Abstract
The constantly rising number of limb stroke survivors and amputees has motivated the development of intelligent prosthetic/rehabilitation devices for their arm function restoration. The device often integrates a pattern recognition (PR) algorithm that decodes amputees’ limb movement intent from electromyogram (EMG) signals, characterized by neural information and symmetric distribution. However, the control performance of the prostheses mostly rely on the interrelations among multiple dynamic factors of feature set, windowing parameters, and signal conditioning that have rarely been jointly investigated to date. This study systematically investigated the interaction effects of these dynamic factors on the performance of EMG-PR system towards constructing optimal parameters for accurately robust movement intent decoding in the context of prosthetic control. In this regard, the interaction effects of various features across window lengths (50 ms~300 ms), increments (50 ms~125 ms), robustness to external interferences and sensor channels (2 ch~6 ch), were examined using EMG signals obtained from twelve subjects through a symmetrical movement elicitation protocol. Compared to single features, multiple features consistently achieved minimum decoding error below 10% across optimal windowing parameters of 250 ms/100 ms. Also, the multiple features showed high robustness to additive noise with obvious trade-offs between accuracy and computation time. Consequently, our findings may provide proper insight for appropriate parameter selection in the context of robust PR-based control strategy for intelligent rehabilitation device.
Highlights
Individuals with limb amputation or congenital limb deficits or stroke often have difficulty in performing simple and complex daily life activities that involve the use of their upper extremity (UE).Symmetry 2020, 12, 1710; doi:10.3390/sym12101710 www.mdpi.com/journal/symmetryThey often depend on the healthy part of their body to compensate for a lost limb, which can greatly affect body posture and symmetry alignment
The properties of the different extracted feature sets were studied in terms of their classification error (CE) across combinations of window lengths and increments (Table 1) for movement intent decoding based on the linear discriminant analysis (LDA) algorithm
A detailed analysis of the experimental results obtained from this study revealed that multiple factors, including windowing parameters, choice of feature sets, and number of electrode channels factors, including windowing parameters, choice of feature sets, and number of electrode channels would influence the overall performance of myoelectric pattern recognition system that adopts linear would influence the overall performance of myoelectric pattern recognition system that adopts linear discriminant analysis classifier
Summary
They often depend on the healthy part of their body to compensate for a lost limb, which can greatly affect body posture and symmetry alignment. Such intelligent robotic system normally incorporate less computational control algorithms that operate on symmetric principle and attract low memory and processor requirement, aiding the realization of portable rehabilitation device that could be worn by amputees to help restore their arm functions [3,4,5,6] Such symmetrical principle play a significant role when it comes to the dynamics of controlling the prosthetic device during activities of daily living [7]. The pattern recognition strategy involves extraction of highly informative feature sets from the measured surface electromyogram (sEMG) data, which are applied to a machine learning model for limb movement intent decoding
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