Investigating Aging-Related Changes in the Coordination of Agonist and Antagonist Muscles Using Fuzzy Entropy and Mutual Information

Abstract

Aging alters muscular coordination patterns. This study aimed to investigate aging-related changes in the coordination of agonist and antagonist muscles from two aspects, the activities of individual muscles and the inter-muscular coupling. Eighteen young subjects and 10 elderly subjects were recruited to modulate the agonist muscle activity to track a target during voluntary isometric elbow flexion and extension. Normalized muscle activation and fuzzy entropy (FuzzyEn) were applied to depict the activities of biceps and triceps. Mutual information (MI) was utilized to measure the inter-muscular coupling between biceps and triceps. The agonist activation decreased and the antagonist activation increased significantly during elbow flexion and extension with aging. FuzzyEn values of agonist electromyogram (EMG) were similar between the two age groups. FuzzyEn values of antagonist EMG increased significantly with aging during elbow extension. MI decreased significantly with aging during elbow extension. These results indicated increased antagonist co-activation and decreased inter-muscular coupling with aging during elbow extension, which might result from the reduced reciprocal inhibition and the recruitment of additional cortical-spinal pathways connected to biceps. Based on FuzzyEn and MI, this study provided a comprehensive understanding of the mechanisms underlying the aging-related changes in the coordination of agonist and antagonist muscles.

Adaptive Admittance Control for an Ankle Exoskeleton Using an EMG-Driven Musculoskeletal Model

“…All subjects were asked to be seated in a chair and place their feet on the footplate with a supporter fixing the right foot. To measure EMG signals, two pairs of circular electrodes (Ag-AgCl, 1-cm diameter) were attached to the skin surface at the centers of the muscle bellies of the TA and GAS with a center-to-center distance of 2 cm along the longitudinal axis (Sun et al, 2016 ). The EMG signals were sampled by the DAQ at a sample rate of 1,000 Hz and then band-pass filtered from 20 to 450 Hz, full-wave rectified, and low-pass filtered at 2 Hz (Canning et al, 2000 ).…”

Kinematic Analysis of Trajectory Dimension-Dependent Sensorimotor Control in Arm Tracking

“…If the boundary are too narrow, then the fApEn 3D would be heavily affected by noise, whereas a too wide boundary might lead to information loss. The fApEn 3D had the property of consistency when N > 300 and r ranged from 0.02 to 1 using physiological signals [3], [29]. Considering the selected data points for the 1D, 2D and 3D conditions in this study, n, r and N were consequently set to 2, 0.1 and 400, respectively, in this study.…”

“…fApEn analysis has been employed to investigate stroke-induced changes in sensorimotor control [34] and steady-state visual evoked potentials-based preictal alert to migraine patients [4]. Moreover, fApEn could provide insight into discriminating the aging-related changes in the coordination of agonist and antagonist muscles [29]. fApEn is most likely capable of providing insight into the variability of sensory inputs [3] and neuromotor noise [19], [34].…”

Characterization of the Stroke-Induced Changes in the Variability and Complexity of Handgrip Force

“…Jerk-based parameters are linear indices of the variability of force output. Nonlinear measures, such as information entropy [ 22 ], approximate entropy [ 13 , 23 , 24 ], sample entropy [ 25 , 26 ], and fuzzy approximate entropy (fApEn) [ 27 , 28 , 29 ] provide a different perspective of force modulation than regularity. Hong et al characterized the changed information entropy and ApEn of force output across different task settings to demonstrate modulated force during the procedure of motor adaptation [ 22 , 24 ].…”