Activity summarisation and fall detection in a supportive home environment

By Hammadi Nait-Charif, Stephen J. McKenna. Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., 2004

370 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/508dd39be48aba01ed45c11b14b931d4f9a48629

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In Computer Vision and Pattern Recognition

In Human-Computer Interaction

Citations in the corpus

Fall Detection from Human Shape and Motion History Using Video Surveillance (2007, background): “Nowadays, the usual solution to detect falls is to use some wearable sensors like accelerometers or help buttons.”
A Smart and Passive Floor-Vibration Based Fall Detector for Elderly (2006, background): “detectors: These devices track the resident using cameras installed at vantage locations and detect an event of a fall based on image processing algorithms that are designed to identify unusual inactivity, which is more likely to follow an event of fall [7].”
The smart thermostat: using occupancy sensors to save energy in homes (2010, background): “Therefore, the smart thermostat does not require cameras or wearable tags that may be considered intrusive to the user [22, 23] or more sophisticated sensing systems used for fine-grained tracking and activity recognition [24, 25, 26].”
A review on vision techniques applied to Human Behaviour Analysis for Ambient-Assisted Living (2012, neutral): “This way, there are works which choose to cover the blob with an elliptical model in order to extract conclusions based on the direction of the major and minor axis of the ellipse and the length ratio between them (see [74]).”
RGB-D Camera-based Daily Living Activity Recognition (background): “Nait-Charif et al. developed a computer-vision based system to recognize abnormal ADL [17] in a supportive home environment.”
Intelligent Assistive Technology Applications to Dementia Care: Current Capabilities, Limitations, and Future Challenges (2009, background): “33,57 Lee and Mihailidis 33 conducted a pilot study using 21 volunteers between the ages of 20 and 40 years to evaluate the efﬁcacy and performance of a fall-detection system that uses a ceiling-mounted video camera.”
Video-Based Abnormal Human Behavior Recognition—A Review (2012, background): “The authors of [79] combined trajectory tracking with a model of spatial context to detect unusual activity (a fall).”
Fall detection - Principles and Methods (2007, methodology): “This inspired Nait-Charif [ 24 ] and Rougier [25] to track the head movements and detect the fall with particles filters algorithms.”
Robust Video Surveillance for Fall Detection Based on Human Shape Deformation (2011, methodology): “Nait-Charif and McKenna [ 11 ] tracked the person using an ellipse, and analyzed the resulting trajectory to detect inactivity outside the normal zones of inactivity like chairs or sofas.”