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Review
. 2018 Sep 21:12:58.
doi: 10.3389/fnbot.2018.00058. eCollection 2018.

Causes of Performance Degradation in Non-invasive Electromyographic Pattern Recognition in Upper Limb Prostheses

Affiliations
Review

Causes of Performance Degradation in Non-invasive Electromyographic Pattern Recognition in Upper Limb Prostheses

Iris Kyranou et al. Front Neurorobot. .

Abstract

Surface Electromyography (EMG)-based pattern recognition methods have been investigated over the past years as a means of controlling upper limb prostheses. Despite the very good reported performance of myoelectric controlled prosthetic hands in lab conditions, real-time performance in everyday life conditions is not as robust and reliable, explaining the limited clinical use of pattern recognition control. The main reason behind the instability of myoelectric pattern recognition control is that EMG signals are non-stationary in real-life environments and present a lot of variability over time and across subjects, hence affecting the system's performance. This can be the result of one or many combined changes, such as muscle fatigue, electrode displacement, difference in arm posture, user adaptation on the device over time and inter-subject singularity. In this paper an extensive literature review is performed to present the causes of the drift of EMG signals, ways of detecting them and possible techniques to counteract for their effects in the application of upper limb prostheses. The suggested techniques are organized in a table that can be used to recognize possible problems in the clinical application of EMG-based pattern recognition methods for upper limb prosthesis applications and state-of-the-art methods to deal with such problems.

Keywords: EMG concept drift; EMG drifts; EMG variability between users; EMG variability with time; electromyography; upper limb prostheses applications.

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Figures

Figure 1
Figure 1
Graph representing algorithm in Luttmann et al. (2000). Increase of with shift of median frequency (MDF) to the higher frequencies corresponds to force increase whereas increase in amplitude and shift to the lower frequencies indicates muscle fatigue. Similarly a decrease of the with simultaneous shift to the lower frequencies of the median frequency indicates force decrease whereas shift to the higher frequencies recovery from fatigue.

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