Application of MML to Motor Skills Acquisition
Chao Sun, Fazel Naghdy, David A. Stirling · 2006
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.