A coarse-to-fine method for subsequence matching of human behavior using multi-dimensional time-series approximation

In this paper, a novel method for subsequence matching of human behaviors is proposed. Since the human behavior data taken from many sensors monitoring human motions is a multi-dimensional time series, we extend an existing time-series approximation method, A-LTK (Approximation with use of Local features at Thinned-out Keypoints), to improve its performance as well as its accuracy in subsequence matching. Since A-LTK can change its approximation level using a parameter, the approach introduced in this paper uses two types of A-LTK levels, coarse followed by fine. The A-LTK-based Coarse-to-Fine subsequence matching method, called A-LTK 2.0, is discussed. We also evaluate the method, by comparing it with existing matching methods, DTW, AMSS, and the original A-LTK. The evaluations showed that A-LTK 2.0 is superior to the others in subsequence matching for long human-behavior sequences.

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