, and multiple-access points that allow
new devices to be easily connected to the physical layer [14].
B. Components and Interfaces
1) Processing Unit and Communication Interfaces
The choice of the processing unit was made regarding the
main requirements for the implementation of the system in real-
time. In general, the system must be fast and resourceful enough
to run advanced assistive motion algorithms and gait analysis
methods, flexible enough to allow easy inclusion of sensors and
actuation systems through Input/Output (I/O) analog interfaces
and/or digital protocols, and portable to provide easy mounting
on users. Thus, we selected the STM32F4-Discovery board
(STMicroelectronics), which is centralized on the
STM2F407VGT high performance MCU with an AMR®
Cortex® -M4 32-bit core, running at 168 MHz. This MCU
meets the proposed requirements, given its key features, such
as the high-speed embedded memories (flash memory up to 1
Mbyte and SRAM up to 192 Kbytes), and the extensive range
of enhanced I/Os and peripherals, with standard and advanced
communication interfaces. The present application takes
advantage of two 12-bit ADCs with 16 channels (ADC1 for
FSRs, and ADC2 for EMG channels), two CAN controllers
(CAN1 for orthoses, and CAN2 for IMUs), and three general-
purposed 32-bit timers (to trigger the acquisition of the sensors
and run the real-time control loop).
For this application, the ADC peripherals were configured
in scan mode (automatic conversion performed simultaneously
on a group) triggered by the overflow of timers. The acquisition
circuits that proceed the sensors were tuned to give an output in
the range 0-3.3V (voltage references of ADC). On the other
hand, two CAN buses were separately created, to establish
communication with the orthosis (CAN1) and IMUs (CAN2),
as shown in Fig. 1. This configuration allows future expansion
to include more active orthoses (up to six) and inertial sensors
(up to sixteen). The CAN controllers incorporated in the
SMT32 MCU are implemented in hardware, therefore, their use
does not bring any additional cost at the software level. Also,
this strategy minimizes scenarios where the bus might be busy,
providing more determinism in the control of both devices.
2) Powered Right Knee Orthosis
The orthosis consists in a modular joint (H2-Joint) from the
lower limb robotic H2-exoskeleton (Technaid S.L., Spain),
developed for gait rehabilitation in stroke survivors [15]. A
technical description of the device is presented in Table 1.
TABLE I. H2-JOINT COMPONENTS
Components
Description
Function
Main Features
Precision
Potentiometer
Angle position
sensor
- 10 kΩ and linearity of ± 0.25%.
-
Coupled to a toothed pulley and belt to
transmit joint’s motion.
- From 0 to 100 degrees.
Strain Gauges
Interaction
torque sensor
- Four strain gauges connected in a full
Wheatstone bridge (enhances accuracy
and sensitivity to temperature).
- From -50 to +50 Nm.
Fig. 1. System’s overview, illustrating the main components and interfaces between them.
Components
Description
Function
Main Features
Hall Effect
Sensors
Motor angular
speed sensor
- Measurement of motor’s angular speed
(rpm)
Brushless DC
Motor
(EC60
-
100W,
Maxon)
Electronic
actuator
-
Nominal voltage of 24V, torque of 221
mNm and current of 4.23 A.
-
86% efficiency.
Strain Wave
Gear Box
(CSD20
-
160-
2A, Harmonic
Driver)
-
Gear ration of 160:1.
-
Continues net torque of 34 Nm and peak
torque of 180 Nm.
-
Coupled to motor.
H2-
Joint
Board
Sensor’s
acquisition and
motor driver
- 64 MHz MCU (DsPIC0F4011,
Microchip).
-
Power management module.
-
Measurements of motor’s current (A).
-
MOSFET drive module.
-
CAN communication transceiver.
- Sensor’s data acquisition.
H2-Joint contains an embedded electronic board
responsible for the communication with external devices,
through CAN. To establish the communication between the
MCU and the board (as well with the IMUs), we created a
circuit based on the CAN transceivers SN65HVD251 (Texas
Instruments, USA). This circuit offers the capability of
transmission and reception between the CAN controller and
CAN bus. The H2-Joint board sends CAN packages with data
collected from the sensors at 1 KHz, and receives packages with
the output command of the controller, in the format presented
in Fig. 2. Also, each joint has an identifier (CAN ID), that is
included in the beginning of the CAN package.
