| Autor: |
Bryan Lao, Tomoya Tamei, Kazushi Ikeda |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
|
| Zdroj: |
Frontiers in Computer Science, Vol 2 (2020) |
| Druh dokumentu: |
article |
| ISSN: |
2624-9898 |
| DOI: |
10.3389/fcomp.2020.00003 |
| Popis: |
Physiotherapy is a labor-intensive process that has become increasingly inaccessible. Existing telehealth solutions overcome many of the logistical problems, but they are cumbersome to re-calibrate for the various exercises involved. To facilitate self-exercise efficiently, we developed a framework for personalized physiotherapy exercises. Our approach eliminates the need to re-calibrate for different exercises, using only few user-specific demonstrations available during collocated therapy. Two types of augmented feedback are available to the user for self-correction. The framework's utility was demonstrated for the sit-to-stand task, an important activity of daily living. Although further testing is necessary, our results suggest that the framework can be generalized to the learning of arbitrary motor behaviors. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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