Presentation Title

Does gender influence interpretations of control complexity via muscle synergy analysis?

Project Collaborators

Reed Gurchiek (Graduate Student Mentor)

Abstract

The theory of muscle synergies describes a neural strategy used by the central nervous system (CNS) in which muscles are grouped into modules, simplifying a motor task. This theory has been proven by experimental and computational research using surface electromyography (sEMG) signals and algorithmic extraction of synergies. The number of synergies used by the CNS suggests its complexity and an individual’s motor capabilities. More synergies utilized indicates a more complex CNS. This research explores the influence of gender on muscle activation during gait by analyzing electromyography (EMG) signals from known measured muscles. Data was collected using wearable surface EMG sensors on 17 different muscles on the right lower limb. Raw sensor data collected in lab sessions was processed and muscle synergies were extracted using the NNMF function built-in to MATLAB. Extraction of 3-9 synergies was done for 10 males and 6 females. The variance accounted for (VAF) was generated for each number of synergies, comparing the feasibility of that muscle activation between genders. Further analysis suggests that gender does not significantly influence interpretation of motor control complexity via muscle synergy analysis. From a clinical perspective this eliminates the need to consider gender when assessing a patient's motor impairment.

Primary Faculty Mentor Name

Ryan McGinnis

Graduate Student Mentors

Reed Gurchiek

Status

Undergraduate

Student College

College of Engineering and Mathematical Sciences

Program/Major

Biomedical Engineering

Primary Research Category

Engineering & Physical Sciences

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Does gender influence interpretations of control complexity via muscle synergy analysis?

The theory of muscle synergies describes a neural strategy used by the central nervous system (CNS) in which muscles are grouped into modules, simplifying a motor task. This theory has been proven by experimental and computational research using surface electromyography (sEMG) signals and algorithmic extraction of synergies. The number of synergies used by the CNS suggests its complexity and an individual’s motor capabilities. More synergies utilized indicates a more complex CNS. This research explores the influence of gender on muscle activation during gait by analyzing electromyography (EMG) signals from known measured muscles. Data was collected using wearable surface EMG sensors on 17 different muscles on the right lower limb. Raw sensor data collected in lab sessions was processed and muscle synergies were extracted using the NNMF function built-in to MATLAB. Extraction of 3-9 synergies was done for 10 males and 6 females. The variance accounted for (VAF) was generated for each number of synergies, comparing the feasibility of that muscle activation between genders. Further analysis suggests that gender does not significantly influence interpretation of motor control complexity via muscle synergy analysis. From a clinical perspective this eliminates the need to consider gender when assessing a patient's motor impairment.