Research
Projects

5 projects — 1 active · 1 upcoming · 3 completed.

EYESPEAK — Mental Fatigue Detection in Team Sports Current
SETU President's Award

EYESPEAK — Mental Fatigue Detection in Team Sports

Developing an interpretable, low-burden machine-learning approach to estimate mental fatigue and its effect on decision quality in team field sports, using non-intrusive signals captured during routine training.

€125,000
AI-Enabled Patient Engagement Tools for Early Health Risk Management Upcoming
In preparation — Research Ireland

AI-Enabled Patient Engagement Tools for Early Health Risk Management

A proposed feasibility study on accessible, AI-enabled digital tools that help people engage earlier with their health — co-designed with communities and healthcare stakeholders, in partnership with Sun Life.

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Accurate Battery State-of-Charge Estimation with Lightweight Machine Learning Completed
Ajman University Internal Research Grant

Accurate Battery State-of-Charge Estimation with Lightweight Machine Learning

Designing and validating lightweight machine-learning models that estimate battery State-of-Charge (SoC) in real time for low-power electronic devices operating under varying temperature and load conditions.

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Battery State-of-Charge Data Collection & Characterisation Completed
Doctoral research — Universiti Sains Malaysia

Battery State-of-Charge Data Collection & Characterisation

A controlled experimental programme to characterise battery behaviour across chemistries, temperatures and load profiles — producing an open dataset for State-of-Charge estimation research in wireless sensor networks.

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Adaptive Clear Channel Assessment (A-CCA) — Energy-Efficient MAC for WSNs Completed
Doctoral research — Universiti Sains Malaysia

Adaptive Clear Channel Assessment (A-CCA) — Energy-Efficient MAC for WSNs

A Media Access Control (MAC) layer technique for IEEE 802.15.4 wireless sensor networks that adapts clear-channel assessment to cut idle listening and false wake-ups — lowering power consumption and extending node lifetime.

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