A Lightweight Model for Falling Detection

Abstract

In human activities life, accidental falls are a frequent occurrence. It can happen in children, the elderly, and even adults. Early detection of human falls is the most effective way to avoid the high risk of loss of self-control, death, or injury in humans. This means also reducing the national health system’s cost. Therefore research and development of fall detection and rescue systems are needed. Currently, the fall detection system is mainly based on wearable sensors, ambient, and vision sensors. Each method has certain advantages and limitations. The previous works usually focused on size while the speed was not often considered. Therefore, studies that aim to propose a lightweight model for Fall Detection with less complexity of memory and processing time but having reasonable accuracy are still potential. A 3-dimensional lightweight model has been proposed based on MobileNet architecture for falling detection in this paper.