A tutorial on hidden Markov models and selected applications in speech recognition
Readings in speech recognition
Neural network design
Naive Bayes models for probability estimation
ICML '05 Proceedings of the 22nd international conference on Machine learning
Activity Recognition and Monitoring Using Multiple Sensors on Different Body Positions
BSN '06 Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks
Sensing Muscle Activities with Body-Worn Sensors
BSN '06 Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Machine Vision and Applications
Detection of early morning daily activities with static home and wearable wireless sensors
EURASIP Journal on Advances in Signal Processing
ICCHP '08 Proceedings of the 11th international conference on Computers Helping People with Special Needs
Multi-sensor fusion for human daily activity recognition in robot-assisted living
Proceedings of the 4th ACM/IEEE international conference on Human robot interaction
Proceedings of the 2nd International Conference on PErvasive Technologies Related to Assistive Environments
A hybrid discriminative/generative approach for modeling human activities
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Human daily activity recognition in robot-assisted living using multi-sensor fusion
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Real-time activity classification using ambient and wearable sensors
IEEE Transactions on Information Technology in Biomedicine - Special section on body sensor networks
View independent human body pose estimation from a single perspective image
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Simultaneous tracking and activity recognition (STAR) using many anonymous, binary sensors
PERVASIVE'05 Proceedings of the Third international conference on Pervasive Computing
Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
IEEE Transactions on Information Theory
High-Speed Action Recognition and Localization in Compressed Domain Videos
IEEE Transactions on Circuits and Systems for Video Technology
Ego network models for Future Internet social networking environments
Computer Communications
Elderly activities recognition and classification for applications in assisted living
Expert Systems with Applications: An International Journal
Mobile context inference using two-layered Bayesian networks for smartphones
Expert Systems with Applications: An International Journal
A computing-efficient algorithm for accelerometer-based real-time activity recognition systems
BodyNets '13 Proceedings of the 8th International Conference on Body Area Networks
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In this paper, we proposed an approach to indoor human daily activity recognition which combines motion data and location information. One inertial sensor is worn on the right thigh of a human subject to provide motion data, while an optical motion capture system is used to provide the human location information. Such a combination has the advantage of significantly reducing the obtrusiveness to the human subject at a moderate cost of vision processing, while maintaining a high accuracy of recognition. First, a two-step algorithm is proposed to recognize the activity based on motion data only. In the coarse-grained classification, two neural networks are used to classify the basic activities. In the fine-grained classification, the sequence of activities is modeled by an HMM to consider the sequential constraints. The modified short-time Viterbi algorithm is used for real-time daily activity recognition. Second, to fuse the motion data with the location information, Bayes' theorem is used to update the activities recognized from the motion data. We conducted experiments in a mock apartment and the obtained results proved the effectiveness and accuracy of our algorithms.