Optimising Temporal Segmentation of Multi-Modal Non-EEGSignals for Human Stress Analysis

Thematic Area: Biomedical Signal Processing and Analytics

Project Incharge:Dr. Radhagayathri Udhayakumar

Project Incharge:Dr Shivapratap Gopakumar

Co-Project Incharge:Dr. Chandan Karmakar, Associate Professor, School of IT, Deakin University, Australia
Dr. Dilpreet Buxi, Founder and CEO, Philia Labs, Australia Bakers Institute, Melbourne, Victoria. Shimmer, Australia.

Optimising Temporal Segmentation of Multi-Modal Non-EEGSignals for Human Stress Analysis

This project tackles the challenge of analysing human stress levels by optimising how we divide time segments in data collected from various sensors beyond electroencephalography (EEG). The key question lies in how to best segment this multi-modal data over time. The project aims to find the optimal temporal segmentation strategies that effectively capture the dynamic changes in these diverse signals, ultimately improving the accuracy of stress analysis.

Publication Details

R. Udhayakumar, S. Rahman, S. Gopakumar and C. Karmakar, “Nonlinear Features from Multi-Modal Signals for Continuous Stress Monitoring,” 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Sydney, Australia, 2023
Udhayakumar, R., Rahman, S., Buxi, D., Macefield, V. G., Dawood, T., Mellor, N., & Karmakar, C. (2023). Measurement of stress-induced sympathetic nervous activity using multi-wavelength PPG. Royal Society Open Science, 10(8), 221382.

Proposed Future Work Details

Future work involves investigation into the following avenues:

Investigate methods for personalising the temporal segmentation based on individual characteristics or stress response patterns.
Applying explainable deep learning methods to investigate stress predictors in complex multimodal signals.
Translate the research findings into practical applications like stress management apps, workplace intervention programs, or mental health monitoring tools.

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