DocumentCode :
2375453
Title :
Unsupervised model generation for motion monitoring
Author :
Weber, Markus ; Bleser, Gabriele ; Hendeby, Gustaf ; Reiss, Attila ; Stricker, Didier
Author_Institution :
Augmented Vision, German Res. Center for Artificial Intell. (DFKI) GmbH, Kaiserslautern, Germany
fYear :
2011
fDate :
9-12 Oct. 2011
Firstpage :
51
Lastpage :
54
Abstract :
This paper addresses two fundamental requirements of full body motion monitoring: (a) the ability to sense the input of the user and (b) the means to interpret the captured input. Appropriate technology in both areas is required for an interactive virtual reality system to provide feedback in a useful and natural way. This paper combines technologies for both areas: It develops a sensor fusion approach for capturing user input based on miniature on-body inertial and magnetic motion sensors. Furthermore, it presents work in progress to automatically generate models for motion patterns from the captured input. The technology is then used and evaluated in the context of a personalized virtual rehabilitation trainer application.
Keywords :
medical computing; patient rehabilitation; sensor fusion; user interfaces; virtual reality; full body motion monitoring; magnetic motion sensor; on-body inertial sensor; personalized virtual rehabilitation trainer application; sensor fusion approach; unsupervised model generation; user input; virtual reality system; Biological system modeling; Hidden Markov models; Joints; Monitoring; Motion segmentation; Training; Viterbi algorithm;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Man, and Cybernetics (SMC), 2011 IEEE International Conference on
Conference_Location :
Anchorage, AK
ISSN :
1062-922X
Print_ISBN :
978-1-4577-0652-3
Type :
conf
DOI :
10.1109/ICSMC.2011.6083641
Filename :
6083641
Link To Document :
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