Deep Learning Assisted Intelligent Fall Detection Mechanism: An IoT and Healthcare Integration of Elderly People

In the context of elderly living, occurrence of fall can create critical health deterioration and it is a significant health concerns during their stay in home. As a result, having reliable and timely prediction that pays high attention can help them in their daily life. In this research, the information obtained from a home environment with the assistance of Internet of Technology (IoT) is classified by utilising a feed forward neural network (FFNN). Information enters at the input unit will flows via layer across the network until it reaches the output unit. There is no feedback amongst layers during normal operating conditions, which is when it functions as a classifier. This is why they're referred to as FFNN. A significant element in fall detection is detecting human posture, because fall events are similar to falling posture. A fall can be effectively identified by a distinguishing feature. The proposed system can clearly detect the normal lying posture and lying after falling and it can efficiently detect action motion as well as recognize the action posture. The performance of the proposed and exiting is compared where FFNN achieves 98% detection accuracy, which utilises standard benchmark data sets.