Development of a wireless sensor network for human fall detection
Oo Oo Khin, Quang Minh Ta, Chien Chern Cheah · 2017
In this paper, we propose a simple methodology for human fall detection in an indoor environment by using a wireless sensor network. In the proposed methodology, two parallel layers at different heights of sensor nodes are deployed in the monitored environment. By monitoring the changes in the received signal strength indicators (RSSI) between the sensor nodes, the presences of humans and human falls in the monitored environment can be detected. Unlike most methodologies for human fall detection which rely on either wearable electronic devices or the camera systems to monitor humans, this paper provides a flexible and reliable methodology for human fall detection, in which humans are not required to wear any devices in order to be detected, and at the same time, privacies in the monitored environment are ensured. Several network configurations are investigated for detection of human states in the monitored environment. Experimental verifications are performed to demonstrate the effectiveness of the proposed methodology.