Layered Representations for Human Activity Recognition
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
Semi-Supervised Adapted HMMs for Unusual Event Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Activity Recognition and Abnormality Detection with the Switching Hidden Semi-Markov Model
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Video Behaviour Profiling and Abnormality Detection without Manual Labelling
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields
International Journal of Robotics Research
Conditional random fields for activity recognition
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
International Journal of Robotics Research
Incremental HMM training applied to ECG signal analysis
Computers in Biology and Medicine
Sensor-Based Abnormal Human-Activity Detection
IEEE Transactions on Knowledge and Data Engineering
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 from Accelerometer Data on a Mobile Phone
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
Concept and Design of a Video Monitoring System for Activity Recognition and Fall Detection
ICOST '09 Proceedings of the 7th International Conference on Smart Homes and Health Telematics: Ambient Assistive Health and Wellness Management in the Heart of the City
Factorial Switching Linear Dynamical Systems Applied to Physiological Condition Monitoring
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Stream Prediction Using Incremental Hidden Markov Models
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
A hybrid discriminative/generative approach for modeling human activities
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Abnormal activity recognition based on HDP-HMM models
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Synthetic Training Data Generation for Activity Monitoring and Behavior Analysis
AmI '09 Proceedings of the European Conference on Ambient Intelligence
Computer Vision and Image Understanding
Activity recognition using cell phone accelerometers
ACM SIGKDD Explorations Newsletter
Activity recognition using hierarchical hidden markov models on a smartphone with 3D accelerometer
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
Smart walkers!: enhancing the mobility of the elderly
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
A practical approach to recognizing physical activities
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
Identification of humans using gait
IEEE Transactions on Image Processing
Infinite Hidden Markov Models for Unusual-Event Detection in Video
IEEE Transactions on Image Processing
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Most of the systems for recognition of activities aim to identify a set of normal human activities. Data is either recorded by computer vision or sensor based networks. These systems may not work properly if an unusual event or abnormal activity occurs, especially ones that have not been encountered in the past. By definition, unusual events are mostly rare and unexpected, and therefore very little or no data may be available for training. In this paper, we focus on the challenging problem of detecting unusual temporal events in a sensor network and present three Hidden Markov Models (HMM) based approaches to tackle this problem. The first approach models each normal activity separately as an HMM and the second approach models all the normal activities together as one common HMM. If the likelihood is lower than a threshold, an unusual event is identified. The third approach models all normal activities together in one HMM and approximates an HMM for the the unusual events. All the methods train HMM models on data of the usual events and do not require training data from the unusual events. We perform our experiments on a Locomotion Analysis dataset that contains gyroscope, force sensor, and accelerometer readings. To test the performance of our approaches, we generate five types of unusual events that represent random activity, extremely unusual events, unusual events similar to specific normal activities, no or little motion and normal activity followed by no or little motion. Our experiments suggest that for a moderately sized time frame window, these approaches can identify all the five types of unusual events with high confidence.