Fig. 2. Representation of exchanged packages between H2-Joint and MCU. ID
is the identifier of the target. DLC counts the bytes of valid information. The
sensors information received in the MCU is organized by angular position (T),
angular velocity (Z), interaction torque (Winter) and motor torque (Wm).
3) IMU device
For measurements of foot kinematic data, we chose the
wearable Tech IMU v4 (Technaid S.L., Spain). This unit
integrates three different tri-dimensional MEMS (micro-
electromechanical systems) sensors, including an
accelerometer (range: ±16 g), a gyroscope (range: ±34.9 rad/s)
and a magnetometer (range: ±8.1 G), and a built-in calibration
which eliminates axes misalignment, sensibility and
compensates the measurements due to temperature variations.
Overall all, this device constitutes an optimal solution given its
small dimensions (11x26x36), weight (10 g) admissible power
consumption (70 mA) and built-in calibration.
The digital acquisition is performed following the protocol
defined by proprietary. Fig. 3 shows a sequential diagram
describing the protocol. As exemplified, the MCU starts the
communication by sending a ‘P’ command (one byte of
information), asking for physical data, ensuring a new
calibration of the device. After, the MCU sends ‘polling’
commands (zero bytes of information) at a given sampling
frequency to collect the data from all sensors. Each response to
the polling commands is composed of five packages of eight
bytes (sent sequentially to the MCU), containing the
information of the axis, represented as a 32-bit float (four
bytes). The chosen sampling frequency was 100 Hz.
Fig. 3. Digital protocol between the MCU and IMU, in a sequential diagram.
4) EMG Module
The EMG module aims the acquisition of electrical activity
of lower limb muscles, according to their effect on the human
joints movement (e.g., hamstrings and quadriceps femoris for
the knee, and tibialis anterior and gastrocnemius for the ankle).
Thus, we selected the MA-420 EMG preamplifier (Motion Lab
Systems, USA). As key features, this device incorporates radio-
frequency interference filters, electrostatic discharge protection
circuitry, a low-impedance output to eliminate cable noise and
cable motion artifacts, and an integral ground reference that
provides immunity to electromagnetic environmental noise,
constituting a reliable solution.
The EMG module is composed by hardware and software
interfaces, represented in Fig. 4. The hardware EMG interface
consists of one board with six channels. For each channel, the
same circuit was designed for proper signal conditioning.
Fig. 4. Hardware and software interfaces for one EMG channel, to process and
convert the analog signal to the respective digital format.
The first stage compromises the signal pre-amplification.
The device is designed to be used with disposable electrodes,
placed on the surface of the skin. The electrical signal is then
amplified with the gain of 20 ±1 (at 1kHz), being the output
voltage in a range of ±0.4 mV to ±40 mV. Additionally, the pre-
amplifier circuit has a high Common-Mode Rejection Ration
(CMRR) of 100 dB, meaning that a great percentage of
common mode voltage is eliminated, maximizing the Signal-
Noise Ratio (SNR). The next state represents an Anti-Aliasing
Low-Pass filter, implemented to avoid aliasing. Since the full
bandwidth of an EMG signal is up to 500 Hz, the cut-off
frequency must be set to this value, eliminating frequencies
outside this band. The last hardware stage has three main
functions, concerning the posterior acquisition by the ADC:
amplification; level-shifting; and voltage limitation of the
signal. The two first stages are performed by means of a
summing amplifier circuit. The same circuit amplifies the signal
with gain in a range of 90 to 260 (selectable gain with high
precision potentiometer) summing a constant voltage
representing half of the high voltage reference of the ADC,
particularly, +1.65 V (VREF+ = +3.3V). The selectable gain
feature was added to allow measurement of the electric activity
of different target muscles, since they can present distinct
amplitudes. At last, a limiter voltage circuit (buffer) is used to
prevent the output signal to exceed the VREF minimum (VREF-
= 0V) and maximum (VREF+ = +3.3V).
With respect to the software created for the acquisition of
the signal, the ADC was programmed to collect the data at 2
kHz (respecting the Nyquist theorem), with 12-bits, and with a
dynamic range of 0.806 mV/bit.
