An Internet of Things Based Bed-Egress Alerting Paradigm Using Wearable Sensors in Elderly Care Environment

Awais, Muhammad; Raza, Mohsin; Ali, Kamran; Ali, Zulfiqar; Irfan, Muhammad; Chughtai, Omer; Khan, Imran; Kim, Sunghwan; Ur Rehman, Masood

Abstract

The lack of healthcare staff and increasing proportions of elderly population is alarming. The traditional means to look after elderly has resulted in 255,000 reported falls (only within UK). This not only resulted in extensive aftercare needs and surgeries (summing up to £4.4 billion) but also in added suffering and increased mortality. In such circumstances, the technology can greatly assist by offering automated solutions for the problem at hand. The proposed work offers an Internet of things (IoT) based patient bed-exit monitoring system in clinical settings, capable of generating a timely response to alert the healthcare workers and elderly by analyzing the wireless data streams, acquired
through wearable sensors. This work analyzes two different datasets obtained from divergent families of sensing technologies, i.e., smartphone-based accelerometer and radio frequency identification
(RFID) based accelerometer. The findings of the proposed system show good efficacy in monitoring the bed-exit and discriminate other ambulating activities. Furthermore, the proposed work manages
to keep the average end-to-end system delay (i.e., communications of sensed data to Data Sink (DS)/Control Center (CC) + machine-based feature extraction and class identification + feedback communications to a relevant healthcare worker/elderly) below 1 10 th of a second.

Citation

Awais, M., Raza, M., Ali, K., Ali, Z., Irfan, M., Chughtai, O., Khan, I., Kim, S., & Ur Rehman, M. (2020). An Internet of Things Based Bed-Egress Alerting Paradigm Using Wearable Sensors in Elderly Care Environment. Sensors, 19(11), Article 2498. https://doi.org/10.3390/s19112498

• Journal Article Type: Article
• Acceptance Date: May 27, 2019
• Online Publication Date: May 31, 2019
• Publication Date: Jun 1, 2020
• Deposit Date: Jun 19, 2020
• Publicly Available Date: Jun 25, 2020
• Journal: Sensors
• Electronic ISSN: 1424-8220
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 19
• Issue: 11
• Article Number: 2498
• DOI: https://doi.org/10.3390/s19112498
• Keywords: Elderly population; Falls; Accelerometer; Radio-frequency identification (RFID); Patient monitoring; Internet of things (IoT); Ambulating activities
• Public URL: https://hull-repository.worktribe.com/output/3502590
• Publisher URL: https://www.mdpi.com/1424-8220/19/11/2498