Estimating Human Body Configurations Using Shape Context Matching
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Analyzing features for activity recognition
Proceedings of the 2005 joint conference on Smart objects and ambient intelligence: innovative context-aware services: usages and technologies
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
Classification of Posture and Movement Using a 3-axis Accelerometer
ICCIT '07 Proceedings of the 2007 International Conference on Convergence Information Technology
Interfacing human and computer with wireless body area sensor networks: the WiMoCA solution
Multimedia Tools and Applications
An active vision system for fall detection and posture recognition in elderly healthcare
Proceedings of the Conference on Design, Automation and Test in Europe
Recovering human body configurations: combining segmentation and recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
SPINE: a domain-specific framework for rapid prototyping of WBSN applications
Software—Practice & Experience
A practical approach to recognizing physical activities
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
IEEE Transactions on Information Technology in Biomedicine
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Body Sensor Networks (BSNs) are conveying notable attention due to their capabilities in supporting humans in their daily life. In particular, real-time and noninvasive monitoring of assisted livings is having great potential in many application domains, such as health care, sport/fitness, e-entertainment, social interaction and e-factory. And the basic as well as crucial feature characterizing such systems is the ability of detecting human actions and behaviors. In this paper, a novel approach for human posture recognition is proposed. Our BSN system relies on an information fusion method based on the D-S Evidence Theory, which is applied on the accelerometer data coming from multiple wearable sensors. Experimental results demonstrate that the developed prototype system is able to achieve a recognition accuracy between 98.5% and 100% for basic postures (standing, sitting, lying, squatting).