IoT-Based Human Fall Detection System

Human falls are an issue that especially affects elderly people, resulting in permanent disabilities or even in the person's death. Preventing human falls is a social desire, but it is almost impossible to achieve because it is not possible to ensure full prevention. A possible solution is the detection of human falls in near real-time so that help can quickly be provided. This has the potential to greatly reduce the severity of the fall in long-term health consequences. This work proposes a solution based on the internet of things devices installed in people's homes. The proposed non-wearable solution is non-intrusive and can be deployed not only in homes but also in hospitals, rehabilitation facilities, and elderly homes. The solution uses a three-layered computation architecture composed of edge, fog, and cloud. A mathematical model using the Morlet wavelet and an artificial intelligence model using artificial neural networks are used for human fall classification; both approaches are compared. The results showed that the combination of both models is possible and brings benefits to the system, achieving an accuracy of 92.5% without false negatives.

Automated summary

This paper presents a human fall detection solution based on IoT devices. The proposed solution is non-intrusive and can be deployed in various settings, such as homes, hospitals, rehabilitation facilities, and elderly homes. It utilizes a three-layered computation architecture of edge, fog, and cloud. A mathematical model using the Morlet wavelet and an artificial intelligence model using artificial neural networks are employed for human fall classification, and the combination of both models achieves an accuracy of 92.5% without false negatives.