FallSense: An Automatic Fall Detection and Alarm Generation System in IoT-Enabled Environment
Soumen Moulik, Shubhankar Majumdar · IEEE Sensors Journal · 2018
In this paper, we elaborate the design, implementation, and testing of a system-FallSense, which is able to detect accidental falls of human beings in a precise way. The distinguishing feature of FallSense is its endeavors beyond the scope of accelerometer, which is a component of traditional body sensor network. Along with this acceleration measuring unit, FallSense uses the benefits of an Internet-of-Things-enabled environment, which consists of a number of infrared transmitter-receiver pairs and ultrasonic sensors. Employing a fuzzy inference system, FallSense fuses the data from multiple sensors and becomes over sure before inferring that a fall has occurred. Depending on the inputs from multiple sensors, FallSense generates a value between 0 and 1, which signifies the chance of fall. Results show that multi-sensor-based FallSense achieves overall 16% improvement in comparison with the existing approaches, on an average. Beyond the theoretical modeling, this paper also practically implemented the same with the help of the real-sensing units.