Human Falling Recognition Based on Movement Energy Expenditure Feature

Falls in the elderly are a common phenomenon in daily life, which causes serious injuries and even death. Human activity recognition methods with wearable sensor signals as input have been proposed to improve the accuracy and automation of daily falling recognition. In order not to affect the normal life behavior of the elderly, to make full use of the functions provided by the smartphone, to reduce the inconvenience caused by wearing sensor devices, and to reduce the cost of monitoring systems, the accelerometer and gyroscope integrated inside the smartphone are employed to collect the behavioral data of the elderly in their daily lives, and the threshold analysis method is used to study the human falling behavior recognition. Based on this, a three-level threshold detection algorithm for human fall behavior recognition is proposed by introducing human movement energy expenditure as a new feature. The algorithm integrates the changes of human movement energy expenditure, combined acceleration, and body tilt angle in the process of falling, which alleviates the problem of misjudgment caused by using only the threshold information of acceleration or (and) angle change to discriminate falls and improves the recognition accuracy. The recognition accuracy of this algorithm is verified by experiments to reach 95.42%. The APP is also devised to realize the timely detection of fall behavior and send alarms automatically.