Fall Detection Algorithm using the Fall Feature Parameters Extracted from Video and Accelerometer Sensor - Yu, YunSeop; Kim, Sang-Hoon (International Information Institute (Tokyo). Information, 2016)
A newly developed fall detection algorithm applied to a simple threshold method and hidden Markov model (HMM) is introduced. The algorithm is based on the fall feature parameters extracted from the video of surrounding environment using overhead camera and accelerometer data of the subject's body. The previously proposed fall feature parameters of video and accelerometer are used to distinguish between a fall and normal activities of daily living, and they are applied to the previously developed fail detection algorithm combining the simple threshold method and HMM. Compared with the authors' previously reported results of separated wearable device and camera, the results obtained with the proposed algorithm are better with a sensitivity of 99.69%, a specificity of 99.69%, and an accuracy of 99.69%.