Deep Learning Enables Exoboot Control to Augment Variable-Speed Walking

Ankle exoskeletons have the potential to improve mobility, but common controllers are often inflexible to variations in tasks, such as changes in walking speed. To enable effective variable-speed exoboot control, we developed and validated a two-headed convolutional neural network trained to (1) classify stance versus swing and (2) predict the phase during stance, which was then mapped to a desired exoboot torque. This Machine Learning Estimator (MLE) was trained from nine participants walking at three speeds and four exoboot assistance levels. A Time-Based Estimator (TBE) that predicted gait phase from the two previous stride durations was used to apply realistic torques during MLE training and served as a within–participant control condition. The MLE was validated online with three new participants walking at a range of speeds and torques, both interpolating within and extrapolating outside the training set. Online validation accuracy (RMSE) across tested speeds and torque levels was 3.9%. On a simple walking task in which treadmill speed was varied sinusoidally between 1.1 and 1.6 m/s with a 30 s period, the three participants exhibited a mean 5.2% decrease in metabolic expenditure with the MLE compared to no-exo (boots only), but exhibited a 5.4% increase when walking with the TBE. The MLE more accurately predicted heel strike and toe off events (heel strike Mean Absolute Error: 9.6 ms; toe off MAE: 13.2 ms) than the TBE (heel strike MAE: 19.1 ms; toe off MAE: 34 ms). These positive results validated the potential of using a deep learning model for gait state estimation to effectively control an ankle exoskeleton across variable walking speeds.