Detection of Activities by Wireless Sensors for Daily Life Surveillance: Eating and Drinking
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 follo...
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MDPI AG
2009-03-01
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Online Access: | http://www.mdpi.com/1424-8220/9/3/1499/ |
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doaj-c40ecbd7d52b40e782c328b5466ae6e42020-11-25T01:54:33ZengMDPI AGSensors1424-82202009-03-01931499151710.3390/s90301499Detection of Activities by Wireless Sensors for Daily Life Surveillance: Eating and DrinkingChen Khong ThamWendong XiaoSen ZhangMarcelo H. AngThis 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. http://www.mdpi.com/1424-8220/9/3/1499/Wireless SensorHTMFeature ExtractionEating and DrinkingEuler Angle |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Chen Khong Tham Wendong Xiao Sen Zhang Marcelo H. Ang |
spellingShingle |
Chen Khong Tham Wendong Xiao Sen Zhang Marcelo H. Ang Detection of Activities by Wireless Sensors for Daily Life Surveillance: Eating and Drinking Sensors Wireless Sensor HTM Feature Extraction Eating and Drinking Euler Angle |
author_facet |
Chen Khong Tham Wendong Xiao Sen Zhang Marcelo H. Ang |
author_sort |
Chen Khong Tham |
title |
Detection of Activities by Wireless Sensors for Daily Life Surveillance: Eating and Drinking |
title_short |
Detection of Activities by Wireless Sensors for Daily Life Surveillance: Eating and Drinking |
title_full |
Detection of Activities by Wireless Sensors for Daily Life Surveillance: Eating and Drinking |
title_fullStr |
Detection of Activities by Wireless Sensors for Daily Life Surveillance: Eating and Drinking |
title_full_unstemmed |
Detection of Activities by Wireless Sensors for Daily Life Surveillance: Eating and Drinking |
title_sort |
detection of activities by wireless sensors for daily life surveillance: eating and drinking |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2009-03-01 |
description |
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. |
topic |
Wireless Sensor HTM Feature Extraction Eating and Drinking Euler Angle |
url |
http://www.mdpi.com/1424-8220/9/3/1499/ |
work_keys_str_mv |
AT chenkhongtham detectionofactivitiesbywirelesssensorsfordailylifesurveillanceeatinganddrinking AT wendongxiao detectionofactivitiesbywirelesssensorsfordailylifesurveillanceeatinganddrinking AT senzhang detectionofactivitiesbywirelesssensorsfordailylifesurveillanceeatinganddrinking AT marcelohang detectionofactivitiesbywirelesssensorsfordailylifesurveillanceeatinganddrinking |
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