Design of Wearable Fall Detection System

Falling has become one of the greatest threats to the physical health of the elderly. Accurately predicting the risk of falling and timely and effective detection of falls are very important. The application of wearable devices in fall detection is receiving increasing attention. This paper uses the small biomedical sensor mpu9150 to collect three-axis acceleration data of the human body, and then uses neural networks to train a fall detection model based on the three-axis acceleration. Additionally, simulated fall experiments were conducted on 30 volunteers in a laboratory environment to build a fall database. The experimental results show that the accuracy of the fall detection system is 95.8%. The system designed in this paper has the features of wearability, miniaturization, low power consumption, and high accuracy, and is suitable for multiple fields such as sports monitoring and high-risk operations.