Transparent RFID tag wall enabled by artificial intelligence for assisted living
Muhammad Zakir Khan, Muhammad Usman, Ahsen Tahir, Muhammad Farooq, Adnan Qayyum, Jawad Ahmad, Hasan Abbas, Muhammad Ali Imran, Qammer H. Abbasi
Abstract
Abstract Current approaches to activity-assisted living (AAL) are complex, expensive, and intrusive, which reduces their practicality and end user acceptance. However, emerging technologies such as artificial intelligence and wireless communications offer new opportunities to enhance AAL systems. These improvements could potentially lower healthcare costs and reduce hospitalisations by enabling more effective identification, monitoring, and localisation of hazardous activities, ensuring rapid response to emergencies. In response to these challenges, this paper introduces the T ransparent R FID T ag Wall (TRT-Wall) , a novel system taht utilises a passive ultra-high frequency (UHF) radio-frequency identification (RFID) tag array combined with deep learning for contactless human activity monitoring. The TRT-Wall is tested on five distinct activities: sitting, standing, walking (in both directions), and no-activity. Experimental results demonstrate that the TRT-Wall distinguishes these activities with an impressive average accuracy of $$95.6\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>95.6</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math> under four distinct distances (2, 2.5, 3.5 and 4.5 m) by capturing the RSSI and phase information. This suggests that our proposed contactless AAL system possesses significant potential to enhance elderly patient-assisted living.