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Abstract Details
Activity Number:
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122
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Type:
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Contributed
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Date/Time:
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Monday, August 1, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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Abstract - #302204 |
Title:
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Accelerometer-Based Prediction of Activity for Epidemiological Research
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Author(s):
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Jiawei Bai*+ and Ciprian Crainiceanu and Brian Caffo and Thomas A. Glass
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Companies:
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The Johns Hopkins University and The Johns Hopkins University and The Johns Hopkins University and The Johns Hopkins University
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Address:
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615 N. Wolfe Street, Baltimore, MD, 21205,
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Keywords:
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Signal Processing ;
Accelerometer ;
Acceleration ;
Activity Prediction
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Abstract:
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We introduce statistical methods to quantify and classify daily activity using accelerometers. The methods are complimentary to the current standard based on the Activities of Daily Living (ADL) questionnaire, which suffers fundamental problems, such as memory and selection bias. In particular, we introduce models to predict several types of activity based accelerometers attached to the subject's waist. We show that simple means and standard deviations of acceleration at each second can be effectively used to distinguish between active and sedentary periods. Analysis of spectrum of the acceleration is used to locate walking time periods. Validation is provided to quantify the model accuracy of prediction.
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