Electromyography as a surrogate for estimating metabolic energy expenditure during locomotion.

Minimizing metabolic energy expenditure (MEE) plays an important role in increasing mobility in people with locomotor disabilities, as movements that require high energy lead to less activity. Rehabilitation programs and devices use MEE to determine how effective they are, but using indirect calorimetry is limiting due to time delays and non-real-world conditions. Electromyography (EMG) offers insight into how muscles activate; thus, the purpose of this study was to develop a real-time MEE feedback system through the utilization of EMG signals. Participants completed five walking conditions at different stride frequencies (preferred, +/- 15%, +/- 30%), while breath-by-breath gas exchange, ground reaction forces and EMG signals were collected. The live EMG signal was numerically integrated and separated into strides, then scaled by a cost of force (COF) coefficient. MEE had the expected quadratic relationship seen in previous literature (R2 = 0.967), along with COF data (R2 = 0.701). The EMG method stabilized between 75.1% - 133.1%, which is not within a close range (90% - 110%) of MEE; thus, future studies must investigate other mathematical methods. Our results indicate a qualitative association between MEE and EMG activity, which could be used to increase mobility and quality of life for populations with disability.