5) FSR modules
The FSR sensors used to measure ground reaction forces in
the foot correspond to the model 406 FSR (Interlink
Electronics). They consist of robust polymer thick film sensors
that exhibit a decrease in resistance when the force applied to
the surface increases. Also, this sensor stands for its high
repeatability (± 2%), cost-effectiveness, and simplicity of use.
Regarding the developed hardware, a simple voltage divider
circuit was designed, having low voltage (near 0V) at the output
if no force is applied and high voltage (around 3.3V) when more
than 10 N (sensitivity range) are measured. Concerning the
software developed for the signal acquisition, it was used the
same strategy implemented in the EMG module, at a sampling
frequency of 100 Hz.
C. Orthosis’ Use
Fig.5 shows one user wearing the proposed system. The
orthotic device was fixed on the right lower limb in four points
with straps: two in the upper limb and two in the lower limb.
Each time the user wears the system, a careful procedure is
made to align the mechanical joint with the human knee joint,
to minimize the loss of mechanical power. Also, the location of
the braces can be adjusted according to the user’s lower limb
length, allowing the device to be wearable and functional for
other users. Also, Fig. 5 discloses how the IMU and FSRs were
mounted in the foot.
Fig. 5. Proposed system (PKO and wearable sensors) mounted in one subject.
D. Tracking Control Strategies
Two control strategies were developed for the first set-up of
the PKO system: position-based trajectory control, which
corresponds to the classic position control and is based on the
difference between the desired angular position (θref) and real
angular position (θm); and torque-based trajectory control,
which is based on the difference between the desired torque
(τref) and the interaction torque between the limb and the
orthosis (τinter). Position control can be used in therapies that
ensure repetitive movements of the user’s limbs, suitable to
improve muscular strength and movement coordination in
neurological patients, such as patients with hemiparesis [15].
Torque control, in a scenario where the torque reference torque
is zero, composes a strategy that minimizes the mechanical
impedance of the joint, allowing the orthosis to behave as a
passive actuator. This approach allows the controller to actuate
at the joint in a way that the user should feel more freedom to
move accordingly with his/her intentions. For instance, this
controller can be used in learning mode, where the trajectories
and interaction torque are recorded, to be posteriorly applied
actively in other strategies.
The real-time control runs on the MCU, at a frequency of 1
KHz. The PKO sensor’s data are read asynchronously, and PID
commands are sent through the CAN bus, at this frequency.
Both control diagrams are presented in Fig. 6.
Fig. 6. Position (above) and torque (below) control schemes.
The controllers implemented are based on a Proportional-
Integral-Derivative (PID) control. The equation that describes
the digital controller generated is presented in Eq. 1.
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 (1)
To find the gains of the controller (Kp, Ki, and Kd), the
Ziegler-Nichols method was used. The correct tuning of these
values must result in a compliant motion, without oscillation in
the trajectory, overshoot response, and instability.
E. Gait Events Detection
As mentioned, the system also incorporates a gait event
detection tool, based on the information recorded from the
MEMS gyroscope, mounted on the instep of the foot (Fig. 7).
The angular velocity from the axis aligned with the sagittal
plane was recorded and computed, to detect six gait events: HS,
FF, MMST, HO, TO and MMSW.
Fig. 7. Gait human events (above) segmented throughout the angular velocity
recorded form the gyroscope over one gait cycle [16].
The proposed method for gait segmentation was based on a
finite state machine with decision rules and adaptive thresholds.
A detailed description of the algorithm and its validation on
healthy subjects are presented by Félix et. al. in [16].
F. Safety
As preliminary safety measures, some features were added
to the orthotic system to prevent damage or unreliable
movements in the users. Firstly, the range of motion of the PKO
was limited in software to 3 98 degrees. This prevents the
device to damage the human legs by applying overextension or
over flexion movements. Additionally, this strategy avoids
stress on the mechanical limits of the joint. Moreover, unstable
and abrupt movements of the joint are avoided by the correct
tune of the controller’s parameters and by limiting the PID
commands. Finally, another safety concern is the alignment of
the joints, that prevents undesired movements.
