Simplified Optimal Estimation of Time-Varying Electromyogram Standard Deviation (EMGσ): Evaluation on Two Datasets

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 15; p. 5165
• Main Authors: Wang, He, Rajotte, Kiriaki J., Wang, Haopeng, Dai, Chenyun, Zhu, Ziling, Huang, Xinming, Clancy, Edward A.
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 30.07.2021; MDPI
• Subjects: advanced signal processing; Algorithms; Bandwidths; biological system modeling; biomedical signal processing; Datasets; Elbow; Elbow Joint; Electrodes; electromyogram; electromyogram (EMG) amplitude estimation; Electromyography; Humans; Isometric Contraction; Muscle Contraction; Muscle, Skeletal; Noise; Posture; Prostheses; Spectrum allocation; Standard deviation; biomedical signal processing; biological system modeling; advanced signal processing; electromyogram (EMG) amplitude estimation; electromyography; electromyogram
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21155165

To facilitate the broader use of EMG signal whitening, we studied four whitening procedures of various complexities, as well as the roles of sampling rate and noise correction. We separately analyzed force-varying and constant-force contractions from 64 subjects who completed constant-posture tasks about the elbow over a range of forces from 0% to 50% maximum voluntary contraction (MVC). From the constant-force tasks, we found that noise correction via the root difference of squares (RDS) method consistently reduced EMG recording noise, often by a factor of 5–10. All other primary results were from the force-varying contractions. Sampling at 4096 Hz provided small and statistically significant improvements over sampling at 2048 Hz (~3%), which, in turn, provided small improvements over sampling at 1024 Hz (~4%). In comparing equivalent processing variants at a sampling rate of 4096 Hz, whitening filters calibrated to the EMG spectrum of each subject generally performed best (4.74% MVC EMG-force error), followed by one universal whitening filter for all subjects (4.83% MVC error), followed by a high-pass filter whitening method (4.89% MVC error) and then a first difference whitening filter (4.91% MVC error)—but none of these statistically differed. Each did significantly improve from EMG-force error without whitening (5.55% MVC). The first difference is an excellent whitening option over this range of contraction forces since no calibration or algorithm decisions are required.