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.
Highlights
Over the last years the design of prosthetic devices has evolved incorporating electrically actuated components in conjunction with the classic mechanical design
When it comes to long-term myoelectric pattern recognition systems a big challenge is that the EMG signal is non-stationary in nature and its statistical properties change over time
This paper focuses on the reasons that cause significant variability in the EMG signal excitation over time, resulting in the deterioration of myoelectric based classifier performance
Summary
Over the last years the design of prosthetic devices has evolved incorporating electrically actuated components in conjunction with the classic mechanical design. Commercial prostheses’ companies utilize myoelectric control by training the patients to trigger specific muscle signals that are used to access a grip This technique is very robust, but not intuitive and limited by the patient’s ability to remember and perform the trigger motions, potentially leading to the abandonment of the device (Biddiss and Chau, 2007). Many recent studies pointed out that there is no direct correlation between offline analysis performance improvement and online (real-time) performance (Jiang et al, 2014; Ortiz-Catalan et al, 2015; Vujaklija et al, 2017), emphasizing the need for online evaluation of such systems When it comes to long-term myoelectric pattern recognition systems a big challenge is that the EMG signal is non-stationary in nature and its statistical properties change over time. Such interferences that are specific to TMR procedure are not to be investigated in the following work
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