G. Validation
To validate the whole system, simple trials were conducted
with 5 healthy subjects (3 males and 2 females), with age of
26.80 ± 2.78 years old, height of 1.68 ± 0.07 m, and weight of
64.60 ± 8.5 Kg. The participants were asked to walk in level-
ground (in a treadmill), for different speed (1.0 km/h to 1.8
km/h), with the two assistive strategies proposed: position and
torque control. Simultaneously, data from the IMU and FSRs
were recorded from the foot. Furthermore, the EMG signal from
the tibialis anterior muscle was recorded, for the minimum (90),
medium (175) and maximum (260) gains.
III. RESULTS AND DISCUSSION
One of the goals of this work compromises the validation of
the designed electronic and control architecture. Its
achievement goes through the validation of the system modules
and the tracking control strategies.
A. EMG board
The EMG board proposed was projected with selectable
gains, tunable for the lower limb muscles of each user. During
the trials, the subjects were asked to walk in a treadmill while
the EMG of the tibialis anterior muscle was recorded for
different gains (Fig. 8).
Fig. 8. EMG signal recorded from three trials at 1km/h, with distinct gains
(minimum, medium, and maximum).
Fig. 8 shows that the EMG signal recorded (1 km/h) when
the gain is set to maximum presents better quality in the stance
phase (regions with maximum amplitude) when compared with
the medium and minimum. This can also be inferred with the
SNR obtained for each plot, -22.38 dB, -20.90 dB, and -19.49
dB, respectively, that shows a lower value (less noise) when the
gain is higher. Also, the signal never saturates for this walking
speed, such that there is no loss of information. A similar
procedure with the same muscle was made to validate the other
channels of the EMG board.
B. FSRs and IMU
The FSRs and IMU were both mounted on the foot (see Fig.
5). Fig 8 shows the data collected in one trial (1.5 km/h).
Fig. 8. FSRs and gyroscope signals recorded from foot, walking at 1.5km/h.
HS
HO
TO
FF
MM
ST
MM
SW
Through the data acquired, Fig. 8 shows the well-
functioning of the interfaces for the FSRs and IMU. As
illustrated, the output of the FSRs (heel in green and toe)
follows the detection of the human gait events (in black)
throughout the gyroscope signal (in blue).
C. Tracking Control Strategies
The two tracking control strategies were also validated
during the trials. Regarding the position control (Fig. 9), a
reference trajectory (blue line) was imposed to the user.
Although a delay between the control variables (reference and
real position) is observed, this approach shows good results
considering that the movement of the user’s limb is performed
smoothly. During the PID tuning, it was noticed that higher
values of the PID gains provoke abrupt movements of the joint,
which can cause discomfort or instability to the user.
Fig. 9. Output signal from position control, walking at 1 km/h.
In the second approach (Fig. 10), the reference torque (black
line) was set to zero. As expected, this allowed the user to move
with freedom in the direction of the interaction force (red line)
measured, with low resistance offered by the orthosis. Thus, the
participants were able to perform distinct trajectories (cyan
line) during walking. Comparing Fig. 9 and Fig. 10, the real
knee angle measured in the joint have a similar shape, although
the position control shows a constant pattern in all steps.
Fig. 10. Output signal from torque control, walking at 1.8 km/h.
IV. CONCLUSION
The development of a new electronic and control
architecture for a powered knee orthosis was presented and
validated in this paper. Each component of the system and
interfaces were tested, with healthy subjects walking in a
treadmill and wearing the orthosis and sensors. Overall, the first
set-up of the system is functional, and ready to be used in
motion assistance therapies. Future work compromises the
inclusion of another powered orthosis, i.e., an ankle orthosis
(another module of H2-exoskeleton) and the replication and
tuning of the components, interfaces, and same control
strategies for this joint. Also, new assistive strategies will be
explored, aiming the development of a compliant actuation for
application in gait rehabilitation interventions of neurologically
injured humans.
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... A literature review of the current orthotics was conducted, which helped understand their design constraints, performance, and limitations. Most of the knee orthoses were to assist people with quadriceps muscle weakness (QMW) [4], [65], [66], stroke patients [67], [68], [69] elderly people [70], [71], [72], [73] and in gait rehabilitation [74], [75], [76], [77], [78], [79], [80], [81], [82], [83], [84].It is also designed for paralyzed people and lower-limb amputees [85], [86]. These devices assisted patients with their knee flexion and extension movements [75], [76], [80], [87], gait movements and raising mobility tasks as well. ...
