Evaluation of User-Prosthesis-Interfaces for sEMG-Based
Multifunctional Prosthetic Hands
Julio Fajardo 1,2,* , Guillermo Maldonado 1, Diego Cardona 1, Victor Ferman 2and Eric Rohmer 2
Citation: Fajardo, J.; Maldonado, G.;
Cardona, D.; Ferman, V.; Rohmer, E.
Evaluation of User-Prosthesis-
Interfaces for sEMG-Based
Multifunctional Prosthetic Hands.
Sensors 2021,21, 7088. https://
Academic Editors: Biswanath
Samanta and Ennio Gambi
Received: 24 August 2021
Accepted: 21 October 2021
Published: 26 October 2021
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1Turing Research Laboratory, FISICC, Galileo University, Guatemala City 01010, Guatemala;
email@example.com (G.M.); firstname.lastname@example.org (D.C.)
2Department of Computer Engineering and Industrial Automation, FEEC, UNICAMP,
Campinas 13083-852, Brazil; email@example.com (V.F.); firstname.lastname@example.org (E.R.)
*Correspondence: email@example.com or firstname.lastname@example.org
The complexity of the user interfaces and the operating modes present in numerous
assistive devices, such as intelligent prostheses, inﬂuence patients to shed them from their daily
living activities. A methodology to evaluate how diverse aspects impact the workload evoked when
using an upper-limb bionic prosthesis for unilateral transradial amputees is proposed and thus able
to determine how user-friendly an interface is. The evaluation process consists of adapting the same
3D-printed terminal device to the different user-prosthesis-interface schemes to facilitate running
the tests and avoid any possible bias. Moreover, a study comparing the results gathered by both
limb-impaired and healthy subjects was carried out to contrast the subjective opinions of both types
of volunteers and determines if their reactions have a signiﬁcant discrepancy, as done in several
Keywords: assistive robotics; upper-limb prosthesis; electromyography; user-prosthesis interface
Several works in the literature present substantial progress in advanced bionic pros-
thetic devices in recent years, offering people with disabilities many different alternatives
and characteristics to improve their condition. This progress includes promising works in
haptics [1,2] and diverse methods to recover and interpret the user intent [3–6]. However,
little to no effort has been directed into research for providing a simple and easy-to-use
user-prosthesis interface (UPI). This aspect is directly related to the patient’s subjective
perception of the prosthetic device itself, greatly inﬂuencing its use or not. This way, it has
been already proven that the acceptability for such devices depends more on the lack of
effort to operate it than consistently achieving successful grasps .
Some methods to operate upper-limb prostheses do not implement a graphical UPI,
controlling the device exclusively by analyzing a speciﬁc activation proﬁle based on pro-
cessing electromyography (EMG) signals. Some of these iterations substitute the visual
stimuli by utilizing other types of feedback, such as vibrotactile ones [
]. Moreover, oth-
ers include implants that utilize Bluetooth or radio channel waves to communicate with
]. These versions use wireless charging to function and regulate the power
dissipation inside a safe range to avoid damage to the user’s skin tissue.
On the other hand, some approaches use brain-machine interfaces (BMI) to control
these devices, eliminating any visual stimulus to interact with the artiﬁcial limb and resem-
bling the way limbs are usually operated. Newer methodologies are based on high-density
electrocorticography (ECoG), which allows the patient to control each ﬁnger individually
through an adequate re-innervation process [
]. However, these interfaces require very
intrusive and expensive procedures. Other projects utilize interaction processes that do
not seem intuitive to the users, employing more creative approaches to analyze the EMG
signals by using other members to drive the movements of the prosthetic limb, as shown
Sensors 2021,21, 7088. https://doi.org/10.3390/s21217088 https://www.mdpi.com/journal/sensors
Sensors 2021,21, 7088 2 of 20
], which use the toes and the tongue, respectively. Such techniques result in viable
alternatives, especially for bilateral amputees. However, such methodologies may not
be the best option for unilateral transradial amputees since they affect how some typical
activities of daily living (ADLs) must be carried out.
Alternatively, the majority of sophisticated research assistive devices are based on
multimodal approaches. These methodologies usually consist of taking a set of predeﬁned
and well-known EMG features and complementing them with information from other kinds
of sensors such as inertial measurement units (IMUs), micro-electromechanical systems
(MEMS) microphone, mechanomyography (MMG), or force myography (FMG) showing a
substantial improvement in classiﬁcation rates and bi-manual
approach has been used successfully to improve the user control of prosthetic devices in
different manners, such as using a multimodal system with Radio Frequency Identiﬁcation
(RFID) tags on speciﬁc objects. In this stance, the cognitive effort is reduced to operate
an upper-limb prosthetic device and address some of the well-known issues of EMG
techniques, such as the limb position effect [
]. Other stances have been taken into
account using the multimodal approach, such as utilizing voice-control, in tandem with
visual feedback through a small embedded touchscreen LCD, providing the users with
other alternatives to control their prosthetic device in different manners [17,18].
Finally, other studies have been carried out to increase upper-limb prostheses’ func-
tionality, combining surface EMG (sEMG) and deep-learning-based artiﬁcial vision systems.
This approach works by associating a subset of predeﬁned objects to a list of speciﬁc grasps
based on the target’s geometric properties, which are gathered by different types of cam-
eras. Such classiﬁcation processes are fulﬁlled via convolutional neural networks (CNN)
employing customized image object classiﬁers.
