Fall Detection Systems Supported by TinyML and Accelerometer Sensors: An Approach for Ensuring the Safety and Quality of Life of the Elderly

Abstract

Many elderly individuals live alone in their homes, which can lead to significant health and safety concerns due to the risk of falls. Falls not only cause physical injuries but also have social, psychological, and economic impacts that negatively affect the quality of life for older adults. In this context, early detection of falls and implementation of preventive measures are of great importance. Edge computing-based fall detection systems have been developed to effectively address the safety of older adults in such situations. In the present study, a fall detection system is proposed that utilizes edge computing and TinyML technologies, operating on an embedded platform. This system is designed for the interpretation of accelerometer sensor data and processes the data collected through sensors to obtain valuable information. The Edge Impulse platform is used for training an extensive dataset consisting of various fall examples for older adults, allowing the proposed system to achieve a 98.5% recognition accuracy. This cost-effective and user-friendly novel approach combines a portable accelerometer sensor and artificial intelligence software to target early detection and prevention of falls in older adults. This study contributes significantly to the field of edge computing and provides effective solutions to enhance the quality of life for elderly individuals.