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

TLDR

The shortage of healthcare staff and rising elderly population have led to 255,000 falls in the UK, costing £4.4 billion in aftercare and increasing mortality, underscoring the need for automated solutions. This study proposes an IoT‑based bed‑exit monitoring system that alerts healthcare workers and patients in real time by analyzing data from wearable sensors. The system processes wireless data from smartphone accelerometers and RFID‑based accelerometers, comparing two sensing datasets to extract features and classify bed‑exit versus other ambulating activities. Results show the system accurately detects bed exits, distinguishes other movements, and achieves an average end‑to‑end delay of less than 0.1 s.

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.