Activity Summarisation and Fall Detection in a Supportive Home Environment
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Fall Detection from Human Shape and Motion History Using Video Surveillance
AINAW '07 Proceedings of the 21st International Conference on Advanced Information Networking and Applications Workshops - Volume 02
Intelligent household surveillance robot
ROBIO '09 Proceedings of the 2008 IEEE International Conference on Robotics and Biomimetics
A survey on visual surveillance of object motion and behaviors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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The aging of population has become a social problem and fall is a major health risk in the elderly. To this end, this paper presents a novel approach for fall detection applied to an intelligent household surveillance robot. Silhouette based features are extracted, including aspect ratio of minimal bounding box of the human silhouette, approximated elliptical eccentricity, normalized central moments and Hu moments. Fall and other human motions, such as walk, bend, run and crouch, are modeled using Hidden Markov Models (HMM) with Gaussian Mixture Models (GMM). The experimental results are evaluated by sensitivity, specificity and accuracy and the average of them reaches 88.71%, 97.56% and 96.26% respectively.