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Sensors 2009, 9(3), 1499-1517; doi:10.3390/s90301499

Detection of Activities by Wireless Sensors for Daily Life Surveillance: Eating and Drinking

Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117576
Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore 117576
Networking Protocols Department, Institute for Infocomm Research, 1 Fusionopolis Way, No. 21-01 Connexis, South Tower, Singapore 138632
Authors to whom correspondence should be addressed.
Received: 12 January 2009 / Accepted: 20 February 2009 / Published: 3 March 2009
(This article belongs to the Special Issue Wireless Sensor Technologies and Applications)
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This paper introduces a two-stage approach to the detection of people eating and/or drinking for the purposes of surveillance of daily life. With the sole use of wearable accelerometer sensor attached to somebody’s (man or a woman) wrists, this two-stage approach consists of feature extraction followed by classification. At the first stage, based on the limb’s three dimensional kinematics movement model and the Extended Kalman Filter (EKF), the realtime arm movement features described by Euler angles are extracted from the raw accelerometer measurement data. In the latter stage, the Hierarchical Temporal Memory (HTM) network is adopted to classify the extracted features of the eating/drinking activities based on the space and time varying property of the features, by making use of the powerful modelling capability of HTM network on dynamic signals which is varying with both space and time. The proposed approach is tested through the real eating and drinking activities using the three dimensional accelerometers. Experimental results show that the EKF and HTM based two-stage approach can perform the activity detection successfully with very high accuracy. View Full-Text
Keywords: Wireless Sensor; HTM; Feature Extraction; Eating and Drinking; Euler Angle Wireless Sensor; HTM; Feature Extraction; Eating and Drinking; Euler Angle

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Zhang, S.; Ang, M.H., Jr.; Xiao, W.; Tham, C.K. Detection of Activities by Wireless Sensors for Daily Life Surveillance: Eating and Drinking. Sensors 2009, 9, 1499-1517.

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