Using Gait Dynamics to Estimate Load from a Body-Worn Accelerometer
MIT Lincoln Laboratory Lexington United States
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Heavy loads increase the risk of musculoskeletal injury for foot soldiers and first responders. Continuous monitoring of load carriage in the field has proven difficult. We propose an algorithm for estimating load from a single body-worn accelerometer. The algorithm utilizes three different methods for characterizing torso movement dynamics, and maps the extracted dynamics features to load estimates using two machine learning multivariate regression techniques. The algorithm is applied to two field collections of soldiers and civilians walking with varying loads. The effect of load on features is analyzed and the feature utility in conjunction with two regression techniques is assessed. Load estimation is done using a fixed set of machine learning parameters and leave-one-subject-out cross-validation. Accurate load estimates are obtained, demonstrating robustness to changes in equipment configuration, walking conditions, and walking speeds. On soldier data with loads ranging from 45 to 89 lbs, load estimates result in mean absolute error MAE of 6.64 lbs and correlation of r 0.81. On combined soldier and civilian data, with loads ranging from 0 to 89 lbs, results are MAE 9.57 lbs and r 0.91.
- Anatomy and Physiology
- Military Forces and Organizations