Internet of Things (IoT) in Smart Hospitals for Pressure Injury Monitoring

Summary

This project explores the use of Internet of Things (IoT) technologies in smart hospitals to enable real-time monitoring and prevention of pressure injuries. It focuses on developing intelligent sensing systems and data-driven analytics to improve patient care, safety, and clinical decision-making.

Supervisor

Dr Zihuai Lin.

Research location

Electrical and Computer Engineering

Synopsis

The project on IoT in Smart Hospitals for Pressure Injury Monitoring aims to design and implement a connected healthcare system that leverages wearable and ambient sensors to continuously track patients’ posture, mobility, and skin interface pressure. Pressure injuries (bedsores) are a significant clinical challenge, particularly for immobile or elderly patients, and early detection is critical for prevention. This research will integrate IoT-enabled sensing devices with wireless communication, edge/cloud computing, and AI-based analytics to provide real-time alerts for caregivers and clinicians. The study will also investigate energy-efficient system design, interoperability with hospital information systems, and data security to ensure scalability in clinical environments. The outcomes of this project will contribute to smarter, safer hospitals by reducing preventable injuries, improving patient outcomes, and lowering healthcare costs.

Research Techniques/Methodologies/Technologies

The project will use IoT-enabled sensing platforms (wearable and pressure sensors), wireless communication (BLE, WiFi, or LoRa), edge/cloud computing, and AI/ML algorithms for predictive analytics. Clinical validation and usability studies will also form part of the research.

Successful candidates

Candidates should have a background in biomedical engineering, computer science, electrical/electronic engineering, or a related discipline. Experience with IoT systems, wireless communications, sensor technologies, or AI/ML is desirable. Knowledge of healthcare applications, data security, or clinical workflows will be advantageous. Strong problem-solving skills, programming experience (Python, MATLAB, or embedded systems), and motivation for interdisciplinary research are essential.

How to apply:

To apply, please email [email protected] the following:

a detailed CV, including academic qualifications, research experience and publications
academic transcripts

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Opportunity ID

The opportunity ID for this research opportunity is 3695