This work focuses on a methodology to evaluate how different UPIs for transradial
upper-limb prostheses inﬂuence the user’s workload and how user-friendly they are. It
is known that several studies have been conducted to evaluate speciﬁc prosthetic devices
with unimpaired subjects only [
]. The evaluation is subjective, and some assumptions
are made regarding the limb-impaired that may not always be accurate. Therefore, these
evaluation processes may show a practical and moral dilemma, especially true when
considering the interaction process with assistive devices. Therefore, an extension of
previous works [
] was carried out, in which the results of the evaluation process
were collected only with unimpaired subjects. This work includes results of an evaluation
process from information gathered from impaired ones and compares results of both types.
Thus, we verify that the results obtained from both are strongly related and verify the
viability and validity of creating such supposition.
The evaluation process was achieved by employing a customized EMG wireless
module (Thalmic Labs’ Myo armband ) to gather user intent, facilitating the device’s
installation independently of the user, and then comparing the retrieved results on the
impact that certain aspects may have on the interaction process. The module was selected
for operating the different UPIs through this work since it is an affordable and viable
replacement for the medical-grade sensors (process and classiﬁes sEMG signals by itself),
even with subjects with different levels of transradial amputation [
]. Its small subset
of self-classiﬁed contractions can be adapted to perform a greater number of gestures and
grips. These features facilitate its utilization and the replication of all the interfaces since its
installation process is more comfortable than wired alternatives or implants, removing any
possible bias regarding the sensors to gather the users’ intent evaluating only the UPIs. In
this way, the NASA Task Load Index (TLX) scale was employed to estimate the workload
evoked from considered UPIs for its evaluation. Besides, a survey describing the UPI’s
user-friendliness was perceived and compared the results using a multifactorial ANOVA
analysis in order to determine how user-friendly an interface is.
The rest of this work is structured as follows: Section 2elaborates on the state of
the art of the existing methods to evaluate UPIs. Section 3describes how the whole
system is integrated and elaborates on the details of the replicated UPIs for its evaluation.
Sensors 2021,21, 7088 3 of 20
describes the evaluation processes and their interpretations. Finally, the last
section, Section 5, deals with the impact of the results.
2. State of the Art
Since the development of UPIs has not been a focus in commercial or academic
works, the ones that center themselves in analyzing the interaction between the user and
the artiﬁcial limb are also scarce and usually focus on gathering the user intent, such as
comparing the efﬁciency of EMG methods with force, position, tactile, or even joystick
]. Nevertheless, most of these results conclude a non-signiﬁcant difference
between them or the EMG one’s superiority. Other methodologies achieve enhancements to
collect that information by using hybrid systems, such as using near-infrared spectroscopy
], or like the ones juxtaposed in [
]. On the other hand, works like [
into the impact of short-term adaptation with independent ﬁnger position control and the
relevance of the real-time performance of the prosthetic control and its “ofﬂine analyses”.
Nonetheless, none of the previously mentioned studies provide details on assessing
interfaces in terms of how to interact with the artiﬁcial limb with the selected control. How-
ever, some works have centered on comparing two primary interfaces, pattern recognition
(PR), and direct control [
]. Some of them even considered active users’ subjective opin-
ions and the objective ones from therapists for a perception analysis on multi-functional
upper-limb prostheses [
]. This resulted in general disapproval for the conventional con-
trol for switching between actions and the unreliability of the pattern recognition algorithm
altogether (even though its speed was praised). Nonetheless, a similar approach has not
been taken for a more extensive array of interfaces (at the best of the authors’ knowledge).
Furthermore, regarding the tools that can be used to evaluate assistive robotics, one
can ﬁnd the Psychosocial Impact of Assistive Devices Scale (PIADS), whose purpose is “to
assess the effects of an assistive device on functional independence, well-being, and quality
of life”. This reﬂects the self-described experience of the users and may provide insight
on the long-term use or disuse [
]. Another method that has been utilized to evaluate
assistive robotics is the use of the Human Activity Assistive Technology (HAAT) model,
an outline of clinically relevant aspects that need to be considered in the practice. This
method provides “enhanced access and application for occupational therapists, but poses
challenges to clarity among concepts” [
]. In addition to those, the Southampton Hand
Assessment Procedure (SHAP) also helps to identify which grips are better suited for
speciﬁc prosthetic designs, as it was created to measure the operating range of a hand.
However, it has been criticized for some inconsistencies during the assessment of artiﬁcial
hands and the lack of a measure for their efﬁciency [
]. Another tool commonly employed
is the NASA Task Load Index scale, used to derive an estimate of the workload of different
types of tasks and simulations [
]. Its implementation has been, mostly, centered on
quantifying the subjective perception of interface designs [
], some of them involving
assistive robotics [11,19].
3. Materials and Methods
3.1. Galileo Hand
The Galileo Hand (shown in Figure 1) was the prosthetic device selected to validate this
work. This prosthesis is an open-source and intrinsic device that encases ﬁve metal-geared
micro DC motors to drive the under-tendon-driven (UTD) [
] mechanism of each ﬁnger,
plus an additional DC motor with a quadrature encoder attached to perform the thumb
rotation. This device consists of an anthropomorphic, modular, and intrinsic 3D-printed
ABS shell; its weight and fabrication cost are under 350 g and USD 350, respectively.
Its main controller PCB is based on the ARM Cortex-M4 microcontroller unit (MCU),
consisting of the PRJC Teensy 3.2 development board in tandem with three TI DRV8833
dual motor drivers and one 4D-Systems’ 1.44
LCD-144-G2 screen used to present visual
feedback from the UPIs to the users [18,23].
Sensors 2021,21, 7088 4 of 20
Figure 1. Galileo Hand: anthropomorphic, 3D-printed upper-limb prosthesis.
