The ageing population is a current issue which can be effectively tackled by applying daily activity monitoring via smart sensing technology. The applications of it are mostly aimed at monitoring residents in the residential area for health care improvement. This study uses low pixel resolution infrared sensors for nonintrusive human activity detection and identification without body attachment and taking of individual image. In this work, we design and implement a multiple IR sensors system and a serial experiment to verify the availability of applying low-resolution IR data for human activity recognition for both signal and multiple target scenarios in the healthcare context. In the experimental setup, the sensor system achieves 82.44% accuracy in general and reach 100% accuracy rate for some particular activities. The work proves that the low-resolution IR information is an effective metric for human activity monitoring in healthcare applications.
|Title of host publication||Proceedings of the 32nd International BCS Human Computer Interaction Conference|
|Publication status||Published - Jul 2018|
|Event||32nd International BCS Human Computer Interaction Conference - University of Ulster, Belfast, United Kingdom|
Duration: 4 Jul 2018 → 6 Jul 2018
|Academic conference||32nd International BCS Human Computer Interaction Conference|
|Period||4/07/18 → 6/07/18|