A Development of a Wrist-Worn Fall Detection using Binary Neural Network

This paper presents a development of wrist-worn device for fall detection which can be a fall detector and localizer. The system consists of a wristband and a mobile phone. The wristband acts as a fall detector and the mobile phone acts as an IoT gateway for locating and transmitting the fall information to the people involved. The fall detection method is based on a threshold-based and a binary neural network that uses to reduce resources and computational requirements. Experimental results showed that the accuracy of the proposed method was 97.23%. However, for some movement patterns such as hand clapping or hand waving, the system might be given a low accuracy.