Each ﬁnger is assembled using waxed strings, which, when are coiling, close the
ﬁngers individually. This process is achieved by motors installed on each ﬁnger, providing
5 degrees of actuation (DOA), plus an additional one for the thumb’s rotation. These
mechanisms are also made up of surgical-grade elastics that allow the ﬁngers’ articulations
to spring back open in a UTD machine model. This conﬁguration provides a total of
15 degrees of freedom (DOF), 1 for the rotation of the tumb and 14 comprised by each joint
in the ﬁngers to simulate ﬂexion and extension (three for each digit, except for the thumb,
which only has two links and two joints). In addition, the thumb is at a 15
angle from the
palmar to emulate both adduction-abduction and opposition-deposition ﬁnger movements.
3.2.1. Adapting the Myo Armband
Since the proposed solution is to incorporate the Myo armband to capture the muscle’s
processed electric signals, a Bluetooth Low Energy (BLE) module, HM-10, was required to
transmit them to the Galileo Hand as interpreted poses. Utilizing the MyoBridge library
and adapting the hardware according to what was proposed in [
] allows for a successful
exchange between the components. The gathered information is later transferred to an
ATmega328P (secondary microcontroller unit) and, posteriorly, to the main MCU to drive
each DC motor; this is illustrated in Figure 2.
System block diagram showing the embedded controller architecture and the integration with external modules.
The complementary MCU is in charge of acquiring the user intent, either as raw EMG
signals or as Myo-speciﬁc poses. Consequently, it converts them into packages transmitted
via UART to the Galileo Hand’s central controller. The HM-10’s ﬁrmware was ﬂashed with
the MyoBridge program, using RedBearLab’s CCLoader as an aide for this procedure to
function aptly. This way, the armband will be able to connect with the BLE module and
transmit the EMG signals correctly. This process was carried out for most of the interfaces,
Sensors 2021,21, 7088 5 of 20
except for the one using an Android app, since the Myo can be connected, by default,
directly to the mobile device.
3.2.2. System Integration
Packet reception is handled using UART interruptions. Once the package is received,
it is evaluated, and action is taken based on the content of the transmission. If the message
contains a Myo-speciﬁc pose, it triggers transitions between Finite State Machines (FSM)
states, described in detail in Section 3.6, used to implement the different UPIs to control the
prosthetic device. Suppose the desired action is to alter the current selection on the screen.
In that case, a notiﬁcation via another UART channel is sent to the independent
microcontroller to perform the change it was ordered to and, thus, present visual feedback
to the user. On the other hand, if the message contains raw EMG signals, the device ﬁlls up
two circular buffers of signals collected by the electrodes placed near the palmaris longus
and the extensor digitorum muscles (for unilateral below-elbow disarticulations). This way,
customized methods to interpret the user intention can be used to adapt the bracelet to the
prosthesis, such as works presented in [26,44].
3.3. Control Strategy
Once the user’s intent has been received, the high-level controller (HLC) uses this
information to perform the necessary action that each ﬁnger must take to achieve predeﬁned
gestures and grips available to the user. Whereas at a low level, each ﬁnger functions with
an individual hybrid control strategy for the ﬂexion and extension processes, except for
the thumb, which also has a quadrature encoder to implement a PI position controller to
perform its rotation. Since the armature current
of each DC motor is the only feedback
signal measured from the system, a simple current on-off controller is implemented to
perform the ﬂexion process. In addition, a robust full-state observer is utilized to estimate
the angular velocity and displacement,
, of the gearhead shaft of each motor [
a robust state feedback controller is used to perform the extension process. This way,
the prosthesis can perform the different predeﬁned grasps, i.e., power and lateral grips,
hook, etc. The functionality for each digit is illustrated in the Finite State Machine in
Finite State Machine demonstrating the opening/closing behavior of each ﬁnger on the
indicates that the ﬁnger is entirely open;
, represents the ﬂexion process triggered by
, indicates the ﬁnger is completely open (since
the extension process triggered by command ountil θ≈θ0.
The prosthesis starts with all its ﬁngers fully extended (in an “open” or “rest” position,
), represented by the state
. Thus, when the command to move a particular
, is received from the high-level controller, the transition to the state
activating the motor and causing the ﬁnger’s ﬂexion. In this state, the RMS value of the
Sensors 2021,21, 7088 6 of 20
, is monitored continuously and, when a predeﬁned threshold related
experimentally to the ﬁngertip wrench,
, is exceeded, the transition to
parameter differs for each ﬁnger since each has discrepant mechanical factors due to their
different size and length of the strings and elastics. Therefore, a proper calibration was
The ﬁnger is considered fully closed at this state and will start with the ﬂexion process
opening the ﬁnger if the
command is issued by the HLC, as shown by the transition
. Finally, the transition from states
happens after the angular
is approximated to its initial value
0. This strategy was adopted
since the elastic installed on each ﬁnger opposes itself to the coiling process but favors the
unfurling one; therefore, ensuring that the motor shaft’s angular displacement is equal
during both processes is essential. Finally, it is relevant to note that the closing/opening
procedures may be interrupted and reversed if the appropriate commands are received.
3.4. Gestures Adapted to the Prosthesis
The purpose of this subsection is to detail and clarify the actions at the patients’
disposal. The selected grasps are the following: “Close” (ﬂexion of all the ﬁngers and
rotation of the thumb, power grasp), “Hook” (the thumb is the only ﬁnger extended, it is
also adducted), “Lateral” (coiling of the strings of all ﬁngers and the thumb is abducted),
“Pinch” (ﬂexion of the index and thumb, plus abduction of the thumb, precision grasp),
“Point” (all motors are actuated, except for the index), “Peace” (all ﬁngers are closed,
except for the index and the middle ﬁnger), “Rock” (ﬂexion of all ﬁngers, but the index
and the little ﬁnger; thumb adducted), “Aloha” (the index, middle and annular ﬁngers are
ﬂexed), “Three” (all motors are actuated except for the index, middle and annular ﬁngers),
“Four” (similar to the previous gesture, but with the little ﬁnger extended), “Fancy” (the
only extended ﬁnger is the little ﬁnger, with an adducted thumb) and “Index” (where
the only ﬂexed ﬁnger is the one giving the name to the action). Some of these gestures
are illustrated in Figure 4. An important note is that some of the actions installed are
for demonstrative purposes only. Other grasps may substitute some of the gestures for a
more personalized approach or even reduce the number of actions available if they are
The image shows the Galileo Hand grabbing the objects used in the trials. On the upper
left the hand is holding a ”water bottle”; on its right, a small plastic ”ball”; underneath, from left to
right, holding a ”wallet” and ”pointing”, respectively.