... It includes Knee-Ankle-Foot Orthoses (KAFO), Stance-control KAFO (SCKAFO), Powered Knee Orthoses (PKO), robotic assists, exoskeletons, etc. Various mechanical and biomechanical tests are carried to understand the performance of the devices. Most of the biomechanical tests consist of a walking test [4], [70], [72], [81], [82], [85], [88], [89], [90], [91] where the individual is asked to walk a certain distance with and without the orthotic device. The results are then compared for the evaluation. ...
... Sitstand and stand-sit tests [65], [68], [73], [91], [92] are also performed to check the locking system performance which helps in gait assistance by providing a locking mechanism during weight acceptance phase and allowing free knee flexion during the swing phase. Treadmill experiments [69], [70], [77], [78], [81], [82], [89], [92] are carried out to verify the ground reaction forces. The important measured parameters are gait speed and gait pattern. ...
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This case study gives an overview of the prosthetics and orthotics used on patients for better gait rehabilitation. Many people suffering from various muscular and neurological diseases, lower-limb amputees, elderly are dependent on various prostheses and orthoses for a better quality of life. The review is considered under three categories: above-knee prosthesis, below-knee prosthesis, and orthotics. Each group is divided into its subdivisions with respect to the part of the body it is suspended. This paper reviews the currently available prostheses and orthoses. An analysis of the various designs and materials for each device, as well as the discussion of their limitation, are provided.
... InertialLAB's software was designed to be modular and open-architecture with the possibility of full customization to operate as a stand-alone solution for general human motion analysis [14] and to be easily and directly integrated into third-party systems, namely a powered orthosis [16]. Such modularity will enable a prompt integration of the software routines into other CPU, limiting the changes to the peripheral devices' configuration routine. ...
... The hardware and software interfaces of InertialLAB were designed with a modular and open architecture with the possibility to operate as a stand-alone solution for general human kinematic analysis and to be directly integrated into third-party systems. Appendix A demonstrates the feasibility of the InertialLAB's integration into a powered exoskeleton, described in [16], for four-fold purposes. First, for real-time gait event detection, as described in [14], to adjust the human-orthosis dynamics for the adaptive impedance control strategy (proposed in [37]). ...
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This paper presents a cost- and time-effective wearable inertial sensor system, the InertialLAB. It includes gyroscopes and accelerometers for the real-time monitoring of 3D-angular velocity and 3D-acceleration of up to six lower limbs and trunk segment and sagittal joint angle up to six joints. InertialLAB followed an open architecture with a low computational load to be executed by wearable processing units up to 200 Hz for fostering kinematic gait data to third-party systems, advancing similar commercial systems. For joint angle estimation, we developed a trigonometric method based on the segments’ orientation previously computed by fusion-based methods. The validation covered healthy gait patterns in varying speed and terrain (flat, ramp, and stairs) and including turns, extending the experiments approached in the literature. The benchmarking analysis to MVN BIOMECH reported that InertialLAB provides more reliable measures in stairs than in flat terrain and ramp. The joint angle time-series of InertialLAB showed good waveform similarity (>0.898) with MVN BIOMECH, resulting in high reliability and excellent validity. User-independent neural network regression models successfully minimized the drift errors observed in InertialLAB’s joint angles (NRMSE < 0.092). Further, users ranked InertialLAB as good in terms of usability. InertialLAB shows promise for daily kinematic gait analysis and real-time kinematic feedback for wearable third-party systems.
... Paulo and his colleagues produced an electronic design and validation of a powered knee orthosis system incorporated with wearable sensors, which was reported in the IEE international conference on Autonomous Robot System and Competitions. This orthosis will help patients' knees and will measure real-time EMG of knee movement [70]. Powered lower limb exoskeleton leg orthosis developed by Stefen and his group to assist knee compliance reduces peak swing collision forces in lower limb so that it can help to lower risk of failing patients [71,72]. ...
