Keywords

Electromyography; Human Locomotion; Muscle Co-Contraction; Aging; Biofeedback; Human-Computer Interface

Abstract

Evaluating electrical muscle activity patterns using surface electromyography (EMG) is a widely used technique to assess movement in research settings, but its use has not been translated to clinical settings. This study aims to investigate the potential of evaluating muscle activity control through a visual matrix display. EMG data from older and younger adults collected in a previous seated locomotor perturbation study was used to generate visual matrices representing their lower-limb ankle muscle activity. Visual matrices from each condition consist of 20x20 grids, where each axis represents activity from an individual muscle of muscle pairs, such as the tibialis anterior and soleus. The color gradient in each square in the matrix represents the time the EMG was in that box such that darker colors indicate more time spent. We anticipated that older adults’ data will generate EMG visual matrices where the center is more filled during the perturbed stepping stage of the conditions compared to the unperturbed stepping stages, indicating higher simultaneous muscle activation strategies (i.e. co-contraction) in response to these perturbations. We also predicted that EMG visual matrices generated from younger adults’ data would be more filled along the axes instead compared to the older adults’ matrices. Our results, however, have been unable to capture a change in muscle activation behavior between unperturbed stepping and perturbed stepping stages, and a difference between young and older adult muscle activity has not yet been observed in these matrices. This may be due to the methods used during EMG processing or unknown variables in the tested dataset, which requires further investigation. Regardless, these EMG visual matrices could provide a more detailed and easily understandable assessment of muscle co-contraction compared to previous measures, allowing clinicians to develop more personalized rehabilitative training options for patients if applied in clinical settings.

Completion Date

2025

Semester

Spring

Committee Chair

Huang, Helen

Degree

Master of Science (M.S.)

College

College of Engineering and Computer Science

Department

Mechanical and Aerospace Engineering

Identifier

DP0029345

Document Type

Dissertation/Thesis

Campus Location

Orlando (Main) Campus

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