A Convolutional Neural Networks Approach with Infrared Array Sensor for Bed-Exit Detection

Abstract

Among various kinds of falling prevention measures, bed exit alarm mechanism has raised serious attention recently. In particular, the recent inflow of innovative ICT advancement from Internet of Things, wearable technology, and artificial intelligence have shed on more possibility in realizing effective bed exit alarm systems. This research proposes a deep learning algorithm to construct the bed exit detection model using monitored behavior information collected from the infrared array sensor. Based on the preliminary experiment results, the bed-exit events can be recognized with 92% accuracy, 99% for precision and 97% for recall rate. This approach also has its advantages in low device costs, less data storage needed, less spacial resolution without privacy and legal concerns, and unaffected performance in various lighting conditions.