An IoT based device-type invariant fall detection system

As the world elderly population is increasing rapidly, the use of technology for the development of accurate and fast automatic fall detection systems has become a necessity. Most of the fall detection systems are developed for specific devices which reduces the versatility of the fall detection system. This paper proposes a centralized unobtrusive IoT based device-type invariant fall detection and rescue system for monitoring of a large population in real-time. Any type of devices such as Smartphones, Raspberry Pi, Arduino, NodeMcu, and Custom Embedded Systems can be used to monitor a large population in the proposed system. The devices are placed into the users' left or right pant pocket. The accelerometer data from the devices are continuously sent to a multithreaded server which hosts a pre-trained machine learning model that analyzes the data to determine whether a fall has occurred or not. The server sends the classification results back to the corresponding devices. If a fall is detected, the server notifies the mediator of the user's location via an SMS. As a failsafe, the corresponding device alerts nearby individuals by sounding the buzzer and contacts emergency medical services and mediators via SMS for immediate medical assistance, thus saving the user's life. The proposed system achieved 99.7% accuracy, 96.3% sensitivity, and 99.6% specificity. Finally, the proposed system can be implemented on a variety of devices and used to reliably monitor a large population with low false alarm rate, without obstructing the users' daily living, as no external connections are required. (C) 2019 Elsevier B.V. All rights reserved.