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

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.

Performance Analysis of Different Types of Machine Learning Classifiers for Non-Technical Loss Detection

“…There is still a need for creating a benchmark dataset which can widely be used in NTL detection. Another future direction is using penalized machine learning models in which weighted classifiers [55] are used. The best classifiers identified in this study can also be implemented on different feature selection approaches.…”

Treating Class Imbalance in Non-Technical Loss Detection: An Exploratory Analysis of a Real Dataset

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Intelligent IoT Framework for Indoor Healthcare Monitoring of Parkinson’s Disease Patient

“…Clinically, accurate diagnosis of PD in the early stages is extremely complex, and the accuracy of diagnosis has been a big challenge for experts and researchers in medicine. However, the recent developments in Internet-of-things (IoT) and machine intelligence [11,12] offer substantial benefits in detection [13,14] and monitoring of patients in their early stages of PD [15][16][17]. Recently, attempts have been made to apply data mining and artificial intelligence-based methods on the auditory data [10,18,19], human movement analysis through inertial sensors [8,20], and imagery data [21] of the PD patients.…”