Action Prediction Using Extremely Low-Resolution Thermopile Sensor Array For Elderly Monitoring

Accident anticipation for monitoring the elderly is an important topic given the global issue of the rapidly aging population. In this work, we propose a novel approach to elderly accident prevention by using a low-resolution (32x32 pixels) infrared camera - thermopile sensor array (TPA) - for the action prediction task. Such a kind of sensor ensures that the monitoring system is cost-effective, does not interfere with daily life of the user, and most importantly fully preserves their privacy which makes it suitable for use in hospitals, nursing homes and private residences. As the majority of accidents involving the elderly occurs when a person attempts to exit the bed unassisted, we concentrate our efforts on predicting that an elderly person will attempt to get off the bed without asking for help. Our system raises an alarm in such a case and informs the caregiver so that they can intervene and offer assistance. Our designed deep-learning model can predict that the elderly patient monitored has the intention to get off the bed by 8.12 seconds at an accuracy of 96.51% (based on our own dataset collected), on average according to experiments, before the action onset is observed.