The Reality of Myoelectric Prostheses: Understanding What Makes These Devices Difficult for Some Users to Control

Abstract

Users of myoelectric prostheses can often find them difficult to control. This can lead to passive-use of the device or total rejection, which can have detrimental effects on the contralateral limb due to overuse. Current clinically available prostheses are “open loop” systems, and although considerable effort has been focused on developing biofeedback to “close the loop,” there is evidence from laboratory-based studies that other factors, notably improving predictability of response, may be as, if not more, important. Interestingly, despite a large volume of research aimed at improving myoelectric prostheses, it is not currently known which aspect of clinically available systems has the greatest impact on overall functionality and everyday usage. A protocol has, therefore, been designed to assess electromyographic (EMG) skill of the user and predictability of the prosthesis response as significant parts of the control chain, and to relate these to functionality and everyday usage. Here, we present the protocol and results from early pilot work. A set of experiments has been developed. First, to characterize user skill in generating the required level of EMG signal, as well as the speed with which users are able to make the decision to activate the appropriate muscles. Second, to measure unpredictability introduced at the skin–electrode interface, in order to understand the effects of the socket-mounted electrode fit under different loads on the variability of time taken for the prosthetic hand to respond. To evaluate prosthesis user functionality, four different outcome measures are assessed. Using a simple upper limb functional task prosthesis users are assessed for (1) success of task completion, (2) task duration, (3) quality of movement, and (4) gaze behavior. To evaluate everyday usage away from the clinic, the symmetricity of their real-world arm use is assessed using activity monitoring. These methods will later be used to assess a prosthesis user cohort to establish the relative contribution of each control factor to the individual measures of functionality and everyday usage (using multiple regression models). The results will support future researchers, designers, and clinicians in concentrating their efforts on the area that will have the greatest impact on improving prosthesis use.

Hybrid Brain–Computer Interface Techniques for Improved Classification Accuracy and Increased Number of Commands: A Review

“…These signals are recorded by EMG electrodes, which are most widely used in neuro-prostheses (Ravindra and Castellini, 2014; Chadwell et al, 2016; Chen et al, 2016). Table 1 summarizes possible combinations that can be used in the development of hBCI hardware.…”

A Review of Intent Detection, Arbitration, and Communication Aspects of Shared Control for Physical Human–Robot Interaction

“…Typically, this problem is resolved with pattern recognition techniques [43,44]. While classification results can be reasonably accurate, users have reported that the prosthesis is still difficult to control [45], and so this process remains an open avenue for research. Interestingly, while upper-limb prostheses generally try to leave full control to the user, they can also include some levels of autonomy, such as for lower level slip prevention tasks [11].…”

Desenvolvimento De Um Sistema Virtual Para Treinamento Muscular Por Miofeedback

“…As próteses mioelétricas funcionam de acordo com o sinal EMG do usuário, assim, a difculdade de controle do nível certo de ativação muscular ou a velocidade de resposta que o individuo possui são fatores fundamentais para a execução de diferentes tipos de tarefas [4].…”

“…Entre elas estão: determinar o tempo de contração muscular, a força produzida pelo músculo, o tempo que o músculo leva para fadigar e o comportamento geral das unidades motoras [2].Graças às inúmeras informações sobre a coleta do sinal EMG e aos diversos campos de pesquisa na área, é possível criar tecnologias assistivas de diferentes tipos [3]. Nesse contexto estão inseridas as próteses mioelétricas, como uma forma de possibilitar maior independência de movimentação a pessoas com algum tipo de deficiencia motora.As próteses mioelétricas funcionam de acordo com o sinal EMG do usuário, assim, a difculdade de controle do nível certo de ativação muscular ou a velocidade de resposta que o individuo possui são fatores fundamentais para a execução de diferentes tipos de tarefas [4].O miofeedback é uma das diferentes modalidades de biofeedback existentes, juntamente com o neurofeedback, termofeedback, cardiofeedback e o biofeedback GSR (Galvanic Skin Response). Essas modalidades são, respectivamente, referentes à atividade cortical, à temperatura corporal, à frequência cardíaca e à resposta galvânica da pele.…”