Open Access
2012 Movelets: A dictionary of movement
Jiawei Bai, Jeff Goldsmith, Brian Caffo, Thomas A. Glass, Ciprian M. Crainiceanu
Electron. J. Statist. 6: 559-578 (2012). DOI: 10.1214/12-EJS684


Recent technological advances provide researchers with a way of gathering real-time information on an individual’s movement through the use of wearable devices that record acceleration. In this paper, we propose a method for identifying activity types, like walking, standing, and resting, from acceleration data. Our approach decomposes movements into short components called “movelets”, and builds a reference for each activity type. Unknown activities are predicted by matching new movelets to the reference. We apply our method to data collected from a single, three-axis accelerometer and focus on activities of interest in studying physical function in elderly populations. An important technical advantage of our methods is that they allow identification of short activities, such as taking two or three steps and then stopping, as well as low frequency rare(compared with the whole time series) activities, such as sitting on a chair. Based on our results we provide simple and actionable recommendations for the design and implementation of large epidemiological studies that could collect accelerometry data for the purpose of predicting the time series of activities and connecting it to health outcomes.


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Jiawei Bai. Jeff Goldsmith. Brian Caffo. Thomas A. Glass. Ciprian M. Crainiceanu. "Movelets: A dictionary of movement." Electron. J. Statist. 6 559 - 578, 2012.


Published: 2012
First available in Project Euclid: 10 April 2012

zbMATH: 1274.68374
MathSciNet: MR2988420
Digital Object Identifier: 10.1214/12-EJS684

Primary: 62P10 , 68T10

Keywords: Accelerometer , Matching , physical activity , time series

Rights: Copyright © 2012 The Institute of Mathematical Statistics and the Bernoulli Society

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