@article {804,
	title = {Normalization and extraction of interpretable metrics from raw accelerometry data.},
	journal = {Biostatistics},
	volume = {15},
	year = {2014},
	month = {2014 Jan},
	pages = {102-16},
	abstract = {<p>We introduce an explicit set of metrics for human activity based on high-density acceleration recordings from a hip-worn tri-axial accelerometer. These metrics are based on two concepts: (i) Time Active, a measure of the length of time when activity is distinguishable from rest and (ii) AI, a measure of the relative amplitude of activity relative to rest. All measurements are normalized (have the same interpretation across subjects and days), easy to explain and implement, and reproducible across platforms and software implementations. Metrics were validated by visual inspection of results and quantitative in-lab replication studies, and by an association study with health outcomes.</p>},
	keywords = {Acceleration, Aged, Baltimore, Cohort Studies, Data Interpretation, Statistical, Female, Humans, Male, Motor Activity},
	issn = {1468-4357},
	doi = {10.1093/biostatistics/kxt029},
	author = {Bai, Jiawei and He, Bing and Shou, Haochang and Zipunnikov, Vadim and Glass, Thomas A and Crainiceanu, Ciprian M}
}
@article {813,
	title = {Movelets: A dictionary of movement.},
	journal = {Electron J Stat},
	volume = {6},
	year = {2012},
	month = {2012},
	pages = {559-578},
	abstract = {<p>Recent technological advances provide researchers with a way of gathering real-time information on an individual{\textquoteright}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.</p>},
	issn = {1935-7524},
	doi = {10.1214/12-EJS684},
	author = {Bai, Jiawei and Goldsmith, Jeff and Caffo, Brian and Glass, Thomas A and Crainiceanu, Ciprian M}
}
