Description of field of research:

Similar to machine condition monitoring, human health monitoring involves collecting signals from different body parts (e.g., brain, chest, muscles) and analysing them for disorder and disease detection and diagnostics. In the past decades, many advanced signal processing techniques have been developed and applied to monitor mechanical systems and components, and better understanding of their features in relation to different types of machine faults have been achieved for fault detection, diagnostics and prognostics. Bio-signals also contain rich information about the health condition and can be used for effective health monitoring if better understanding of their features and relationship with the health condition is achieved. This cross-disciplinary project is to analyse bio-signals for health monitoring by utilising the advanced signal processing techniques developed by experts in machine condition monitoring.

The project is likely to involve a collaboration with the School of Biomedical Engineering.


Mechanical and Manufacturing Engineering

Research areas

Signal processing, Bio-engineering, Health monitoring

The group of Tribology and Machine Condition Monitoring at UNSW is a world-leading research team in the field and has many national and international collaborations. The research group offers appropriate research environment to Taste of Research (ToR) students who are keen to learn many useful skills including data collection and analytical skills using advanced 3D image processing and signal analysis techniques. This group has many years research and supervision experience in the fields of tribology, surface characterisation, and vibration analysis for various applications including fault diagnosis and wear analysis of mechanical and bio-engineering systems. Our group brings together two main areas of expertise - wear debris and vibration analyses - for applications largely in the field of machine condition monitoring as well in the bio-engineering field. The ToR student will have an opportunity to learn useful signal processing skills on physiological signals through doing the project.

  • Identify suitable dataset(s) of physiological signals for the project and understand the relationship of the physiological signals and health conditions
  • Select and apply proper signal processing techniques to extract the physiological features
  • Apply AI techniques for feature extraction and compare with the results obtained using the signal processing techniques (if time is permitted)
  • Produce a report to summarise the research findings and outcomes

To be provided upon request