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
The Teager energy based feature parameters for robust speech recognition in car noise
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 01
HMM based falling person detection using both audio and video
ICCV'05 Proceedings of the 2005 international conference on Computer Vision in Human-Computer Interaction
A survey on fall detection: Principles and approaches
Neurocomputing
Fall detection using single-tree complex wavelet transform
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Falls are of the most important problems for frail and elderly people living independently. Early detection of falls is vital to provide a safe and active lifestyle for elderly. Sound, passive infrared (PIR), and vibration sensors can be placed in a supportive home environment to provide information about daily activities of an elderly person. In this paper, signals produced by sound, PIR, and vibration sensors are simultaneously analyzed to detect falls. Hidden Markov models (HMM) are trained for regular and unusual activities of an elderly person and a pet for each sensor signal. Decisions of HMMs are fused together to reach a final decision.