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Application of MML to Motor Skills Acquisition

C. Sun, University of Wollongong
F. Naghdy, University of Wollongong
D. Stirling, University of Wollongong

Article comments

This article was originally published as: Sun, C, Naghdy, F & Stirling, D, Application of MML to Motor Skills Acquisition, International Conference on Computational Intelligence for Modelling, Control and Automation 2006 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, Sydney, November 2006, 77. Copyright IEEE 2006.

Abstract

Study on modeling human psychomotor behaviour based on tracked motion data is reported. The motion data is acquired through various integrated inertial sensors, and represented as Euler angles and accelerations. The Minimum Message Length (MML) algorithm is used to identify frames of intrinsic segmentations and to acquire a classification basis for unsupervised machine learning. The classification model can ultimately be deployed in recognizing certain skilled behaviors. The prior results are analyzed as FSMs¿ (Finite State Machines) to extract the potential rules underlying behaviors. The progress made so far and plan for further work is reported.

Suggested Citation

C. Sun, F. Naghdy, and D. Stirling. "Application of MML to Motor Skills Acquisition" Faculty of Informatics - Papers. Nov. 2006.
Available at: http://works.bepress.com/fnaghdy/2