Richard G. Freedman

Learning Therapy Strategies from Demonstration Using Latent Dirichlet Allocation

The use of robots in stroke rehabilitation has become a popular trend in rehabilitation robotics. However, despite the acknowledged value of customized service for individual patients, research on programming adaptive therapy for individual patients has received little attention. The goal of the current study is to model teletherapy sessions in the form of a generative process for autonomous therapy that approximate the demonstrations of the therapist. The resulting autonomous programs for therapy may imitate the strategy that the therapist might have employed and reinforce therapeutic exercises between teletherapy sessions. We propose to encode the therapists decision criteria in terms of the patients motor performance features. Specifically, in this work, we apply Latent Dirichlet Allocation on the batch data collected during teletherapy sessions between a single stroke patient and a single therapist. Using the resulting models, the therapeutic exercise targets are generated and are verified with the same therapist who generated the data.

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.