This research project investigates the development of artificial intelligence–based activity recognition and smart monitoring systems to support independent living and healthcare monitoring for elderly populations.

The work integrates computer vision, machine learning, and intelligent sensing technologies to detect behavioural patterns, monitor daily activities, and identify deviations that may indicate health risks or functional decline. The research aims to enable scalable and unobtrusive monitoring solutions for care homes and residential environments.

Smart monitoring framework for elderly activity recognition and behavioural analysis.

The project spans both algorithmic research and applied system development, including real-world smart care prototypes and deployment-oriented architectures.

Research Contributions

• Vision-based human activity recognition using deep learning and pose analysis
• Behavioural pattern modelling for elderly independence monitoring
• Intelligent anomaly detection for risk identification and fall detection
• Integration of sensing technologies within smart care environments
• Edge- and cloud-based architectures for healthcare monitoring systems

Technologies

Computer vision · Deep learning · Human activity recognition · Smart sensing · IoT · Digital health · Ambient intelligence

Related Publications

Journal Article

Vision-based activity recognition for unobtrusive monitoring of the elderly in care settings
Technologies (MDPI), 2025

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Conference Paper

SkyStrm: An Activity Monitoring System to Support Elderly Independence Through Smart Care Homes
IEEE Conference on Cloud and Internet of Things (CIoT), 2023

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