Gait
Understanding the underlying mechanisms and associated deficits in movement dynamics across the lifespan and the effects of pathological conditions, such as falls, will lead to many applications in the design and evaluation of diagnostic and assessment methods for human movement. For example, the methods may be used to assess age-related decline in gait control, the associated risk of sustaining a fall, and determining the effects of exercise interventions and treatments. Falls and injuries during walking in older adults are a major public health issue and cost Australia $498million pa; these costs are projected to triple by 2051 if falls rates remain unchanged (Moller, 2003). Our research is mainly focussed on building novel hardware solutions for acquiring gait parameters and developing associated signal processing/machine learning algorithms.
- Real-time Hand Gesture Identification for Human Computer Interaction based on ICA of Surface Electromyogram:
Dinesh Kant Kumar.
- Improving Isometric Hand Gesture Identification for HCI based on Independent Component Analysis in Bio-signal Processing:
Dinesh Kant Kumar.
- Human Gait Pattern Analysis and Modelling:
M. Palaniswami;
Post Doctoral Research Fellow: Ahsan Khandoker, Daniel T. H. Lai, Slaven Marusic. - Smart Shoe Sensors:
Rezaul Begg;
Post Doctoral Research Fellow: Daniel T. H. Lai. - Automated Detection of Gait Disorders:
Rezaul Begg;
Post Doctoral Research Fellow: Daniel T. H. Lai, Ahsan Khandoker, Pazit Levinger. - Multi-Camera Multi-Person Pointing Gesture Recognition for Interaction in Immersive Environments:
Arcot Sowmya.
