Research Article
Mining Crucial Features for Automatic Rehabilitation Coaching Systems
@INPROCEEDINGS{10.4108/icst.pervasivehealth.2014.255133, author={Carsten R\o{}cker and Norimichi Ukita and Koki Eimon}, title={Mining Crucial Features for Automatic Rehabilitation Coaching Systems}, proceedings={4th International Workshop on User-Centered Design of Pervasive Healthcare Applications}, publisher={ICST}, proceedings_a={USER CENTERED DESIGN}, year={2014}, month={7}, keywords={motion coach rehabilitation pervasive health ambient assisted living}, doi={10.4108/icst.pervasivehealth.2014.255133} }- Carsten Röcker
Norimichi Ukita
Koki Eimon
Year: 2014
Mining Crucial Features for Automatic Rehabilitation Coaching Systems
USER CENTERED DESIGN
ICST
DOI: 10.4108/icst.pervasivehealth.2014.255133
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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