Sensors 2021,21, 7088 7 of 20
Now, the supported gestures for each evaluated interface will be enumerated. The tra-
ditional pattern recognition interface can complete the ﬁrst four actions from the previous
list. On the other hand, the version in Section 3.6.3, the one using the app, can fulﬁll the
same as the previous iteration, plus “Pinch” and “Peace”. Finally, the rest of the interfaces
allow the user to select any hand actions available on the menu.
3.5. NASA Task Load Index
The NASA-TLX test was used to measure and analyze the workload evoked by each
interface under evaluation, as done in [
]. This test was selected to evaluate
the impact that each UPI has on the users’ workload effectively. So, considering that the
post-test evaluation techniques, such as SUS, do not permit evaluating different parts
of the interface separately, and methods such as SEQ do not consider many different
categories during testing, providing more binary results, the NASA-TLX scale was selected
because it requires user testing through a post-task evaluation method for each interface
taking into account six different workload categories: mental, physical, and time demand,
the performance, the effort needed to operate it, and the frustration evoked. In this
work, the index quantiﬁes the effectiveness and performance of the workload to operate a
prosthetic device using a given UPI; besides, it is also considered a more comprehensive
test to evaluate user interaction, with well-known research and industry benchmarks to
interpret scores in the context, which can be helpful for future works.
In addition, a binary response survey was used to determine if a user perceived an
interface as user-friendly or not, intending to compare its results with the workload evoked
by each UPI. Finally, a multifactorial ANOVA analysis is performed to determine how
user-friendly an interface is according to the results obtained from the tests.
3.6. Experiment Design
Several interfaces were chosen for evaluation to determine the most relevant aspects
for user-friendly interaction, affecting the workload of UPIs. The selection process was
carried out by analyzing different interaction processes and considering the physical
characteristics that correspond to traditional UPIs solutions; similar price ranges were also
considered. Thus, the same one was adapted to work with each UPI to avoid selecting
hardware bias to conduct the experiments. The different UPIs evaluated for this work are
3.6.1. Multimodal Approach Using Buttons and Myo Interface
Based on the work presented in [
], this interface operates either by receiving gestures
from the Myo armband or push buttons installed on the hand’s dorsal side to select a grip
from the graphical menu or to perform an action. The functionality of this UPI is shown in
the FSM in Figure 5. Both, the buttons,
, and the muscle contractions subset,
, corresponding to Thalmic Labs’ “Myo poses”, are used to operate
the prosthesis. By performing “wave out”,
, and “wave in”,
, hand extension and
ﬂexion respectively, as well as
, causes a forwards or backward switch of the
selected element in the menu displayed on the screen (shown in Figure 6); this process is
represented by the state
indicates that the ﬁngers on the prosthesis are fully
extended, in their default initial state; while in
, the hand is currently performing the
chosen grip. An important aspect to note is that, whilst in this state, changing the menu’s
selection is presented to the user, as the motor activation processes’ timing differs between
actions and could lead to wrong ﬁnger positioning if the case arose.
Sensors 2021,21, 7088 8 of 20
Finite State Machine showing the behavior of the interface using buttons and the Myo to
indicates that the hand is completely open;
, that there was a change in the selected
, that the selected grip is being performed (when it is completed, the ﬂag
is lifted). In
represents that the hand is currently enacting the chosen gesture; while,
, that the
ﬁngers are opening (process that informs it is ﬁnished by lifting the ﬂag f2).
Galileo Hand’s graphical menu (
) and the prosthesis performing the action “Close”
indicate that the prosthetic device is currently closing or
opening its ﬁngers, respectively. These procedures can be interrupted by each other if a
correct command is received. In addition to that, to execute an action
, “ﬁst” needs to
be performed by the user. At the same time, both “double tap” (two swift, consecutive
contractions) and “ﬁngers spread” are the contractions
that deactivate the action. It
was decided to use both gestures to deactivate the user’s selected actions according to the
results shown in Section 4. Finally, the last elements in the FSM representing the interface’s
behavior are the ﬂags
. The ﬁrst one is triggered when all the ﬁngers have reached
their desired position when performing an action, while the second triggers when all the
ﬁngers returned to their initial position, θ0.
3.6.2. Myo-Powered Interface with a Reduced Contractions Subset
This interface works similarly to the multimodal one explained in Section 3.6.1, i.e., se-
lecting the desired action in a menu and performing it with an “activation pose”. The main
difference is that the subset,
, is reduced to only two contractions. In this
way, it is imitating the iteration proposed in [
], by utilizing “wave in” to act and
Sensors 2021,21, 7088 9 of 20
“wave out” to select and deactivate a grip, illustrated in Figure 7. This simpliﬁed subset
provides a viable alternative if some of the Myo poses are unperformable by the patient.
Additionally, the buttons are absent for this UPI to help accommodate a reliable solution to
Finite State Machine representing the UPI interaction process from the version with the
reduced contraction subset.
indicates that the hand is completely open;
, that there was a change
in the selected grip;
, that the selected grip is being performed (when it is completed, the ﬂag
lifted). In addition,
represents that the hand is currently enacting the chosen gesture; while,
that the ﬁngers are opening.
