Research on the prediction model of elderly fall

Abstract

As the world’s aging process accelerates, the issue of elderly safety is about to become a serious social problem. The elderly are prone to falls due to physiological reasons such as decreased physical function, weakened balance and coordination ability, and poor vision. The study of fall prediction models can predict the impending fall behavior in time before the fall, and have enough time to remind the elderly to adjust or take corresponding protective measures. Reduce the damage caused by falls to the human body, reduce the medical expenses caused by falls, and enhance the confidence of the elderly to live independently. This article gives a detailed overview of the research on the wearable device-based fall prediction system, and introduces the entire process of falling. According to the work flow of the wearable device fall detection system, it includes data collection, data preprocessing, feature extraction, and discrimination algorithms. Several aspects of the current research work are introduced, and the existing research results are classified, compared and statistically analyzed to provide meaningful reference and reference for subsequent research work. Finally, a fall prediction model based on an improved ConvLSTM is proposed.