A stochastic framework for movement strategy identification and analysis

The human body has many biomechanical degrees of freedom, and thus, multiple movement strategies can be employed to execute a given task. Joint loading patterns and risk of injury are highly sensitive to themovement strategy employed. This paper develops a computational framework to automatically identify and recognize different movement strategies to perform a task from human motion data. A divisive clustering approach is developed to identify movement strategies. Hidden Markov models (HMMs) are trained with the clustered observation sequences to generate strategy-specific models that are improved iteratively by using the maximum likelihood to relocate sequences to the most suitable cluster. Differences in individual joint trajectories are compared across strategies using a stochastic distance measure. The proposed algorithm is compared against three existing algorithms-joint contribution vector, decision tree, and HMM-based agglomerative clustering. Experimental results indicate that the proposed approach performs better than existing algorithms to detect motion strategies and automatically determine the differences between the strategies.