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

Published: 11 December 2015 Publication History

Abstract

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

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        iiWAS '15: Proceedings of the 17th International Conference on Information Integration and Web-based Applications & Services
        December 2015
        704 pages
        ISBN:9781450334914
        DOI:10.1145/2837185
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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        Published: 11 December 2015

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