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Biomedical Research Projects
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Australia’s elderly population are susceptible to tripping which is dangerous because they cause fractures and may be fatal. Injuries due to this are estimated to cost $2.4 billion AUD per annum and are known to afflict more than 50% of the elderly population. In addition, other gait disorders such as patellafemoral pain syndrome (PFPS) and osteoartritis (OA) affect more than 25% of the population. The research in this area focuses on developing automated gait detection systems for screening gait disorders.
Current investigations incorporate signal processing techniques and Support Vector Machine (SVM) classifiers for building gait models which can detect these disorders.
Contact: Rezaul.Begg (Rezaul.Begg AT vu.edu.au) |
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Sleep apnoea hypopnea syndrome (SAHS) is a common sleep related breathing disorder that is usually diagnosed through expensive studies in sleep laboratories. Undiagnosed SAHS is associated with cardiovascular morbidity as well as excessive daytime sleepiness and poor quality of life. If SAHS could be diagnosed using only ECG recordings, it could be possible to diagnose SAHS inexpensively from ECG recordings acquired in the patient’s home. In this collaborative research project, we are working on developing algorithms to detect sleep disordered breathing based on ECGs and nonlinear modelling of heart rate variability. We investigated into different machine learning techniques [support vector machines (SVM), Neural Network (NN), Quadratic discriminant (QD) model ] with an aim to find an appropriate model for the automatic detection of SAHS types from their respective overnight ECG recordings and estimation of relative degree of sleep disordered breathing. This investigation may provide essential information for introducing a novel screening device that can aid sleep specialist or other physicians in the initial assessment of patients with suspected SAHS and estimate the relative risk of sleep related breathing disorder, thereby indicating need for referral for overnight sleep studies [i.e. polysomnogram (PSG) recording]. This in turn may help prioritize patients, so that those in greatest need of treatment will undergo full PSG recordings in a timely manner, while those without apnoea will be able to avoid this tedious procedure. Contact: Ahsan Khandoker (a.khandoker AT ee.unimelb.edu.au) |
Crossbow MicaZ mote |
Recently wireless sensor networks have become increasingly important for fine grain sensing. In healthcare, more and more applications are turning towards wireless sensors to facilitate data collection. Wireless sensors are easily deployed and can be used to monitor the health of patients in hospitals, rehabitilation centres and homes. This research aims to develope small sensor devices which can form a smart healthcare network for patient monitoring. The network will integrate computational intelligence techniques to perform automated diagnostics based on the monitoring data. In addition, research problems such as location estimation, tracking and security will be investigated in the framework of healthcare monitoring. Current Undergraduate Project: GAIT Shoe: A wireless sensing shoe for monitoring the elderly Contact: Daniel T.H. Lai (d.lai AT ee.unimelb.edu.au)
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