Back close
Admissions

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 

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. 

Related Projects

Development of Porous Structured Scaffold for Bone Implants
Development of Porous Structured Scaffold for Bone Implants
Development of a Real-time, Process Control Method Based on Neural Network Model Using Feedback of Weld Pool Geometric Parameters Measured by a Vision-based Technique and Experimental Verification for Automated Arc Welding Processes
Development of a Real-time, Process Control Method Based on Neural Network Model Using Feedback of Weld Pool Geometric Parameters Measured by a Vision-based Technique and Experimental Verification for Automated Arc Welding Processes
Modeling of Deformation in Machining Processes
Modeling of Deformation in Machining Processes
Object Oriented Approach To Context Based Image Retrieval
Object Oriented Approach To Context Based Image Retrieval
Developing Wearable Devices to Monitor Breath Rate and Blood Pressure
Developing Wearable Devices to Monitor Breath Rate and Blood Pressure
Admissions Apply Now