Feasibility of Training a Random Forest Model With Incomplete User-Specific Data for Devising a Control Strategy for Active Biomimetic Ankle

Intelligent control strategies for active biomimetic prostheses could exploit the inter-joint coordination of limbs in human gait in order to mimic the functioning of a biological joint. A machine learning regression model could be employed to learn an input-output relationship between the coordinated limb motion in human gait and predict the motion of a particular limb/joint given the motion of other limbs/joints. Such a model could be potentially used as a controller for an intelligent prosthesis which aims to restore the functioning similar to an intact biological joint. For this, the model needs to be tailored for each user by learning the gait pattern specific to the user. The challenge of training such machine learning regression models in prosthetic control is that, the desired reference output cannot be obtained from an amputee due to the missing limb. In this study, we investigate the feasibility of using two different methods for training a random forest algorithm using incomplete amputee-specific data to predict the ankle kinematics and dynamics from hip, knee, and shank kinematics. First is an inter-subject approach which learns a generalized input-output relationship from a group of able-bodied individuals and then applies this generalized relationship to amputees. Second is a subject-specific approach which maps the amputee's inputs to a desired normative reference output calculated from able-bodied individuals. The subject-specific model outperformed the inter-subject model in predicting the ankle angle and moment in most cases and can be potentially used for devising a control strategy for an intelligent biomimetic ankle.