Yordanka Karayaneva , Samuel Baker , Bo Tan , Yanguo Jing
July 2018
Proceedings of the 32nd International BCS Human Computer Interaction Conference (HCI)
Human Computer Interaction Conference
4 - 6 July 2018
Infrared sensors, Human activity detection, Classification methods, Elderly care homes
The daily monitoring of ageing population is a current issue which can be effectively tackled by applying daily activity monitoring via smart sensing technology. The purpose of the monitoring is mostly aimed at collecting health conditional related activity awareness and emergency events detection. This is a pilot study that uses low pixel resolution infrared sensors for nonintrusive human activity detection and recognition without body attachments 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 single and multiple target scenarios in the healthcare context. In the experimental setup, the sensor system achieves 82.44% accuracy in general and reaches 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.
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