Techniques are provided for using machine learning methods to predict user preferences with respect to robotic assistive prostheses and/or custom tune the robotic assistive prostheses for individual users based on the predicted user preferences. A training biomechanical dataset, including historical biomechanical sensor data for a robotic assistive prosthetic device operating using a plurality of tuning settings at a plurality of speeds, and a training user preference dataset including historical user tuning preference data for the robotic assistive prosthetic device for each respective tuning setting and speed, are generated. A machine learning model is trained using the training biomechanical dataset and the training user preference dataset. The trained machine learning model is applied to new biomechanical sensor data associated with a particular user to predict the user's tuning preferences for the robotic assistive prosthetic device, and the settings of the device are automatically modified based on the predicted tuning preferences.
PREDICTING USER PREFERENCE WITH AI TO CONTROL A NEW GENERATION OF WEARABLE ASSISTIVE TECHNOLOGIES