Surface EMG model of the bicep during aging: a preliminary study

Age-related motor unit remodeling in the Tibialis Anterior

Limited studies exist on the use of surface electromyogram (EMG) signal features to detect age-related motor unit remodeling in the Tibialis Anterior. Motor unit remodeling leads to declined muscle strength and force steadiness during submaximal contractions which are factors for risk of falls in the elderly. This study investigated the remodeling phenomena in the Tibialis Anterior using sample entropy and higher order statistics. Eighteen young (26.1 ± 2.9 years) and twelve elderly (68.7 ± 9.0 years) participants performed isometric dorsiflexion of the ankle at 20% maximal voluntary contraction (MVC) and their Tibialis Anterior (TA) EMG was recorded. Sample entropy, Gaussianity and Linearity Test statistics were calculated from the recorded EMG for each MVC. Shapiro-Wilk test was used to determine normality, and either a two-tail student t-test or Wilcoxon rank sum test was performed to determine significant difference in the EMG features between the young and old cohorts. Results show age-related motor unit remodeling to be depicted by decreased sample entropy (p <; 0.1), increased non-Gaussianity (p <; 0.05) and lesser degree of linearity in the elderly. This is due to the increased sparsity of the MUAPs as a result of the denervation-reinnervation process, and the decrease in total number of motor units.

Identification of contaminant type in surface electromyography (EMG) signals

The ability to recognize various forms of contaminants in surface electromyography (EMG) signals and to ascertain the overall quality of such signals is important in many EMG-enabled rehabilitation systems. In this paper, new methods for the automatic identification of commonly occurring contaminant types in surface EMG signals are presented. Such methods are advantageous because the contaminant type is typically not known in advance. The presented approach uses support vector machines as the main classification system. Both simulated and real EMG signals are used to assess the performance of the methods. The contaminants considered include: 1) electrocardiogram interference; 2) motion artifact; 3) power line interference; 4) amplifier saturation; and 5) additive white Gaussian noise. Results show that the contaminants can readily be distinguished at lower signal to noise ratios, with a growing degree of confusion at higher signal to noise ratios, where their effects on signal quality are less significant.