Rapid energy expenditure estimation for ankle assisted and inclined loaded walking.

Affiliation

Department of Mechanical Engineering, Stanford University, Stanford, CA, USA. [Email]

Abstract

Estimating energy expenditure with indirect calorimetry requires expensive equipment and several minutes of data collection for each condition of interest. While several methods estimate energy expenditure using correlation to data from wearable sensors, such as heart rate monitors or accelerometers, their accuracy has not been evaluated for activity conditions or subjects not included in the correlation process. The goal of our study was to develop data-driven models to estimate energy expenditure at intervals of approximately one second and demonstrate their ability to predict energetic cost for new conditions and subjects. Model inputs were muscle activity and vertical ground reaction forces, which are measurable by wearable electromyography electrodes and pressure sensing insoles.

Keywords

Electromyography,Energy expenditure,Estimation,Gait,Ground reaction forces,Machine learning,

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