Deep-Learning Technique for Risk-Based Action Prediction Using Extremely Low-Resolution Thermopile Sensor Array

Igor Morawski, Wen-Nung Lie, Lee Aing, Jui-Chiu Chiang, Kuan-Ting Chen

Abstract: Eldering caring is important in today’s aging society, especially that accident anticipation/prevention plays an important role. In this paper, a novel approach to preventing elderly accidents based on a very low-resolution thermopile sensor array (TPA) (only $32\times 32$ pixels) is proposed for prediction of bed-exit event that might lead to elderly falls in home caring. Low-resolution TPA sensor, capable of collecting far infrared energy, ensures cost-effective monitoring, no interference with user’s daily life, and most importantly privacy-preservation. Since most of the fall accidents occur when the elderly attempts to get off the bed without assistance, it is thus the focus of this paper to monitor his/her posture and action via TPA image sensor and then predict that an action of getting off the bed will occur in a near future (e.g., $S$ seconds later). Our system can raise an alarm to the caregivers so that they can intervene and offer the necessary assistance. A deep-learning model based on CNN-RNN (Convolutional neural network-Recurrent neural network) architecture was designed which is capable of predicting the elderly bed-exit intention by $S =5.78$ seconds in advance of the action onset at an accuracy of 99.37% according to our dataset evaluation. Our system is also suitable for on-line real-time operation which will be helpful to elderly caring in our society.