An Improved Compound Gaussian Model for Bivariate Surface EMG Signals Related to Strength Training

• Published in: IEEE transactions on human-machine systems Vol. 55; no. 1; pp. 58 - 70
• Main Authors: Kusuru, Durgesh, Turlapaty, Anish C., Thakur, Mainak
• Format: Journal Article
• Language: English
• Published: IEEE 01.02.2025
• Subjects: Adaptation models; Compound Gaussian (CG) models; Electromyography; expectation maximization (EM) algorithm; Expectation-maximization algorithms; exponential random variable; Force; Gaussian processes; Muscles; Random variables; surface electromyography (sEMG); Training
• ISSN: 2168-2291; 2168-2305
• DOI: 10.1109/THMS.2024.3486450

Recent literature suggests that the surface electromyography (sEMG) signals have nonstationary statistical characteristics, specifically due to the random nature of the covariance. Thus, the suitability of a statistical model for sEMG signals is determined by the choice of an appropriate model for describing the covariance. The purpose of this study is to propose a compound-Gaussian (CG) model for multivariate sEMG signals in which the latent variable of covariance is modeled as a random variable that follows an exponential model. The parameters of the model are estimated using the iterative expectation maximization (EM) algorithm. Further, a new dataset, electromyography analysis of human activities database 2 (EMAHA-DB2), is developed. The proposed model is evaluated through both qualitative and quantitative methods. Based on the model fitting analysis on the sEMG signals from EMAHA-DB2, it is found that the proposed CG model fits more closely to the empirical pdf of sEMG signals than the existing models. In addition, statistical analyses are carried out among the models and estimated parameters under different scenarios. The estimate of the exponential model's rate parameter exhibits a clear relationship with training weights, potentially correlating with underlying motor unit activity. Finally, the average signal power estimates of the channels show distinctive dependency on the training weights, the subject's training experience, and the type of activity.