Abstract
BackgroundUnreliability of surface EMG recordings over time is a challenge for applying the EMG pattern recognition (PR)-controlled prostheses in clinical practice. Our previous study proposed a sensor fault-tolerant module (SFTM) by utilizing redundant information in multiple EMG signals. The SFTM consists of multiple sensor fault detectors and a self-recovery mechanism that can identify anomaly in EMG signals and remove the recordings of the disturbed signals from the input of the pattern classifier to recover the PR performance. While the proposed SFTM has shown great promise, the previous design is impractical. A practical SFTM has to be fast enough, lightweight, automatic, and robust under different conditions with or without disturbances.MethodsThis paper presented a real-time, practical SFTM towards robust EMG PR. A novel fast LDA retraining algorithm and a fully automatic sensor fault detector based on outlier detection were developed, which allowed the SFTM to promptly detect disturbances and recover the PR performance immediately. These components of SFTM were then integrated with the EMG PR module and tested on five able-bodied subjects and a transradial amputee in real-time for classifying multiple hand and wrist motions under different conditions with different disturbance types and levels.ResultsThe proposed fast LDA retraining algorithm significantly shortened the retraining time from nearly 1 s to less than 4 ms when tested on the embedded system prototype, which demonstrated the feasibility of a nearly “zero-delay” SFTM that is imperceptible to the users. The results of the real-time tests suggested that the SFTM was able to handle different types of disturbances investigated in this study and significantly improve the classification performance when one or multiple EMG signals were disturbed. In addition, the SFTM could also maintain the system’s classification performance when there was no disturbance.ConclusionsThis paper presented a real-time, lightweight, and automatic SFTM, which paved the way for reliable and robust EMG PR for prosthesis control.
Highlights
Unreliability of surface Electromyographic signal (EMG) recordings over time is a challenge for applying the EMG pattern recognition (PR)-controlled prostheses in clinical practice
The proposed fast linear discriminant analysis (LDA) retraining algorithm significantly shortened the retraining time from up to 1 s to less than 4 ms when tested on the embedded system prototype, which successfully addressed the most critical challenge in realizing the designed sensor fault-tolerant module (SFTM) in real-time
A fast LDA retraining algorithm and a fully automatic sensor fault detector based on outlier detection were developed, which allowed the SFTM to promptly detect various disturbances and recover the PR performance with nearly zero delay
Summary
Unreliability of surface EMG recordings over time is a challenge for applying the EMG pattern recognition (PR)-controlled prostheses in clinical practice. Our previous study proposed a sensor fault-tolerant module (SFTM) by utilizing redundant information in multiple EMG signals. The SFTM consists of multiple sensor fault detectors and a self-recovery mechanism that can identify anomaly in EMG signals and remove the recordings of the disturbed signals from the input of the pattern classifier to recover the PR performance. Unreliability of surface EMG recordings over time is a challenge for applying the EMG PR-controlled prostheses in clinical. In order to make EMG PR clinically viable for control of artificial limbs, several strategies have been developed to address the challenge of unreliability in EMG recordings. Amsuss et al [23] proposed a self-correcting EMG PRcontrolled system by adding a postprocessing algorithm to the existing EMG PR algorithm to detect and remove misclassifications of the system
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