Optimal Estimation of EMG Standard Deviation (EMG \sigma ) in Additive Measurement Noise: Model-Based Derivations and Their Implications

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 27; no. 12; pp. 2328 - 2335
• Main Authors: Wang, Haopeng, Rajotte, Kiriaki J., Wang, He, Dai, Chenyun, Zhu, Ziling, Bhuiyan, Moinuddin, Huang, Xinming, Clancy, Edward A.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Biological system modeling; biomedical signal processing; electromyogram; electromyogram (EMG) amplitude estimation; Electromyography; Laplace equations; Maximum likelihood estimates; Maximum likelihood estimation; Model testing; Muscles; myoelectric signal processing; Noise; Noise measurement; Noise standards; Orthoses; Processors; Program processors; Prostheses; Signal processing; Standard deviation; Subtraction

Typical electromyogram (EMG) processors estimate EMG signal standard deviation (EMGσ) via moving average root mean square (RMS) or mean absolute value (MAV) filters, whose outputs are used in force estimation, prosthesis/orthosis control, etc. In the inevitable presence of additive measurement noise...