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| Content Provider | ACM Digital Library |
|---|---|
| Author | Tsiakas, Konstantinos Makedon, Fillia Huber, Manfred |
| Abstract | Physical exercising is an essential part of any rehabilitation plan. The subject must be committed to a daily exercising routine, as well as to a frequent contact with the therapist. Rehabilitation plans can be quite expensive and time-consuming. On the other hand, tele-rehabilitation systems can be really helpful and efficient for both subjects and therapists. In this paper, we present ReAdapt, an adaptive module for a tele-rehabilitation system that takes into consideration the progress and performance of the exercising utilizing multisensing data and adjusts the session difficulty resulting to a personalized session. Multimodal data such as speech, facial expressions and body motion are being collected during the exercising and feed the system to decide on the exercise and session difficulty. We formulate the problem as a Markov Decision Process and apply a Reinforcement Learning algorithm to train and evaluate the system on simulated data. |
| Starting Page | 1 |
| Ending Page | 8 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781450334525 |
| DOI | 10.1145/2769493.2769507 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2015-07-01 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Multimodal adaptive systems Reinforcement learning Markov decision process Personalized rehabilitation systems |
| Content Type | Text |
| Resource Type | Article |
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