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
Smart home care network using sensor fusion and distributed vision-based reasoning
Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks
Probabilistic posture classification for Human-behavior analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Human Body Posture Classification by a Neural Fuzzy Network and Home Care System Application
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Classification of speech dysfluencies with MFCC and LPCC features
Expert Systems with Applications: An International Journal
Eigenspace-based fall detection and activity recognition from motion templates and machine learning
Expert Systems with Applications: An International Journal
Classification of Speech Dysfluencies Using LPC Based Parameterization Techniques
Journal of Medical Systems
Human fall detection by mean shift combined with depth connected components
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
A fuzzy model for human fall detection in infrared video
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Computational intelligence models for image processing and information reasoning
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The main purpose of this paper is to use off-the-shelf devices to develop a fall detection system. In human body identification, human body silhouette is adopted to improve privacy protection, and vertical projection histograms of the silhouette image and statistical scheme are used to reduce the effect of human body upper limb activities. The kNN classification algorithm is used to classify the postures using the ratio and difference of human body silhouette bounding box height and width. Meanwhile, since time difference is a vital factor to differentiate fall incident event and lying down event, the critical time difference is obtained from the experiment and verified by statistical hypothesis testing. With the help of the kNN classifier and the critical time difference, a fall incident detection system is developed to detect fall incident events. The experiment shows that it could reduce the effect of upper limb activities and the system has a correct rate of 84.44% on fall detection and lying down event detection.