Date of Award
Master of Science (MS)
Ryan S. McGinnis
Digital medicine promises to improve healthcare and enable its delivery to rural and underserved communities. A key component of digital medicine is accurate and robust remote patient monitoring. For example, remote monitoring of biomechanical measures of limb impairment during daily life could allow near real-time tracking of rehabilitation progress and personalization of rehabilitation paradigms in those recovering from orthopedic surgery. Wearable sensors have long been suggested as a means for quantifying muscle and joint loading, which can provide a direct measure of limb impairment. However, current approaches either do not provide these measures or require unwieldy wearable sensor arrays and/or in-person calibration activities that limit their use. In this thesis, I advance the use of muscle synergy functions, which leverage the synergistic relationship within a group of muscles, to reduce the complexity of wearable sensor arrays and overcome the current need for an in-person visit to a human performance laboratory for calibration. Surface electromyography (EMG) and kinematic data were recorded from leg muscles and segments of nine healthy subjects during walking. Subject-general muscle synergy models were validated using the leave-one-subject-out method for 4 different pairs of input muscle model sets using filtered EMG data. The effect of adding kinematic data (angular velocity) from thigh and shank segment locations was investigated. The average correlation between true and estimated excitations was 96% higher when angular velocity data was included in the 4-muscle input model set. The estimated excitations informed muscle activations with 6.7% mean absolute error (MAE) and 43% variance accounted for (VAF) averaged across all muscles when kinematic data was included in the model, and 7.3% MAE and 43% VAF without kinematic data. These results lay the groundwork for developing muscle synergy functions that no longer require in-person calibration, paving the way for completely remote studies of muscle and joint loading.
Number of Pages
Donahue, Nicole Marie, "Developing Muscle Synergy Functions For Remote Gait Analysis" (2023). Graduate College Dissertations and Theses. 1656.