3.6.3. Multimodal Approach Based on Object Classiﬁcation and Detection
This version uses a mobile application to control the prosthesis. The device possesses
a camera facing the palm, which takes pictures of the objects to be interacted with and
suggests a grasp. Alternatively, the photos can be taken with the mobile device’s photo-
graphic equipment. By performing Myo’s poses, the user can either accept, reject or cancel
the recommended grips provided by the app’s detection algorithm. This process uses a
bag of words computer vision algorithm to assign a label to the detected object with a grip.
This is a replica of the one used in .
The interface’s behavior is described as shown in Figure 8, where the set of contrac-
, represent the Myo poses which are used to choose along with
the states of the FSM: “ﬁst”, “ﬁngers spread”, “wave in” and “‘wave out”, respectively.
The interface’s behavior is described as shown in Figure 8, where the set of contractions,
, represent the Myo poses which are used to choose along with the states
of the FSM: “ﬁst”, “ﬁngers spread”, “wave in” and “‘wave out”, respectively. The state
denotes that the prosthetic device is in its rest position with all its ﬁnger entirely open.
Simultaneously, the UPI stays idle until the user performs the contraction
to trigger a
transition to the state
where the system takes a picture of the object with which he wants
to interact, and then is classiﬁed by the CNN algorithm running in a smartphone until a
is deﬁned. Thus, the label is validated when the classiﬁcation certainty reaches
a heuristic threshold that triggers the transition to the state
. If the CNN classiﬁcation
does not return a valid label, the system returns to the initial state
, upon a predeﬁned
. In the same state,
is performed, the transition indicates that another
photo needs to be taken, canceling the action selection process. The contraction
the algorithm’s suggestion while
rejects it, so the system proposes another grasp or
gesture. The text and animations of the suggested grip are provided as visual feedback via
the LCD screen, as shown in Figure 9.
Sensors 2021,21, 7088 10 of 20
Comportment of the UPI from the version based on object recognition.
indicates that the
prosthesis it is completely open;
, that a picture is being taken;
, that a label is being determined
(when this process is ﬁnished, the ﬂag
is lifted, if not, timeout
is raised); and
, that the action is
On the left, the visual feedback presented to the user on the Android app. Beside it is the
animation of the grip, which is shown to the user via the Galileo Hand’s LCD screen.
3.6.4. sEMG Pattern Recognition
Based on [
], this interface consists of a system that, utilizing Myos’s pattern recog-
nition methods, maps each of the predeﬁned “Myo poses” to a grip to be performed.
So, the prosthesis executes an action after receiving the interpreted contraction from
The layout is deﬁned as follows: “ﬁst” and “ﬁngers spread” to close and open all the
ﬁngers, respectively; “wave in” to a pointing position; “wave out” to carry out a lateral
grasp; and “double tap” to a hooking stance. The gestures were selected according to their
usability in ADLs, an aspect that was also taken into account when assigning the actions
concerning Myo’s success rate.
4. Results and Discussion
4.1. Myo Armband Efﬁciency
The myoelectric classiﬁer embedded in the Myo armband is not fault-free; some
contractions are misclassiﬁed at times, even for people without muscle damage. Therefore,
a confusion matrix was elaborated to corroborate the results shown in works such as [
Sensors 2021,21, 7088 11 of 20
and to verify its reliability in gathering the user intention. This analysis also served to select
which of the Myo armband-supported poses are the most adequate to be implemented as
default contractions to operate each interface. Therefore, depending on the amputation
level, the Myo will not correctly classify all contractions for limb-impaired subjects.
The data was obtained in two stages, one for the able-bodied subjects, done in [
and another for the unilateral limb-impaired ones (as depicted in Figure 10). The ﬁrst was
composed of 8 males and 2 females between the ages of 22 and 35, while the latter, by
2 male volunteers of 30 and 55 years old, as shown in Table 1.
Figure 10. A limb-impaired volunteer testing the UPI with the reduced contraction set of muscles.
Table 1. List of volunteers used in the experiment.
No. Limb-Impaired Prosthesis User? Age Gender
1 No No 25 M
2 No No 27 M
3 No No 24 M
4 No No 24 F
5 No No 23 F
6 No No 23 M
7 No No 23 M
8 No No 26 M
9 No No 22 M
10 No No 35 M
11 Yes Yes 55 M
12 Yes No 30 M
To avoid biased results, these volunteers had no experience whatsoever with the
Myo armband. Even though a more comprehensive range of ages may provide more
accuracy to a generalized population, the musculature differences tend to be minimal,
as the amputation damages it in a similar manner [
]. So, able-bodied subjects were asked
to perform every Myo pose in its default roster 50 times; while noting what the classiﬁer
detected each time. The resulting matrix is shown in Figure 11, where the default MYO
poses are numbered as follows: (1) “wave out”, (2) “wave in”, (3) “ﬁst”, (4) “double-tap”,
(5) indicates a no-operation (NOP) meaning the armband did not detect any pose and (6)
“ﬁngers spread”. According to the results gathered by this experiment, the Myo poses
were mapped to the operation actions in diverse manners to the different interfaces. These
results do not include the tests from the two impaired volunteers due to the poor accuracy
obtained with some contractions performed by non-disabled people (speciﬁcally, “ﬁngers
spread” and “double-tap”). This was also reﬂected in the constant misclassiﬁcation of
these contractions from the limb-impaired ones. Therefore, the data gathered from this
Sensors 2021,21, 7088 12 of 20
type of volunteers was merely regarding the interface that obtained the closest overall
performance compared to the UPI described in Section 3.6.4, according to the able-bodied
The total accuracy achieved by the default classiﬁer of the bracelet was about 87.7%.
