Comparison of Particle Filter to Established Filtering Methods in Electromyography Biofeedback. (July 2020)

• Record Type: Journal Article
• Authors: Lyu, Mingxing; Lambelet, Charles; Woolley, Daniel; Zhang, Xue; Chen, Weihai; Ding, Xilun; Gassert, Roger; Wenderoth, Nicole
• Is Part Of: Biomedical signal processing and control. Volume 60(2020)
• Publication Date: 2020-07
• Subjects: Adaptive -- Bayes filter -- Biofeedback -- Electromyography (EMG) -- Kalman filter -- Moving average windowing -- Particle filter -- Surface electromyography (sEMG); Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2020.101949
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: Objective: Surface electromyography (sEMG) is a potentially useful signal that can provide therapeutic biofeedback. However, sEMG signal processing is difficult because of the low signal-to-noise ratio and non-stationarity of the raw signal. Conventional online filters often suffer from a compromise between smoothness and responsiveness. Here we propose a new particle filtering method for sEMG processing and compare it to established filtering methods. Methods: A wrist apparatus measuring isometric wrist extension/flexion force was developed. Six filters (moving average windowing (MAW), adaptive-MAW, 3-layer, Kalman, Bayes and particle filters) were tested on forearm sEMG collected with a Myo armband. Fourteen subjects performed two visuomotor tracking tasks (square and sine wave tracking). Tracking error, measured as the root mean square error (RMSE2), was used as a metric to compare the influence of different filters on overall performance. Results: For sine wave tracking tasks (representing continuous trajectory control), the particle filter (RMSE2: 53.30 ± 15.69 pixels) had the lowest tracking error. For the square wave tracking task (representing discrete endpoint control), the Bayes filter (RMSE2: 37.82 ± 23.53 pixels) had the lowest tracking error. With respect to computational requirements, the Kalman filter was the most efficient. Conclusion: Our results indicate that the filter requirements for sEMG controllers are task specific, but the new particle filtering method presented here represents a good compromise for the different types of motor control tested here. Significance: The particle filter has the potential to improve sEMG based therapeutic biofeedback.