Using Deep Learning Models and Wearable Sensors to Predict Prosthetic Ankle Torques

Abstract

Inverse dynamics from motion capture is the most common technique for analyzing human biomechanics. However, this method is time-intensive, limited to a gait laboratory setting, and requires a large array of reflective markers to be attached to the body. A practical alternative must be developed to provide biomechanical information to high-bandwidth prosthesis control systems to enable predictive controllers. In this study, we applied deep learning to build dynamical system models capable of accurately estimating and predicting prosthetic ankle torque from inverse dynamics using only six signals that can be accessed in real time using wearable sensors. We performed a hyperparameter optimization protocol that automatically selected the model architectures and learning parameters that resulted in the most accurate predictions. We show that the trained deep neural networks predict ankle torques one sample into the future with an average RMSE of 0.04±0.02 Nm/kg, corresponding to 2.9±1.6% of the ankle torque’s dynamic range. Comparatively, a manually-derived analytical regression model predicted ankle torques with a RMSE of 0.35±0.53 Nm/kg, corresponding to 26.6±40.9% of the ankle torque’s dynamic range. In addition, the deep neural networks predicted ankle torque values half a gait cycle into the future with an average drop in performance of 1.7% of the ankle torque’s dynamic range. This application of deep learning provides an avenue towards the development of predictive control systems for powered limbs aimed at optimizing prosthetic ankle torque.