Dr Zhi-Qiang Zhang

Profile

Since March 2016, I have been an academic staff at University of Leeds with a joint appointment 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. 

I have pioneered research into human motion and kinematics analysis using wearable inertial sensors and has established a proven track record in wearable sensing, deep learning, rehabilitation and musculoskeletal modelling.

 

Research interests

Data Analytics; Data Science; Machine Learning; Deep Learning; Wearable Sensing and Robotics; Biomechanics; Musculoskeletal Modelling; Assistive Technology and Rehabilitation

<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

Research groups and institutes

• Institute of Design, Robotics and Optimisation
• Institute of Robotics, Autonomous Systems and Sensing
• Assistive and rehabilitation robotics

Current postgraduate researchers

• Tianao Cao
• Kejian Li

<h4>Postgraduate research opportunities</h4> <p>We welcome enquiries from motivated and qualified applicants from all around the world who are interested in PhD study. Our <a href="https://phd.leeds.ac.uk">research opportunities</a> allow you to search for projects and scholarships.</p>

Projects

<li><a href="//phd.leeds.ac.uk/project/1267-computational-neuromusculoskeletal-modelling">Computational Neuromusculoskeletal Modelling</a></li> <li><a href="//phd.leeds.ac.uk/project/1273-contactless-sensing-for-vital-sign-monitoring">Contactless Sensing for Vital Sign Monitoring</a></li> <li><a href="//phd.leeds.ac.uk/project/1271-data-analytics-for-steady-state-visual-evoked-potential-based-brain-computer-interface">Data Analytics for Steady-State Visual Evoked Potential-based Brain-Computer Interface</a></li> <li><a href="//phd.leeds.ac.uk/project/1272-machine-learning-and-deep-learning-for-myoelectric-control">Machine Learning and Deep Learning for Myoelectric Control</a></li> <li><a href="//phd.leeds.ac.uk/project/1269-soft-sensing-and-actuation-for-upper-limb-rehabilitation">Soft Sensing and Actuation for Upper Limb Rehabilitation</a></li> <li><a href="//phd.leeds.ac.uk/project/1270-wearable-data-analytics">Wearable Data Analytics</a></li> <li><a href="//phd.leeds.ac.uk/project/1268-wearable-energy-harvester-from-human-gait">Wearable Energy Harvester from Human Gait</a></li> <li><a href="//phd.leeds.ac.uk/project/1050-wearable-sensing-for-stroke-rehabilitation">Wearable sensing for stroke rehabilitation</a></li>