Dr Zhi-Qiang Zhang
- Position: Associate Professor
- Areas of expertise: body sensor network; embedded systems; wearable sensing; statistic signal processing; sensor fusion; bio-mechanics; machine learning; big data; gait analysis; rehabilitation
- Email: Z.Zhang3@leeds.ac.uk
- Location: 1.69 School of Electronic & Electrical Eng
- Website: LinkedIn | Googlescholar
Since March 2016, I have been an academic staff at University of Leeds, where I hold a joint position in Body Sensor Network for Healthcare and Robot between School of Electronic and Electrical Engineering and School of Mechanical Engineering. I received my Ph.D. degree in Electrical Engineering from the University of Chinese Academy of Sciences in 2010. Upon completion of my Ph.D. degree, I then moved to Imperial College London working as a research associate for five and half years.
My primary research interests are applying wearable/pervasive sensing technologies into healthcare and wellbeing research, with emphasis on wearable human motion analysis for different patients groups. Meanwhile, I am also interested in exoskeleton rehab robots and prosthetics control using wearable sensors.
Body sensor network,embedded systems, wearable sensing, statistic signal processing, sensor fusion, bio-mechanics, machine learning, big data, gait analysis, rehabiliation<h4>Research projects</h4> <p>Any research projects I'm currently working on will be listed below. Our list of all <a href="https://eps.leeds.ac.uk/dir/research-projects">research projects</a> allows you to view and search the full list of projects in the faculty.</p>
- Personalised Quantitative Upper Extremity Assessment for Stroke Rehabilitation
- REST: Reconfigurable lower limb Exoskeleton for effective Stroke Treatment in residential settings
Research groups and institutes
- Institute of Design, Robotics and Optimisation
- Institute of Robotics, Autonomous Systems and Sensing
Current postgraduate researchers
<li><a href="//phd.leeds.ac.uk/project/1050-wearable-sensing-for-stroke-rehabilitation">Wearable sensing for stroke rehabilitation</a></li>