Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
SPEEDY: A Fall Detector in a Wrist Watch
ISWC '03 Proceedings of the 7th IEEE International Symposium on Wearable Computers
A fall detection system using k-nearest neighbor classifier
Expert Systems with Applications: An International Journal
The estimation of the gradient of a density function, with applications in pattern recognition
IEEE Transactions on Information Theory
Fall detection on embedded platform using kinect and wireless accelerometer
ICCHP'12 Proceedings of the 13th international conference on Computers Helping People with Special Needs - Volume Part II
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Depth is very useful cue to achieve reliable fall detection since humans may not have consistent color and texture but must occupy an integrated region in space. In this work we demonstrate how to accomplish reliable fall detection using depth image sequences. The depth images are extracted by low-cost Kinect device. The person undergoing monitoring is extracted through mean-shift clustering. A depth connected component algorithm is used to delineate he/she in sequence of images. The system permits unobtrusive fall detection as well as preserves privacy of the user. The experimental results indicate high effectiveness of fall detection in indoor environments and low computational overhead of the algorithm.