A resistive force correlated electromyogram feature selection method for muscle strength prediction. (January 2023) - Saranya, S.; Poonguzhali, S.
Graphical abstract: Highlights: Muscle strength quantification using Electromyogram features. Feature selection in correlation with dynamometer force. Improved robustness by considering two different muscle groups. Performance of proposed feature set is better than Hudgin's Feature set. Alternative approach to Manual muscle test (MMT) grading. Abstract: The need for standardization of muscle strength measurement techniques has been of interest over many years. The relationship between Electromyography (EMG) and strength of isometric muscle contraction provides an alternative method of a more natural means of analyzing muscle strength. Quadriceps and Erector Spinae muscle groups are considered in this study. Muscle specific isometric protocol is followed where dynamometer resisted force and EMG are simultaneously acquired. A three level feature selection process based on the measured force and EMG is proposed. The first and second level selection is based on visible shape similarity and statistical Spearmans' correlation respectively between the resisted force and EMG features, followed by feature ranking using Laplacian Score. A very weak but statistically insignificant (p > 0.05) correlation was observed for SSI and Log features for both Quadriceps ('SSI' ρ = 0.04, 'LOG' ρ = 0.13) and Erector Spinae ('SSI' ρ = 0.08, 'LOG' ρ = 0.002) muscle groups. The proposed feature set including IEMG, RMS, WL, and FE consistently top ranked in Laplacian score for all the muscles. Fuzzy C-means clustering method is used to classify low, moderate and strong muscle strength levels. In comparison to the popular feature set proposed by Hudgin, the performance of the proposed feature set revealed a better cluster separation (SI score > 0.5, DB Index < 0.5, CH Index > 100). The choice of three of clusters for low, moderate and strong muscle strength using the proposed feature set has also resulted in a low Hartington's index (<3). The proposed feature set proves to be more robust in muscle strength classification using surface electromyogram signals.