Development of a two-threshold-based fall detection algorithm for elderly health monitoring

Population aging has become a worldwide problem. Falls are considered as the first source of disabilities among elderly people. Fall detection algorithms are the key to distinguish a fall from daily activities, automatically alert when a fall occurred and significantly decrease the time of rescue when the monitored patient falls down. The algorithm presented in this paper uses tri-axial accelerometer outputs to discriminate between falls and daily activities. It is mainly based on a two-thresholds approach and inactivity posture recognition after falling. The algorithm showed prominent results compared to existing works and will be improved and implemented on a Zynq board for future applications.