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Physical activity recognition using multiple sensors embedded in a wearable device

Physical activity recognition using multiple sensors embedded in a wearable device Physical Activity Recognition using Multiple Sensors Embedded in a Wearable Device YUNYOUNG NAM, Ajou University, Suwon, South Korea SEUNGMIN RHO and CHULUNG LEE, Korea University In this article, we present a wearable intelligence device for activity monitoring applications. We developed and evaluated algorithms to recognize physical activities from data acquired using a 3-axis accelerometer with a single camera worn on a body. The recognition process is performed in two steps: at first the features for defining a human activity are measured by the 3-axis accelerometer sensor and the image sensor embedded in a wearable device. Then, the physical activity corresponding to the measured features is determined by applying the SVM classifier. The 3-axis accelerometer sensor computes the correlation between axes and the magnitude of the FFT for other features of an activity. Acceleration data is classified into nine activity labels. Through the image sensor, multiple optical flow vectors computed on each grid image patch are extracted as features for defining an activity. In the experiments, we showed that an overall accuracy rate of activity recognition based our method was 92.78%. Categories and Subject Descriptors: G.3 [Probability and Statistics]: reliability and life testing; J.3 [Life and Medical Sciences]: health http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Embedded Computing Systems (TECS) Association for Computing Machinery

Physical activity recognition using multiple sensors embedded in a wearable device

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References (26)

Publisher
Association for Computing Machinery
Copyright
Copyright © 2013 by ACM Inc.
ISSN
1539-9087
DOI
10.1145/2423636.2423644
Publisher site
See Article on Publisher Site

Abstract

Physical Activity Recognition using Multiple Sensors Embedded in a Wearable Device YUNYOUNG NAM, Ajou University, Suwon, South Korea SEUNGMIN RHO and CHULUNG LEE, Korea University In this article, we present a wearable intelligence device for activity monitoring applications. We developed and evaluated algorithms to recognize physical activities from data acquired using a 3-axis accelerometer with a single camera worn on a body. The recognition process is performed in two steps: at first the features for defining a human activity are measured by the 3-axis accelerometer sensor and the image sensor embedded in a wearable device. Then, the physical activity corresponding to the measured features is determined by applying the SVM classifier. The 3-axis accelerometer sensor computes the correlation between axes and the magnitude of the FFT for other features of an activity. Acceleration data is classified into nine activity labels. Through the image sensor, multiple optical flow vectors computed on each grid image patch are extracted as features for defining an activity. In the experiments, we showed that an overall accuracy rate of activity recognition based our method was 92.78%. Categories and Subject Descriptors: G.3 [Probability and Statistics]: reliability and life testing; J.3 [Life and Medical Sciences]: health

Journal

ACM Transactions on Embedded Computing Systems (TECS) – Association for Computing Machinery

Published: Feb 1, 2013

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