Fall Detection Using Depth Maps Acquired by a Depth Sensing Camera

Abstract: In a time when the population and life expectancy increase, the demands on health care change. The biggest cost in Swedish health care today is related to accidents regarding falls of old people. This Master's thesis presents a solution to fall detection and logging of data from falls. Falls themselves are hard to stop, but there are several factors behind falls that can be changed in order to prevent them. In this Master's thesis the development of a fall detection system and it's results are presented. The system is based on a Microsoft Kinect and a Raspberry Pi 2, these components are standard, of-the-shelf products with a total price less than 2000 SEK, which is significantly less than the price of the hardware used in the majority of other projects in this field. Using consumer components opens up the possibility for others to further develop the system in the future. The developed solution uses thresholds based on acceleration and height to identify falls. These parameters have been used alone in earlier studies, by using the unique technique of combining them gives more accurate results. The development of the system was divided in to two phases. In the first phase a data collection was carried out, 200 falls and activities performed by a total of 5 test subjects were logged and the data analyzed. The results were used when developing the final fall detection software. In the second phase, 75 falls and activities where performed by two test subjects in order to test the accuracy of the software. Combining acceleration with height proved to be a good solution, detecting falls with a sensitivity of 92 percent and a specificity of 96 percent.