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The three-dimensional (3D) printing in medical implants unlocks unparalleled opportunities to completely configure the product to the patient’s measurements and needs. To be noted, the use of personalized 3D printed orthosis used in regeneration for serious orthosis implants of specific patients is growing to date. The 3D printed is unique to the patient instruments that can be used to facilitate correct positioning of implants and improved functional outcomes. The 3D printing, also defined as ‘rapid prototyping’ and ‘additive manufacturing’ is widely regarded as the ‘second technological revolution. The orthosis is an “externally applied mechanism used to alter the structural and functional properties of the musculoskeletal and skeletal system”. Applications in orthosis healthcare that are pioneering the way 3D printing is performed, changing the orthosis implant markets. This paper is reporting literature on the development of orthosis using 3D printing technology that could make the users more comfortable and easier to maintain. From the literature search, this paper summarises some important information about the use of 3D printing for orthosis development where it focusses on specific regions of human body, the materials for the 3D printed orthosis and further directions of this technology and research. In conclusion, the findings from this review paper may lead to a future recommendation and study in providing better treatment for patients.
... The interaction between these devices and users is carried out through bio-metric signal processing systems, which regulate actuator's speed and position of each joint in order to meet specific tasks [1,2]. These interaction systems provide autonomy in the operation of the device when a patient uses the robotic exoskeleton [3]. Several studies about robotic exoskeletons demonstrate their advantages [4,5,6], among which, are their high accuracy of movements, adaptability to the user needs, and portability. ...
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This paper presents the design, development and evaluation of an energy management system (EMS) for covering the energy demand from an autonomous lower limb exoskeleton (ALLEX) prototype. The ALLEX prototype is composed by four energy subsystems: actuators, sensing, communications, and control. The energy demanded by ALLEX is estimated by considering metabolic requirements of neurological rehabilitation applied to the actuators subsystem, as well as average consumption of the sensing, communications, and control subsystems. The EMS proposed in this paper is composed by a lithium-ion battery bank, a battery management system (BMS), and proper instrumentation for measuring voltages, currents, and temperature from the battery pack. Experimental results show adequate coverage of the energy demand from ALLEX, both instantly and during continuous operation (1 hour approximately). Additionally, the efficiency of the EMS is assessed by testing the cells balancing and battery charg-ing/discharging processes, which showed equalized values of the energy cells as well as correct temperature operating values.
... This trajectory is set as the ankle-foot exoskeleton reference joint angle by the mid-level controller. The low-level control is based on a close-loop proportional-integral-derivative (PID) controller described in [16]. The mid-level and low-level control ran at 1000 Hz on an STM32F4-Discovery board (STMicroelectronics, Switzerland). ...
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The adjunctive use of biofeedback systems with exoskeletons may accelerate post-stroke gait rehabilitation. Wearable patient-oriented human-robot interaction-based biofeedback is proposed to improve patient-exoskeleton compliance regarding the interaction torque’s direction (joint motion strategy) and magnitude (user participation strategy) through auditory and vibrotactile cues during assisted gait training, respectively. Parallel physiotherapist-oriented strategies are also proposed such that physiotherapists can follow in real-time a patient’s motor performance towards effective involvement during training. A preliminary pre-post controlled study was conducted with eight healthy participants to conclude about the biofeedback’s efficacy during gait training driven by an ankle-foot exoskeleton and guided by a technical person. For the study group, performance related to the interaction torque’s direction increased during (p-value = 0.07) and after (p-value = 0.07) joint motion training. Further, the performance regarding the interaction torque’s magnitude significantly increased during (p-value = 0.03) and after (p-value = 68.59 × 10−3) user participation training. The experimental group and a technical person reported promising usability of the biofeedback and highlighted the importance of the timely cues from physiotherapist-oriented strategies. Less significant improvements in patient–exoskeleton compliance were observed in the control group. The overall findings suggest that the proposed biofeedback was able to improve the participant-exoskeleton compliance by enhancing human-robot interaction; thus, it may be a powerful tool to accelerate post-stroke ankle-foot deformity recovery.
... Robotic exoskeletons are able to assist limb movements as well as to provide missing capabilities of the human body, so their applications are easily adapted to physical rehabilitation, whose primary outcome is the patient's ability to recover walking independence [2]. The interaction between these devices and users is mainly performed by digital systems which process biometric signals to anticipate movement intentions [3] and to coordinate the device motion with the user movements [4], enabling autonomous operation of the exoskeletons while used by patients [5]. ...
