Development of a Wearable Fall Sensing Device for Enhanced Independent Living Among the Elderly - Olusola K. AKINDE; Abolaji O. ILORI; Habeeb A. ARIKEWUYO (FUDMA Journal of Engineering and Technology, 2025)
Unwitnessed falls among elderly people which consequently has resulted in cases of fatality before caregivers are alerted for prompt response, has been a call for concern. This was the motivation for this work. It integrates a motion sensors and Internet of Things (IoT) algorithms with a web application platform that alerts caregivers. The device would measure the wearer’s physical parameters; movement speed and angular orientation through the sensors. The design makes use of two sensors; the accelerometer for measuring acceleration forces and the gyroscope for rotational motion. The adopted method includes component selection, sensor integration, data acquisition and processing, fall detection algorithm, wireless communication, user interface, power management, testing, and packaging. The test for fall sensor for elderly people was carried out by attaching the device to a mannequin to measure the accuracy of the device. The mannequin was pushed in various ways to check for accuracy when detecting various types of falls. The sensor’s data were sent to the web server (ThingSpeak) at 15s intervals for visualization by the user and to enhance storage of data about fall incidences. The readings from the MPU6050 sensor had good data precision with an average movement speed of 8.37 m/s. The readings from the sensor showed an average trigger speed of 8.05 m/s and an average execution time of 2.4 s. There is a 90% accuracy in the detection of fall occurrences. The use of fall sensors for fall detection has greatly improved the detection of fall occurrences among elderly people, providing a safer approach to caring for elderly people.