Wearable sensor and machine learning estimate tendon load and walking speed during immobilizing boot ambulation.

The purpose of this study is to develop a wearable paradigm to accurately monitor Achilles tendon loading and walking speed using wearable sensors that reduce subject burden. Ten healthy adults walked in an immobilizing boot under various heel wedge conditions (30°, 5°, 0°) and walking speeds. Three-dimensional motion capture, ground reaction force, and 6-axis inertial measurement unit (IMU) signals were collected. We used a Least Absolute Shrinkage and Selection Operator (LASSO) regression to predict peak Achilles tendon load and walking speed. The effects of altering sensor parameters were also explored. Walking speed models (mean absolute percentage error (MAPE): 8.81 ± 4.29%) outperformed tendon load models (MAPE: 34.93 ± 26.3%). Models trained with subject-specific data performed better than models trained without subject-specific data. Removing the gyroscope, decreasing the sampling frequency, and using combinations of sensors did not change the usability of the models, having inconsequential effects on model performance. We developed a simple monitoring paradigm that uses LASSO regression and wearable sensors to accurately predict (MAPE ≤ 12.6%) Achilles tendon loading and walking speed while ambulating in an immobilizing boot. This paradigm provides a clinically implementable strategy to longitudinally monitor patient loading and activity while recovering from Achilles tendon injuries.