IEEE Robotics & Automation Magazine - December 2017 - 103

affected by an upper-extremity amputation. In 2005, 41,000
individuals in the United States were living with the debilitating loss of an upper limb [2]. To remediate this issue, the
robotics community has put great focus on mechatronics
devices that may bring back more natural human hand
function [3]. During the past decade, multiple sophisticated
powered prosthetic hands have become available in the
market, yet the rejection rate for electric upper-limb prostheses is roughly 30% [1] due to dissatisfaction about issues
like cosmetics, comfort, function, ease of control, reliability,
and cost [4].
In spite of the availability of multi-DoF upper-limb
prostheses, the reliable and natural control of such robotic
hands is still an issue [5]. Conventionally, such systems
are unnaturally controlled in an on-off manner. This
unintuitive control has been one of the causes for prostheses abandonment [6]. Recently, natural prosthetic control
using pattern recognition technology has drawn a lot of
attention in academic research [7], [8]. Pattern-recognition-based control may potentially allow for the natural
control of upper-extremity prostheses by manipulating
multiple-DoF prosthetic hands.
In the past couple of years, commercial devices using pattern recognition for more intuitive control have also become
available, such as CoApt Engineering Complete Control, Chicago, Illinois (https://coaptengineering.com/). However, there
are still issues with pattern-recognition-based control when it
comes to clinical practice [3]. The currently available sensory
system for controlling robotic hands is based on sEMG [8],
which has specific downsides: sEMG's signals are affected by
interference from ambient noise, electrode shifts, sweat and
humidity, and signal crosstalk between adjacent electrodes [9].
These problems make sEMG a less suitable sensing technology
for prolonged use [9].
FMG has demonstrated a promising alternative to conventional sensing techniques [10]. FMG is based on pressure sensing and can potentially provide high accuracy in prediction,
stability over time, wearability, simplicity in socket embedding,
and affordability [10]. The feasibility of gesture recognition in
individuals with an upper-extremity amputation using FMG
has been demonstrated [9], and the performances of FMGand EMG-based systems have been compared in multiple
studies [7], [10], [11], with some showing that FMG can outperform EMG-based approaches [7], [11].
Although numerous studies have been conducted to
investigate various HMIs for the control of prosthetic
hands, most of them [7], [11], [12] have used healthy subjects due to their capability to produce distinguishable signals for various movements [13]. Much of the (limited)
research using subjects with an amputation has employed
static protocols [9], [13] that limit arm movement during
experiments, which cannot ensure the system's usability in
the dynamic movements used in daily life [3]. Yang et al.
[14] conducted a study employing both static and dynamic
protocols on ten subjects with amputations to control a
prosthetic hand using EMG. The dynamic protocol results

are reported in terms of drop probability and time to complete a task in a grasp/release experiment. The reported
results are in the range of a 0-21.4% probability of drop
and a 75-136-s task completion time. Yang et al. [3] also
conducted a study using three subjects with amputations
performing both static
and dynamic protocols to
control a hand prosthesis
Pattern-recognition-based
using EMG. Each subject
performed a different
control may potentially
number of motions (either
nine, five, or six). The reallow for the natural
ported results are in terms
of the receiver operating
control of upper-extremity
characteristic area ratio
and an outline value of
prostheses by manipulating
82.2% for the dynamic
protocol. To the best of
multiple-DoF prosthetic
our knowledge, no study
has been conducted to
hands.
evaluate the feasibility of
using FMG (with or without EMG) as an HMI for the control of upper-limb prostheses working with an amputee subject using a prosthetic
socket and a dynamic movement protocol.
This study is a preliminary assessment of the feasibility of
facilitating FMG as an alternative or synergist to EMG and
using pattern recognition in a device that can be adapted to
daily activities. A standard myoelectric socket was custom
made for the pilot, and an off-the-shelf robotic hand was used
to simulate a real-life scenario. To simulate activities of daily
living (ADL), we used a proven dynamic protocol as a reasonable simulation [11]. The presented results are based on the
offline processing of obtained data.
Materials and Methods
Subject
This pilot case study was conducted on a 59-year-old righthanded male subject with acquired transradial amputation of
the left arm due to a work accident in 1980. His current prosthetic device is a body-powered mechanical hook. The
subject used a myoelectric prosthesis for two years but abandoned the device due to multiple factors, such as a lack of
reliability in control, slow response, and unsuitability of an
electric prosthesis to his career and lifestyle.
Hardware
The main objective of this study was to investigate the feasibility of an FMG-based HMI in a near-to-real-case scenario.
To achieve this goal, all system components were designed to
maximize portability, compatibility with the current standards of prosthetics, and repeatability of use. All standalone
system components were embedded in a custom prosthetic
socket so that the system was comparable to a standard myoelectric prosthetic device in portability.
DECEMBER 2017

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IEEE ROBOTICS & AUTOMATION MAGAZINE

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https://www.coaptengineering.com/

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