Learning Therapy Strategies from Demonstration Using Latent Dirichlet Allocation

Hee-Tae Jung, Richard G. Freedman, Tammie Foster, Yu-Kyong Choe, Shlomo Zilberstein, and Roderic A. Grupen. Learning Therapy Strategies from Demonstration Using Latent Dirichlet Allocation. Proceedings of the Twentieth ACM Conference on Intelligent User Interfaces (IUI), 432-436, Atlanta, Georgia, 2015.

Abstract

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 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 therapeutic exercise targets are generated and are verified with the same therapist who generated the data.

Bibtex entry:

@inproceedings{JFFCZGiui15,
  author	= {Hee-Tae Jung and Richard G. Freedman and Tammie Foster and
                   Yu-Kyong Choe and Shlomo Zilberstein and Roderic A. Grupen},
  title		= {Learning Therapy Strategies from Demonstration Using
                   Latent Dirichlet Allocation},
  booktitle     = {Proceedings of the Twentieth ACM Conference on
                   Intelligent User Interfaces},
  year		= {2015},
  pages		= {432-436},
  address       = {Atlanta, Georgia},
  url		= {http://rbr.cs.umass.edu/shlomo/papers/JFFCZGiui15.html}
}

shlomo@cs.umass.edu