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mSIMPAD: Efficient and Robust Mining of Successive Similar Patterns of Multiple Lengths in Time Series

Published:30 September 2020Publication History
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Abstract

A successive similar pattern (SSP) is a series of similar sequences that occur consecutively at non-regular intervals in time series. Mining SSPs could provide valuable information without a priori knowledge, which is crucial in many applications ranging from health monitoring to activity recognition. However, most existing work is computationally expensive, focuses only on periodic patterns occurring in regular time intervals, and is unable to recognize patterns containing multiple periods. Here we investigate a more general problem of finding similar patterns occurring successively, in which the similarity between patterns is measured by the z-normalized Euclidean distance. We propose a linear time, robust method, called Multiple-length Successive sIMilar PAtterns Detector (mSIMPAD), that mines SSPs of multiple lengths, making no assumptions regarding periodicity. We apply our method on the detection of repetitive movement using a wearable inertial measurement unit. The experiments were conducted on three public datasets, two of which contain simple walking and idle data, whereas the third is more complex and contains multiple activities. mSIMPAD achieved F-score improvements of 3.2% and 6.5%, respectively, over the simple and complex datasets compared to the state-of-the-art walking detector. In addition, mSIMPAD is scalable and applicable to real-time applications since it operates in linear time complexity.

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        • Published in

          cover image ACM Transactions on Computing for Healthcare
          ACM Transactions on Computing for Healthcare  Volume 1, Issue 4
          Special Issue on Wearable Technologies for Smart Health: Part 1
          October 2020
          184 pages
          ISSN:2691-1957
          EISSN:2637-8051
          DOI:10.1145/3427421
          Issue’s Table of Contents

          Copyright © 2020 ACM

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 30 September 2020
          • Online AM: 7 May 2020
          • Revised: 1 April 2020
          • Accepted: 1 April 2020
          • Received: 1 August 2019
          Published in health Volume 1, Issue 4

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