Mining crucial features for automatic rehabilitation coaching systems
Pages 223 - 226
Abstract
Our goal is to develop a system for coaching human motions (e.g. rehabilitation). Such a coaching system should have several function such as motion measurement, evaluation, and feedback. Among all, this paper focuses on how to modify a user's motion so that it gets closer to the good template of a target motion. To this end, it is important to efficiently advise the user to emulate the crucial features that define the good template. The proposed method automatically mines the crucial features of any kind of motions from a set of all motion features. The crucial features are mined based on feature sparsification through binary classification between the samples of good and other motions.
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Index Terms
- Mining crucial features for automatic rehabilitation coaching systems
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Published In
May 2014
459 pages
ISBN:9781631900112
- General Chair:
- Andreas Hein,
- Program Chairs:
- Susanne Boll,
- Friedrich Köhler
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ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering)
Brussels, Belgium
Publication History
Published: 20 May 2014
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PervasiveHealth '14
PervasiveHealth '14: 8th International Conference on Pervasive Computing Technologies for Healthcare
May 20 - 23, 2014
Oldenburg, Germany
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Overall Acceptance Rate 55 of 116 submissions, 47%
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