Learning Therapy Strategies from Demonstration Using Latent Dirichlet Allocation

The use of robots in stroke rehabilitation has become a pop-ular trend in rehabilitation robotics. However, despite the ac-knowledged value of customized service for individual pa-tients, research on programming adaptive therapy for indi-vidual 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 approxi-mate the demonstrations of the therapist. The resulting au-tonomous programs for therapy may imitate the strategy that the therapist might have employed and reinforce therapeutic exercises between teletherapy sessions. We propose to en-code the therapist’s decision criteria in terms of the patient’s 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 thera-peutic exercise targets are generated and are verified with the same therapist who generated the data

Upper-Limb Exercises for Stroke Patients through the Direct Engagement of an Embodied Agent

In this case study, we examine the functional utility of an embodied agent as an interactive medium in stroke rehab. A set of physical rehab exercises is conducted through the direct engagement of an embodied agent, the uBot-5. Based on the preliminary data, we argue that a general-purpose embodied agent has a potential to functionally complement human therapists in providing rehab to stroke patients. Categories and Subject Descriptor