Sensor-Based Abnormal Human-Activity Detection

With the availability of affordable sensors and sensor networks, sensor-based human activity recognition has attracted much attention in artificial intelligence and ubiquitous computing. In this paper, we present a novel two-phase approach for detecting abnormal activities based on wireless sensors...

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Published in:	IEEE transactions on knowledge and data engineering Vol. 20; no. 8; pp. 1082 - 1090
Main Authors:	Jie Yin, Qiang Yang, Pan, J.J.
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.08.2008 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Activity Recognition Artificial intelligence Communication system security Data Mining Data security Human motion body Humans Intelligent sensors Mathematical models Monitoring Networks Outlier Detection Regression Sensor Networks Sensors Studies Support vector machines Training Training data Ubiquitous computing Wireless sensor networks Activity Recognition Data Mining Outlier Detection Sensor Networks
ISSN:	1041-4347, 1558-2191
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Abstract	With the availability of affordable sensors and sensor networks, sensor-based human activity recognition has attracted much attention in artificial intelligence and ubiquitous computing. In this paper, we present a novel two-phase approach for detecting abnormal activities based on wireless sensors attached to a human body. Detecting abnormal activities is a particular important task in security monitoring and healthcare applications of sensor networks, among many others. Traditional approaches to this problem suffer from a high false positive rate, particularly when the collected sensor data are biased towards normal data while the abnormal events are rare. Therefore, there is a lack of training data for many traditional data mining methods to be applied. To solve this problem, our approach first employs a one-class support vector machine (SVM) that is trained on commonly available normal activities, which filters out the activities that have a very high probability of being normal. We then derive abnormal activity models from a general normal model via a kernel nonlinear regression (KNLR) to reduce false positive rate in an unsupervised manner. We show that our approach provides a good tradeoff between abnormality detection rate and false alarm rate, and allows abnormal activity models to be automatically derived without the need to explicitly label the abnormal training data, which are scarce. We demonstrate the effectiveness of our approach using real data collected from a sensor network that is deployed in a realistic setting.
AbstractList	With the availability of affordable sensors and sensor networks, sensor-based human activity recognition has attracted much attention in artificial intelligence and ubiquitous computing. In this paper, we present a novel two-phase approach for detecting abnormal activities based on wireless sensors attached to a human body. Detecting abnormal activities is a particular important task in security monitoring and healthcare applications of sensor networks, among many others. Traditional approaches to this problem suffer from a high false positive rate, particularly when the collected sensor data are biased towards normal data while the abnormal events are rare. Therefore, there is a lack of training data for many traditional data mining methods to be applied. To solve this problem, our approach first employs a one-class support vector machine (SVM) that is trained on commonly available normal activities, which filters out the activities that have a very high probability of being normal. We then derive abnormal activity models from a general normal model via a kernel nonlinear regression (KNLR) to reduce false positive rate in an unsupervised manner. We show that our approach provides a good tradeoff between abnormality detection rate and false alarm rate, and allows abnormal activity models to be automatically derived without the need to explicitly label the abnormal training data, which are scarce. We demonstrate the effectiveness of our approach using real data collected from a sensor network that is deployed in a realistic setting. Traditional approaches to this problem suffer from a high false positive rate, particularly when the collected sensor data are biased towards normal data while the abnormal events are rare. [...] there is a lack of training data for many traditional data mining methods to be applied.
Author	Pan, J.J. Jie Yin Qiang Yang
Author_xml	– sequence: 1 surname: Jie Yin fullname: Jie Yin organization: Inf. & Commun. Technol. (ICT) Centre, Commonwealth Sci. & Ind. Res. Organ., Hobart, TAS – sequence: 2 surname: Qiang Yang fullname: Qiang Yang organization: Inf. & Commun. Technol. (ICT) Centre, Commonwealth Sci. & Ind. Res. Organ., Hobart, TAS – sequence: 3 givenname: J.J. surname: Pan fullname: Pan, J.J.
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SubjectTerms	Activity Recognition Artificial intelligence Communication system security Data Mining Data security Human motion body Humans Intelligent sensors Mathematical models Monitoring Networks Outlier Detection Regression Sensor Networks Sensors Studies Support vector machines Training Training data Ubiquitous computing Wireless sensor networks
Title	Sensor-Based Abnormal Human-Activity Detection
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