Enhanced Human Body Fall Detection Utilizing Advanced Classification of Video and Motion Perceptual Components

The monitoring of human physiological data, in both normal and abnormal situations of activity, is interesting for the purpose of emergency event detection, especially in the case of elderly people living on their own. Several techniques have been proposed for identifying such distress situations using either motion, audio or video data from the monitored subject and the surrounding environment. This paper aims to present an integrated patient fall detection platform that may be used for patient activity recognition and emergency treatment. Both visual data captured from the user's environment and motion data collected from the subject's body are utilized. Visual information is acquired using overhead cameras, while motion data is collected from on-body sensors. Appropriate tracking techniques are applied to the aforementioned visual perceptual component enabling the trajectory tracking of the subjects. Acceleration data from the sensors can indicate a fall incident. Trajectory information and subject's visual location can verify fall and indicate an emergency event. Support Vector Machines (SVM) classification methodology has been evaluated using the latter acceleration and visual trajectory data. The performance of the classifier has been assessed in terms of accuracy and efficiency and results are presented.