As was expected, NOP was always classiﬁed correctly. On the other hand, as shown in
Figure 11, three gestures (“wave in”, “wave out” and “ﬁst”) reached acceptable perfor-
mance metrics in terms of accuracy (diagonal cells), precision (the column on the far right),
and recall (the row at the bottom). In this way, for the interface employing a multimodal
approach using the MYO bracelet in tandem with buttons, “wave in/out” were selected
to naturally choose between a set of predeﬁned gestures and “ﬁst” activates the selected
gesture on the prosthetic device. In this way, for the interface employing a multimodal
approach using the MYO bracelet in tandem with buttons, “wave in/out” were selected to
choose between a set of predeﬁned gestures naturally. At the same time, “ﬁst” activates the
selected gesture on the prosthetic device. The remaining gestures, the ones with the least
successful rates (“ﬁnger spread” and “double-tap”), were selected to return the prosthesis
to the rest position. Thus, the high error rate of these gestures cannot inﬂuence the UPI’s
performance since the user cannot select or change a gesture while the prosthesis is acting.
Figure 11. Confusion matrix evaluating the default classiﬁer of the Myo.
Moreover, the UPI that employs deep learning-based artiﬁcial vision algorithms was
replicated precisely from work proposed in [
]. Since this approach utilizes four muscle
contractions to operate the interface, the same (“wave in”, “wave out”, “ﬁst” and “ﬁnger
spread”) were elected to interact with prosthesis and the android mobile application.
Thus, the gesture with minor performance metrics was elected to deactivate the prosthesis,
returning the ﬁngers to the rest position. Finally, for the UPI based on sEMG pattern
recognition (Section 3.6.4), the contractions with the most signiﬁcant performance rates
were mapped to the most useful grips according to the user’s preferred ADLs. However,
it is considered a natural mapping that facilitates the operation of the prosthetic device,
where “ﬁst” activates the power grip action, “ﬁngers spread” open the prosthesis, “wave
in” could be used for customized grips and “double tap” for a least used gesture.
Regarding the UPI based on a reduced set of contractions (Section 3.6.2), the set of
contractions was selected, taking into account the contractions that ended with better
performance (accuracy, precision, and recall). In this way, “wave in” was selected to
Sensors 2021,21, 7088 13 of 20
activate predeﬁned grips and gestures, while “wave out” was chosen to select between
the different predeﬁned grips and gestures and also to return to the open position. Thus,
in this iteration, the system avoids using the actions with low success rates and replacing
them with the most accurate ones, ensuring better performance for the limb-impaired
ones, as well as increasing the functionality of the prosthetic device by performing only
these contractions. This alternative is possible since the menu is blocked during a gesture’s
performance, so both hand extension and ﬂexion are available to return the hand to its
4.2. NASA Task Load Index Evaluation
The ﬁrst evaluation process consisted of asking the non-disabled volunteers to rate
each of the UPIs mentioned above on each category on a scale divided into 20 intervals,
with a lower score indicating a better result. The test consisted of performing different
gestures and utilizing different grasps to interact with commonly encountered everyday
objects. The trials were held after providing the subjects a training period (a couple of
minutes) for them to be accustomed to the interfaces (as indicated by each participant); this
was to avoid any bias regarding the order in which the interfaces were tested, which was
done randomly, not unlike [
]. The actions asked to be performed by the volunteers were:
to hold a small plastic ball, a water bottle, and a wallet, as well as to press a speciﬁc key
on a laptop’s keyboard. These were selected for them among the most common grasps in
ADLs that correspond to Cutkosky grasp taxonomy [
]. The tasks, as mentioned earlier,
were repeated thrice so that the subjects could adequately adapt to each operational mode.
Additionally, the performances of these actions can be easily evaluated, as one can visualize
the output of the keyboard on a computer screen, and the grips should hold the objects
ﬁrmly. In addition, since the purpose of this study is to evaluate the workload of the user
interface only, the terminal device was not attached to the volunteers’ limb. In this way,
both the weight of the prosthesis and the objects do not directly inﬂuence the physical
demand evoked by each user-prosthesis interface.
This assessment was carried out with the same volunteers as the previous experiment.
Considering that not every workload class carries the same level of relevancy in the
prosthetic ﬁeld, these preliminary results may show bias or skewness if not appropriately
weighted. Thus, an overall performance statistic was determined Figure 13 which calculates
a weighted average of all categories for each interface, ranking them based on feedback
from the volunteers, opinions of expert engineers, and remarks from several patients,
in this order (from most important to least): Temporal Demand, Mental Demand, Physical
Demand, Performance, Effort & Frustration. Figure 12, shows the means and the standard
deviations for each of the considered categories. The results reﬂect a signiﬁcant discrepancy
between the UPI that uses deep learning-based computer vision algorithms and all other
UPIs, showing an inferior interface that presents a signiﬁcant workload in several categories.
Therefore, performing a Factorial Analysis of Variance (ANOVA) test on the results obtained
demonstrates a signiﬁcant difference in contrast to the UPI described in Section 3.6.2. In
addition, with a critical value of 3.84 and an alpha of 0.05, the F statistic obtained for this
test was about 132.4. This value discards the main effect hypothesis, showing a signiﬁcant
inequality between the evaluated interfaces.
Sensors 2021,21, 7088 14 of 20
Mean of the results gathered from the volunteers. Where (
) is the sEMG PR UPI; (
the one using the buttons and the Myo; (
) is the version using the camera; and (
) is the iteration
with reduced contractions subset.
