Unsupervised Model Generation for Motion Monitoring

Unsupervised Model Generation for Motion Monitoring
Markus Weber, Gabriele Bleser, Gustaf Hendeby, Attila Reiss, Didier Stricker
IEEE SMC 2011 IEEE International Conference on Systems, Man, and Cybernetics (SMC-11), Workshop on Robust Machine Learning Techniques for Human Activity Recognition, located at IEEE International Conference on Systems, Man and Cybernetics, October 9-12

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.

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Unsupervised Model Generation for Motion Monitoring

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.

Back to the overview

Link 1