IMU Sensor Based Expandable Fall Detection System Design

Falls and their consequences pose significant health problems affecting individuals of various age groups. Aging individuals are generally weaker, less stable, and slower to react, increasing the likelihood of falls and injuries. Falls are a serious concern, have a significant impact on mobility and quality of life. They also have a significant financial impact on healthcare systems worldwide. The effects of a fall can range from minor bruises, injuries, life-threatening fractures and even fatal conditions. For these reasons, continuous monitoring of the activities of elderly and disabled people has become one of the main goals of telemedicine, and wearable devices have become widespread. The main goal of this study is to develop a system that allows for the precise and automatic detection and monitoring of falls. This approach will generate timely alerts and notifications to quickly inform caregivers or medical doctors. The system created in the study is expandable and can add a large number of sensors. The data transferred from the IMU sensors placed on the patient to the Raspberry Pi is evaluated by software. A fall perception is created when sudden changes occur from the values determined as normal posture levels. Bending and falling are separated. Taking this into account, various falling variations are detected.