Adaptive therapy strategies: Efficacy and learning framework

This paper considers a data-driven framework to model target selection strategies using runtime kinematic parameters of individual patients. These models can be used to select new exercise targets that conform with the decision criteria of the therapist. We present the results from a single-subject case study with a manually written target selection function. Motivated by promising results, we propose a framework to learning customized/adaptive therapy models for individual patients. Through the data collected from a normally functioning adult, we demonstrate that it is feasible to model varying strategies from the demonstration of target selection.