The interface based on sEMG pattern recognition presents the best results in physical
and temporal demand categories and on the category that evaluates the user’s effort to
complete a task. Furthermore, the multimodal UPI that employs buttons in tandem with the
Myo bracelet resulted in the least frustrating interface for users. In contrast, the UPI based
on reduced contractions subset obtained better results than the others in the performance
and mental demand categories. All three interfaces proved to be proﬁcient in the different
categories; however, the results (as shown in Figure 12) do not show a signiﬁcant difference
to determine which of them has a better overall performance. These results showed that
all interfaces are straightforward iterations with an overall performance around the upper
70% according to the NASA TLX’s scale. The obtained means for the remaining UPIs
are still pretty similar. As shown in Figure 13, the UPI (a) has a mean of 5.75; (b) one of
6.2; and (d) 5.86, Therefore, more Factorial ANOVA analyses were performed on these
interfaces with the same alpha value. All previous tests were performed comparing the
reduced contractions subset version to the other interfaces to corroborate improvements or
signiﬁcant differences due to several participants’ interest in an alternative to a PR-based
UPI. Thus, these results show that the different aspects involved in the interaction process
do not affect the workload in a relevant matter.
The second evaluation process consisted of requesting the limb-impaired subjects to
perform the same ADLs from the preliminary testing by using the reduced contractions
subset UPI. This way, one can compare the performance concerning the other volunteers’
quantiﬁed results. This new score averaged 7.2 in the TLX scale (with 2.39 standard
deviation), as shown in Figure 14. Compared to the average value of able-bodied subjects
for the same interface (5.86), an ANOVA test was performed, and the results show no
signiﬁcant difference between groups. The F-statistic obtained was 0.78, a critical value of
3.98 with an alpha value of 0.05. Additionally, every volunteer sent a survey to determine
if the interfaces are considered user-friendly. The PR, the multimodal approach, and the
reduced contractions subset interfaces show an acceptable result, as around 70%, 80%, and
90% of participants perceived them to be user-friendly UPIs, respectively. On the other
hand, the only UPI-based that shows poor results was the one based on object classiﬁcation
and detection since only 30% of participants perceived it as user-friendly.
Sensors 2021,21, 7088 15 of 20
Overall performance of the different versions. (
) is the sEMG PR iteration; (
) is the one
with the buttons; (
) uses the computer vision algorithms; and (
) is the interface utilizing a reduced
Overall performance of the reduced contractions subset version. (
) is the score from the
able-bodied subjects; and (b) is the one with volunteers with upper-limb difference.
An effective interaction process between the user with the prosthesis is a very relevant
aspect that users consider when selecting an assistive device and thus continue to use
in their ADLs. Therefore, it is essential to identify the aspects favoring or opposing the
target users when designing a more efﬁcient and user-friendly interface. The results for
Sensors 2021,21, 7088 16 of 20
the interface described in Section 3.6.3 showed a trend strongly tied between the execution
time of the actions and their subjective evaluation, as evidenced by the poor reception
and the long operation time required to select and execute an action on the prosthetic
device. These strongly impact the process of interaction with the most common objects
that are part of its environment. This perception on users can be caused by the amount
of time it takes to select the object with which the user wants to interact and then take a
photo of it that must be processed to suggest the proper grip or gesture. Thus, this process
becomes complex and tedious for users, evoking frustration and demanding more effort to
achieve a particular goal. In addition to that, if the system employs the camera mounted
on a mobile device (s.a. smartphones or tablets), the user requires an able-bodied hand to
operate it with the app, needing particular physical prowess not possessed by certain kinds
of patients, speciﬁcally by bilateral amputees. If the system uses a camera mounted on the
prosthetic device, the weight and position of the camera can inﬂuence the effectiveness of
the UPI since it is crucial for the system to frame the object with which the user wants to
interact appropriately. Moreover, the object classiﬁcation and detection algorithms impose
another requirement to the system in terms of the processing device’s performance running
the interface’s software. This increases the price, either by the need for a smartphone
or an embedded system that is powerful enough to run the necessary machine learning
methods. Since these accommodations are not easily attainable in developing countries
due to the general shortage of high-speed internet, cellphone service, or even electricity,
these restrictions mainly affect amputees from regions suffering from poverty. In this
way, this iteration was the worst evaluated both in the survey and in the NASA-TLX test,
demonstrating that multimodal alternatives do not always improve the interaction between
the user and the assistive device, especially when the interaction process becomes very
complicated for the user.
Regarding the results showed in Figure 12, the superiority of the interface presented
in Section 3.6.4 lies in the swift selection of grips and gestures. This perception is due
to the lack of a menu with which it is necessary to select the desired action. Therefore,
the results obtained on the physical demand and the required effort categories are low.
In contrast, the results for frustration and mental demand for this iteration are caused by
the need to memorize which Myo contractions activate a predeﬁned action, resulting in a
slightly more complex process for patients. This is also frustrating for the limb-impaired
subjects since customized pattern recognition systems (requiring extended periods of
training) are needed to achieve low misclassiﬁcation rates, and still, only a limited number
of actions can be selected. However, these impressions show that no visual feedback is
necessary for a UPI to be user-friendly, leading to a simpler and more affordable alternative
as long as the user can still operate the prosthesis. For these reasons, this interface was
the third-best evaluated by the volunteers, despite the good results obtained from the
NASA-TLX test, which show that the workload is relatively low for this iteration. On the
other hand, this interface is the one that seems to interact with the device more naturally.
However, technological advances are still needed in biomedical signal processing and
pattern recognition to naturally interpret the user’s intention, especially using affordable
approaches available to amputees.
