NDLI: Enabling Efficient Time Series Analysis for Wearable Activity Data

Content Provider	IEEE Xplore Digital Library
Author	Van Laerhoven, K. Berlin, E. Schiele, B.
Copyright Year	2009
Abstract	Long-term activity recognition relies on wearable sensors that log the physical actions of the wearer, so that these can be analyzed afterwards. Recent progress in this field has made it feasible to log high-resolution inertial data, resulting in increasingly large data sets. We propose the use of piecewise linear approximation techniques to facilitate this analysis. This paper presents a modified version of SWAB to approximate human inertial data as efficiently as possible, together with a matching algorithm to query for similar subsequences in large activity logs. We show that our proposed algorithms are faster on human acceleration streams than the traditional ones while being comparable in accuracy to spot similar actions, benefitting post-analysis of human activity data.
Starting Page	392
Ending Page	397
File Size	1470427
Page Count	6
File Format	PDF
ISBN	9780769539263
DOI	10.1109/ICMLA.2009.112
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-13
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis dynamic time warping time series approximation Time series analysis Piecewise linear approximation Humans Application software wearable sensing Computer science Prototypes Hidden Markov models Machine learning Wearable sensors activity recognition
Content Type	Text
Resource Type	Article

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