Post-Fall Time Accounting for Fall Detection Using a Portable Camera

International audienceFalls are one the major problems that threatens the health of the elderly. For this reason, many devices have been developed by researchers all around the globe to continuously monitor and detect critical events, like falls, which allow for a fast-medical intervention to take place. The proposed method for the detection of fall is based on the original version of the Histogram of Oriented Gradient (HOG) combined with Optical Flow and immobilization time to reduce the numbers of false detections. The method was implemented in a system composed of a portable camera and an embedded multi-core computer (Raspberry Pi) to parallelize computations which allows for real time detection. The results of 45 tests conducted on 09 subjects show that falls from standing position can be detected with 80% of sensitivity. The inclusion of immobilization time in the detection process improves the specificity for rotations by 14%

Fall detection system with portable camera

International audienceThe fall is one of the major problems that threaten the health of the elderly. According to world statistics, between 28 and 35% of seniors aged over 65 suffer from at least one fall per year. Continuous monitoring and rapid detection of critical events such as falls allows for rapid response and minimizes impacts. For this, several fall detection devices have been designed by the researchers. This paper proposes a fall detection device for elderly people at home using a portable camera worn on the hips to preserve privacy. The method of fall detection that we propose uses two image processing tools: The oriented gradient histogram (HOG) and the optical flow. The results of tests being carried on 14 subjects show that falls can be detected from standing, sitting or lying with a general sensitivity of 95%, from a set of data resulting from 20 tests, performed by each of the volunteers, for each of these three scenarios as well as for activities of daily living; the HOG-based method allowed the detection of falls but introduced many false detections which led to a specificity of 46.66%. The introduction of the optical flow has improved the specificity by reducing it to 68.33%. The system has been shown to be effective for rotations with a specificity increased by 50% over the use of HOG only. Also, the specificity has slightly increased for the events: sit down and lie down. However, this increase is accompanied by a decrease in sensitivity, since some falls are not detected by the optical flow, as the case of falls from an ‘elongated’ position