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
Summarising contextual activity and detecting unusual inactivity in a supportive home environment
Pattern Analysis & Applications
The relationship between Precision-Recall and ROC curves
ICML '06 Proceedings of the 23rd international conference on Machine learning
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
Linguistic summarization of video for fall detection using voxel person and fuzzy logic
Computer Vision and Image Understanding
An active vision system for fall detection and posture recognition in elderly healthcare
Proceedings of the Conference on Design, Automation and Test in Europe
Introducing a statistical behavior model into camera-based fall detection
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part I
Fall detection from depth map video sequences
ICOST'11 Proceedings of the 9th international conference on Toward useful services for elderly and people with disabilities: smart homes and health telematics
Real-time human pose recognition in parts from single depth images
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
IEEE Transactions on Information Technology in Biomedicine
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Falls are a major risk for the elderly and where immediate help is needed. The elderly, especially when suffering from dementia, are not able to react to emergency situations properly, thus falls need to be detected automatically. An overview of different classes of fall detection approaches is presented and a vision-based approach is introduced. We propose the use of a Kinect to obtain 3D data in combination with fuzzy logic for robust fall detection and show that our approach outperforms current state-of-the-art algorithms. Our approach is evaluated on 72 video sequences, containing 40 falls and 32 activities of daily living.