Remote Gait Behavior Monitoring System for Older People Based on Wearable Sensor

Gait describes moving the body in a specific direction. Gait analysis, as an effective tool to monitor humans in daily life, can provide technical support for reducing the risk of falls by studying the gait characteristics of older people. In the last decade, the increasing health challenges posed by population aging and chronic noncommunicable diseases have generated significant interest in wearable physiological measurement devices. Wearable sensors have the characteristics of small size and portability, which have great potential in human health care. They can play a crucial role in monitoring human gait in health-related issues and have advantages in gait data collection and processing. In this study, a portable and wearable gait recognition system based on a wireless body area network was designed to solve the problems of single data signal collection and poor portability of the collection equipment in the existing gait recognition system. Based on the wearable acquisition node, the system can collect, transmit, process, and analyze the human body's plantar pressure signal and posture signal in real time, which can realize the effective recognition of human gait and remote data transmission and monitoring. The gait recognition system is used for data collection, feature extraction, and classification, and the classification accuracy can reach more than 90%, which provides a reliable basis for elderly gait behavior monitoring.