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In: Proceedings of the 8th International Conference on Pervasive Computing Technologies for Healthcare. Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering (ICST), Brussels, Belgium, pp. 223 - 226, ACM

Mining crucial features for automatic rehabilitation coaching systems

Norimichi Ukita , Koki Eimon und Carsten Röcker,
May 2014

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.

Literatur Beschaffung: Proceedings of the 8th International Conference on Pervasive Computing Technologies for Healthcare. Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering (ICST), Brussels, Belgium, pp. 223 - 226, ACM
@book{199,
author= {Ukita, Norimichi and Eimon, Koki and Röcker, Carsten},
title= {Mining crucial features for automatic rehabilitation coaching systems},
publisher= {ACM},
year= {2014},
volume= {},
series= {},
address= {Brussels, Belgium},
edition= {},
month= {May},
note= {},
isbn= {},
}