Real world activity recognition with multiple goals
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Detecting Abnormal Events via Hierarchical Dirichlet Processes
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Activity recognition: linking low-level sensors to high-level intelligence
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Abnormal activity recognition based on HDP-HMM models
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Three challenges in data mining
Frontiers of Computer Science in China
Wireless sensor networks for healthcare: A survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
The forecasting model based on modified SVRM and PSO penalizing Gaussian noise
Expert Systems with Applications: An International Journal
An adaptive sensor network for home intrusion detection by human activity profiling
Artificial Life and Robotics
Towards mobile intelligence: Learning from GPS history data for collaborative recommendation
Artificial Intelligence
Centinela: A human activity recognition system based on acceleration and vital sign data
Pervasive and Mobile Computing
Towards the detection of unusual temporal events during activities using HMMs
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Elderly activities recognition and classification for applications in assisted living
Expert Systems with Applications: An International Journal
A mobile data collection platform for mental health research
Personal and Ubiquitous Computing
Design of a situation-aware system for abnormal activity detection of elderly people
AMT'12 Proceedings of the 8th international conference on Active Media Technology
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
An evidential fusion approach for activity recognition in ambient intelligence environments
Robotics and Autonomous Systems
Accurate energy expenditure estimation using smartphone sensors
Proceedings of the 4th Conference on Wireless Health
Understanding spatial contexts of the real world under explicit or tacit roles of location
Proceedings of the 25th Australian Computer-Human Interaction Conference: Augmentation, Application, Innovation, Collaboration
Energy expenditure estimation with smartphone body sensors
BodyNets '13 Proceedings of the 8th International Conference on Body Area Networks
Proceedings of the Fourth International Conference on Learning Analytics And Knowledge
Detection of daily living activities using a two-stage Markov model
Journal of Ambient Intelligence and Smart Environments - Intelligent agents in Ambient Intelligence and smart environments
Journal of Ambient Intelligence and Smart Environments
Unsupervised categorization of human motion sequences
Intelligent Data Analysis
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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.