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      Proof of Concept of an Online EMG-Based Decoding of Hand Postures and Individual Digit Forces for Prosthetic Hand Control

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          Abstract

          Introduction

          Options currently available to individuals with upper limb loss range from prosthetic hands that can perform many movements, but require more cognitive effort to control, to simpler terminal devices with limited functional abilities. We attempted to address this issue by designing a myoelectric control system to modulate prosthetic hand posture and digit force distribution.

          Methods

          We recorded surface electromyographic (EMG) signals from five forearm muscles in eight able-bodied subjects while they modulated hand posture and the flexion force distribution of individual fingers. We used a support vector machine (SVM) and a random forest regression (RFR) to map EMG signal features to hand posture and individual digit forces, respectively. After training, subjects performed grasping tasks and hand gestures while a computer program computed and displayed online feedback of all digit forces, in which digits were flexed, and the magnitude of contact forces. We also used a commercially available prosthetic hand, the i-Limb (Touch Bionics), to provide a practical demonstration of the proposed approach’s ability to control hand posture and finger forces.

          Results

          Subjects could control hand pose and force distribution across the fingers during online testing. Decoding success rates ranged from 60% (index finger pointing) to 83–99% for 2-digit grasp and resting state, respectively. Subjects could also modulate finger force distribution.

          Discussion

          This work provides a proof of concept for the application of SVM and RFR for online control of hand posture and finger force distribution, respectively. Our approach has potential applications for enabling in-hand manipulation with a prosthetic hand.

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          Most cited references15

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          Limb amputation and limb deficiency: epidemiology and recent trends in the United States.

          The purpose of this study was to provide a comprehensive perspective on the epidemiology and time trends in the incidence of limb amputations and limb deficiency in the United States. Data from the Healthcare Cost and Utilization Project from 1988 through 1996 were used to calculate rates of congenital deficiency, trauma-related, cancer-related, and dysvascular amputations in the United States. Trends over time in adjusted rates were then examined using linear regression techniques. Dysvascular amputations accounted for 82% of limb loss discharges and increased over the period studied. Over all years, the estimated increase in the rate of dysvascular amputations was 27%. Rates of trauma-related and cancer-related amputations both declined by approximately half. The incidence of congenital deficiencies remained stable. The risk of amputations increased with age for all causes and was highest among blacks having dysvascular amputations. Increasing risk of dysvascular amputations, particularly among elderly and minority populations, is of concern and warrants further investigation.
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            The optimal controller delay for myoelectric prostheses.

            A tradeoff exists when considering the delay created by multifunctional prosthesis controllers. Large controller delays maximize the amount of time available for EMG signal collection and analysis (and thus maximize classification accuracy); however, large delays also degrade prosthesis performance by decreasing the responsiveness of the prosthesis. To elucidate an "optimal controller delay" twenty able-bodied subjects performed the Box and Block Test using a device called PHABS (prosthetic hand for able bodied subjects). Tests were conducted with seven different levels of controller delay ranging from nearly 0-300 ms and with two different artificial hand speeds. Based on repeted measures ANOVA analysis and a linear mixed effects model, the optimal controller delay was found to range between approximately 100 ms for fast prehensors and 125 ms for slower prehensors. Furthermore, the linear mixed effects model shows that there is a linear degradation in performance with increasing delay.
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              Classification of finger movements for the dexterous hand prosthesis control with surface electromyography.

              A method for the classification of finger movements for dexterous control of prosthetic hands is proposed. Previous research was mainly devoted to identify hand movements as these actions generate strong electromyography (EMG) signals recorded from the forearm. In contrast, in this paper, we assess the use of multichannel surface electromyography (sEMG) to classify individual and combined finger movements for dexterous prosthetic control. sEMG channels were recorded from ten intact-limbed and six below-elbow amputee persons. Offline processing was used to evaluate the classification performance. The results show that high classification accuracies can be achieved with a processing chain consisting of time domain-autoregression feature extraction, orthogonal fuzzy neighborhood discriminant analysis for feature reduction, and linear discriminant analysis for classification. We show that finger and thumb movements can be decoded accurately with high accuracy with latencies as short as 200 ms. Thumb abduction was decoded successfully with high accuracy for six amputee persons for the first time. We also found that subsets of six EMG channels provide accuracy values similar to those computed with the full set of EMG channels (98% accuracy over ten intact-limbed subjects for the classification of 15 classes of different finger movements and 90% accuracy over six amputee persons for the classification of 12 classes of individual finger movements). These accuracy values are higher than previous studies, whereas we typically employed half the number of EMG channels per identified movement.
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                Author and article information

                Contributors
                URI : http://frontiersin.org/people/u/318300
                URI : http://frontiersin.org/people/u/124189
                URI : http://frontiersin.org/people/u/61500
                Journal
                Front Neurol
                Front Neurol
                Front. Neurol.
                Frontiers in Neurology
                Frontiers Media S.A.
                1664-2295
                01 February 2017
                2017
                : 8
                : 7
                Affiliations
                [1] 1School of Biological and Health Systems Engineering , Tempe, AZ, USA
                [2] 2School for Engineering of Matter, Transport, and Energy, Arizona State University , Tempe, AZ, USA
                Author notes

                Edited by: Marc Slutzky, Northwestern University, USA

                Reviewed by: Adenike Adewuyi, Northwestern University, USA; Matthew R. Williams, U.S. Department of Veterans Affairs, USA

                *Correspondence: Marco Santello, marco.santello@ 123456asu.edu

                Specialty section: This article was submitted to Neuroprosthetics, a section of the journal Frontiers in Neurology

                Article
                10.3389/fneur.2017.00007
                5285344
                c855a04a-4a0e-4e79-a9eb-c39d1743223f
                Copyright © 2017 Gailey, Artemiadis and Santello.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 30 July 2016
                : 06 January 2017
                Page count
                Figures: 5, Tables: 4, Equations: 9, References: 23, Pages: 15, Words: 12132
                Funding
                Funded by: Eunice Kennedy Shriver National Institute of Child Health and Human Development 10.13039/100009633
                Award ID: R21HD081938
                Funded by: Grainger Foundation 10.13039/100008074
                Categories
                Neuroscience
                Original Research

                Neurology
                myoelectric hand,neuroprosthesis,machine learning applied to neuroscience,neurorobotics,brain–machine interface

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