Furthermore, the results also show a lack of frustration for the UPI presented in
being the second-best evaluated by volunteers. This perception may result
from the sporadic inexactitude of the default Myo classiﬁcation process. This UPI provides
an alternative to navigate along with the menu by using buttons; therefore, an EMG
classiﬁer is not strictly necessary to select an action but to conﬁrm it, which provides a
satisfactory alternative in a multi-modal approach. This leads to the fact that a pattern
recognition system may not be necessary, which vastly reduces training time and the
complexity of the EMG sensor and the device gathering the user intent. This allows
for a simpler and less expensive solution for amputees, as only two sEMG channels in
combination with traditional digital signal processing techniques are required to detect
muscle activity from both ﬂexor and extensor sets of muscles . This is especially valid
Sensors 2021,21, 7088 17 of 20
considering that volunteers stated that they only need different grips to hold various types
of objects, not an extensive array of hand actions, meaning that the contractions to be
assessed do not need to be vast, allowing for a more straightforward and intuitive interface.
However, a UPI involving mechanical interaction (i.e., pressing buttons) is not a feasible
solution for bilateral amputees, as the interaction process does not favor them.
Furthermore, the results also show that mental exertion needed to operate the best
evaluated UPI described in Section 3.6.2 achieve the lowest score on the scale. This
perception from the volunteers may occur since the user does not need to memorize the
particular mapping that relates a contraction with a grip or gesture, nor do they need to
consider using the buttons installed on the top of the artiﬁcial limb. Since the subset of
contractions for this UPI is limited (only two contractions), the mental demand is also
reduced because the contractions were carefully selected to operate the device naturally.
Besides, the performance for this interface results in being the best along with all the
interfaces. This advantage may be due to the accuracy with which the Myo interprets
the pose used to return the prosthesis to its rest position compared to its multimodal
counterpart. The frustration level also scores low, particularly on unilateral amputees,
which may be due to their ability and experience to adapt their ADLs to employ one
healthy hand with the help of an assistive device. Thus, such patients do not need many
complex grasps, as they prefer to carry out the mechanically dexterous tasks with their
undamaged limbs. A typical example is opening a bottle, which may be easily done by
holding it ﬁrmly with the prosthesis and turning the cap with the other hand. Nevertheless,
bilateral amputees are not beneﬁted from such a reduced pool of alternatives. However,
another advantage of this version over the PR one, though not explicitly shown on the
overall scores, is that a broader range of actions might be provided without the need to
increase the number of contractions detected.
On the other hand, after conducting these trials, the multimodal approach using a
mechanical input (buttons) and the one based on reduced contractions set did not result in
a relevant improvement. The same conclusion can be drawn to the UPI that employs an
extended subset of contractions and a range of actions. These experiments demonstrate
that a simpler and more affordable UPI results in a similar interface to the user. However,
reducing the contractions subset to operate the device can restrict the operation mode to ﬁt
each amputee’s unique necessities, prompting the user to employ the prosthesis even if
they are unable or unwilling to complete certain Myo’s poses. In addition, these results
could vary due to the lack of evaluation by bilateral amputees in this study.
The results collected during this research give us a better idea of how different ap-
proaches used to interact with upper limb prostheses affect the user’s workload and
interface amiability. This can be used to ﬁnd alternatives to improve the price, performance,
reception, and adaptation of such assistive devices by reducing the workload required to
operate them and the interaction process’s complexity altogether. This leads to believe
that the UPI does not need to be a complex one, as shown by the results for the one using
the camera, but a simple, functional one, preferably using the smallest contraction subset
possible (to increase the range of users able to operate it). The time required to complete a
grasp was also shown to be an essential aspect when evaluating the interfaces, which is
unsurprising considering it may be compared to the response time of the healthy limb. Fi-
nally, even though there is a substantial difference between able-bodied and limb-impaired
subjects, this research work’s results do not show a signiﬁcant deviation, as the tests av-
eraged a similar score, and most discrepancy comes from variance within groups instead
of between groups. Therefore, the evaluation process using only healthy subjects beneﬁts
the user-friendly UPI design process. Thus, it can help the UPIs designer discard or favor
possible solutions before being tested by people suffering from upper limb amputation
according to the analysis of the evaluation results and then test only the best iterations. It is
best to test selected iterations for deeper analysis regarding an interface’s evoked workload
and amiability for this kind of volunteer. This way, we can provide better UPIs that will
improve the quality of life of those who need it.
Sensors 2021,21, 7088 18 of 20
Conceptualization, J.F., V.F., and E.R.; methodology, J.F., D.C., and G.M.;
software, J.F., D.C., G.M., and V.F.; validation, J.F., D.C., and G.M.; formal analysis, J.F., D.C., and G.M.;
investigation, J.F., D.C., and G.M.; resources, J.F., D.C., and G.M.; data curation, J.F., D.C., and G.M.;
writing—original draft preparation, J.F., D.C., and G.M.; writing—review and editing, J.F., D.C., G.M.,
V.F., and E.R.; visualization, D.C., and G.M.; supervision, J.F., V.F., and E.R.; project administration,
J.F. and E.R.; funding acquisition, E.R. All authors have read and agreed to the published version of
This work was supported in part by São Paulo Research Foundation (FAPESP) under
Grant 2013/07559-3, in part by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
(CAPES) under Finance Code 001, and in part by Conselho Nacional de Desenvolvimento Cientíﬁco
e Tecnológico (CNPq).
Institutional Review Board Statement:
The study and experiments were conducted following the
recommendations of the Brazilian Resolution 466/12 and its complementaries, and approved by the
National Research Ethics Commission (CONEP) under authorizations CAAE 37515520.1.0000.5404
and CAAE 17283319.7.0000.5404.
Informed Consent Statement:
Informed consent was obtained from all subjects involved in the
study, and written informed consent has been obtained from the patient(s) to publish this paper.
Data Availability Statement:
Datasets and original images are available from the corresponding
author on request.
Conﬂicts of Interest: The authors declare no conﬂict of interest.
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