Fall detection model based on AlphaPose combined with LSTM and Lightgbm

Abstract

Major countries in the world are facing the problem of aging. Whether it is an internal cause or an external cause of the fall, if the rescue is not timely, it will cause great harm to the elderly. Therefore, we urgently need a real-time and accurate fall detection technology for timely rescue after the elderly fall. For fall detection, the existing sensor-based wearable fall detection devices are expensive to popularize, and there is a problem that the elderly forget to wear them. Therefore, a fall detection model based on AlphaPose combined with LSTM and Lightgbm is proposed. In the algorithm, AlphaPose is first used to extract the key points of the human body, and then two LSTM sub-networks are used to extract temporal and spatial features, and then sent to the main LSTM network for feature fusion. Lightgbm performs classification to achieve more accurate detection results. Experiments were conducted on two fall datasets, KFALL and UR, and the fall detection accuracy rates were 94.43% and 93.81%, respectively.