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In this paper, a knee exoskeleton device and its Tele-Impedance based assistive control scheme is presented. The exoskeleton device is an inherently compliant actuated system that was implemented based on the series elastic actuation (SEA) to provide improved and intrinsically soft interaction behaviour. Details of the exoskeleton design are presented. A detailed musculoskeletal model was developed and experimentally identified in order to map electromyographic signals to the antagonistic muscle torques, acting on the human knee joint. The estimated muscle torques are used in order to determine the user's intent and joint stiffness trend. These reference signals are exploited by a novel Tele-Impedance controller which is applied to a knee exoskeleton device to provide assistance and stiffness augmentation to the user's knee joint. Experimental trials of a standing-up motion task were carried out for evaluation of the proposed control strategy. The results indicate that the proposed knee exoskeleton device and control scheme can effectively generate assistive actions that are intrinsically and naturally controlled by the user muscle activity.
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A number of studies discuss the design and control of various exoskeleton mechanisms, yet relatively few address the effect on the energy expenditure of the user. In this paper we discuss the effect of a performance augmenting exoskeleton on the metabolic cost of an able-bodied user/pilot during periodic squatting. We investigated whether an exoskeleton device will significantly reduce the metabolic cost and what is the influence of the chosen device control strategy. By measuring oxygen consumption, minute ventilation, heart rate, blood oxygenation and muscle EMG during 5 minute squatting series, at one squat every 2 seconds, we show the effects of using a prototype robotic knee exoskeleton under three different noninvasive control approaches: gravity compensation approach, position based approach and a novel oscillator based approach. The latter proposes a novel control which ensures synchronization of the device and the user. Statistically significant decrease in physiological responses can be observed when using the robotic knee exoskeleton under gravity compensation and oscillator based control. On the other hand, the effects of position based control were not significant in all parameters although all approaches significantly reduced the energy expenditure during squatting.
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Background: The aim of this case study was to identify the effect of a powered stance control knee ankle foot orthosis on the kinematics and temporospatial parameters of walking by a person with poliomyelitis when compared to a knee ankle foot orthosis. Case description and methods: A knee ankle foot orthosis was initially manufactured by incorporating drop lock knee joints and custom molded ankle foot orthoses and fitted to a person with poliomyelitis. The orthosis was then adapted by adding electrically activated powered knee joints to provide knee extension torque during stance and also flexion torque in swing phase. Lower limb kinematic and kinetic data plus data for temporospatial parameters were acquired from three test walks using each orthosis. Findings and outcomes: Walking speed, step length, and vertical and horizontal displacement of the pelvis decreased when walking with the powered stance control knee ankle foot orthosis compared to the knee ankle foot orthosis. When using the powered stance control knee ankle foot orthosis, the knee flexion achieved during swing and also the overall pattern of walking more closely matched that of normal human walking. The reduced walking speed may have caused the smaller compensatory motions detected when the powered stance control knee ankle foot orthosis was used. Conclusion: The new powered SCKAFO facilitated controlled knee flexion and extension during ambulation for a volunteer poliomyelitis person.
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A Powered Knee Orthosis (PKO) was developed for the elderly and patients with disordered gait to regain normal walking. In order to enhance the PKO performance and reduce system complexity especially for people with muscle weakness in their knee joints, an algorithm named HIP-KNEE control is proposed. This algorithm is based on the analysis of kinematic gait model, and the desired knee joint angle (KNEE) is estimated from the measurements of hip joint angle (HIP). The relationship between HIP and KNEE is modeled as a polynomial, which can be easily implemented to an embedded controller for real-time control. This control method is suitable to subjects with good function in hip joint, and it can provide help in walking without special training. An Inertia Measurement Units (IMU) is used for obtaining HIP input, and integrated with a footswitch for checking the heel condition; the gait assistance performance can be further improved.
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Owing to the recent progress in the field of supportive robotic technologies, interest in the area of active orthoses and exoskeletons has increased rapidly. The first attempts to create such devices took place 40 years ago. Although many solutions have been found since then, many challenges still remain. Works concerning the lower extremities and active orthoses are listed and described in this paper. The research conducted and commercially available devices are presented, and their actuation, hardware, and movements they make possible are described. In addition, possible challenges and improvements are outlined.