Age-sensitive high density surface electromyogram indices for detecting muscle fatigue using core shape modelling. (March 2023)

• Record Type: Journal Article
• Authors: Krishnan, Bharath; Zanelli, Serena; Boudaoud, Sofiane; Scapucciati, Léa; McPhee, John; Jiang, Ning
• Is Part Of: Biomedical signal processing and control. Volume 81(2023)
• Publication Date: 2023-03
• Subjects: High density Electromyography -- Aging -- Muscle Fatigue -- Core Shape Modelling, Gaussianity monitoring; 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.2022.104446
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: sEMG were acquired from both senior and young adults, during a standard fatiguing protocol. Three sEMG features were extracted by the Core Shape Modelling (CSM) methods. Two of these features, left shape distance (LSD) and right shape distance (RSD) were found to be age-sensitive, with different changes between the two groups during the fatiguing task. Abstract: The purpose of this preliminary study was to examine age-sensitive High Density surface Electromyogram (HD-sEMG) features by Core Shape Modelling (CSM) method. Fatiguing low force isometric contractions of the biceps brachii was performed by eight young (age, 24.40 ± 2.42 years) and five elderly (72.90 ± 2.21 years) males, while HD-sEMG recorded signals from the biceps brachii. The task was performed at 20 % maximal voluntary contraction (MVC). From the recorded HD-sEMG signals, three Probability Density Function (PDF) shape distances (SD) measures the departure from Gaussianity, i.e. Left (LSD), Right (RSD), and Central (CSD), were derived by the CSM method from non-overlapping five-second windows until task failure. A linear regression analysis was then used to quantify the change of these shape parameters throughout the contraction. The resultant slopes revealed that the elderly group showed a decreasing trend in PDF shape parameters as the contraction approached task failure. In contrast, the young showed an increasing trend. Statistical differences between the two groups were found for LSD ( p = 0.006) and RSD ( p = 0.001). No such age-sensitivity was detected using conventional sEMG fatigue features. These results suggest that the proposed CSM method can be used to obtain fatigue-related features from HD-sEMG that are age-sensitive and possibly related to different motor unit recruitment and synchronization schemes.