Dr Ziyun Ding PhD, FHEA

Dr Ziyun Ding

Department of Mechanical Engineering
Associate Professor

Contact details

Email
z.ding@bham.ac.uk
Twitter
@ziyunding
View my research portal
Address
University of Birmingham
Edgbaston
Birmingham
B15 2TT
UK

Dr Ziyun Ding is an Associate Professor in the Department of Mechanical Engineering, and the deputy head of the Biomedical Engineering Research Group in the School of Engineering. 

Her research is driven by the pressing societal need to improve mobility, independence, and quality of life for individuals affected by musculoskeletal and neurological impairments — challenges that are intensifying globally with ageing populations and rising healthcare demands. Dr Ding’s research focuses on advancing understanding of how muscle-tendon and neuro-musculoskeletal dynamics adapt to trauma, degenerative diseases, and ageing. By combining experimental biomechanics, wearable technologies, computational neuromusculoskeletal modelling and AI, her vision is to unravel the complex mechanisms underlying human movement. New knowledge and insights are translated into intelligent, evidence-based solutions for assistive technologies, surgeries and rehabilitation, ultimately enhancing global well-being and shaping a more sustainable future.

Qualifications

  • Fellow of The Higher Education Academy, 2021
  • Associate Fellow of The Higher Education Academy, 2019
  • PhD in Engineering, University of Liverpool, UK, 2013
  • MEng in Mechanical Engineering, Nanjing University of Aeronautics and Astronautics, China, 2009
  • BEng in Mechanical Engineering, Nanjing University of Aeronautics and Astronautics, China, 2006

Biography

Ziyun received her PhD in 2013 from the University of Liverpool. Her PhD thesis entitled “Manual Assembly Modelling and Simulation for Ergonomics Analysis” was conducted across the Virtual Engineering Centre (VEC) and the School of Engineering.

She joined in the Department of Bioengineering, Imperial College London, as a research associate in 2014. Her research focused on the development and validation of personalised musculoskeletal model. The high-fidelity computational model has contributed to improve the movement strategies and rehabilitation technologies for patients with knee osteoarthritis and anterior cruciate ligament (ACL) injury. She joined in the Royal British Legion Centre for Blast Injuries Studies (CBIS), Imperial College London in 2015. Based on computational musculoskeletal modelling her research aimed to design and optimise the mitigation strategies for lower limb military amputees in order to reduce the injury burden in the amputated musculoskeletal system. She joined in the Department of Mechanical Engineering, University of Birmingham in 2019 as a lecturer.

Teaching

  • LI Mechanics 2
  • Advanced Mechanics
  • Bio-medical and Micro Engineering

Postgraduate supervision

Students with interests in computational biomechanics, human movement sciences, rehabilitation engineering, assistive technologies (including prosthetics and orthotics), wearable technologies, biomedical signal processing and control, and the application of AI in healthcare and biomechanics are encouraged to get in touch.

Research

Ziyun’s research applies model-based methodologies to study patient-specific mappings between locomotion, musculoskeletal system and mechanics, in order to design and develop clinically viable technologies for patient-accessible and patient-centred interventions, and targets at potential audients with lower limb musculoskeletal injury and disease.

Publications

Recent publications

Article

Kim, JM, Bian, Q, Martinez Valdes, E, Ding, Z & Yeo, S-H 2026, 'A multimodal gait dataset with ultrasound, EMG, and motion capture from young adults at various walking speeds', Scientific Data.

Wang, W, Bian, Q, Shepherd, D, Ge, Q, Bull, AMJ & Ding, Z 2026, 'An Optimal Control-Based Digital Computational Framework for Predicting the Entire Cycle Sit-to-Stand Motion in Unilateral Transtibial Amputees', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 14, pp. 1229-1238. https://doi.org/10.1109/TNSRE.2026.3666534

Zhao, H, Wei, G, Xie, J, Liu, A, Qu, Q, Cao, J, Ding, Z & Liao, W-H 2026, 'A predictive model of joint dynamics and ground reaction force using only leg length, body mass, and walking cadence', PLOS ONE, vol. 21, no. 1, e0338041. https://doi.org/10.1371/journal.pone.0338041

Li, Z, Huang, X, Ding, Z, Featherston, C, Evans, S, Zioupos, P & Wu, Z 2026, 'A robust topology optimization based biomechanical computational framework for patient-specific trabecular bone microstructure reconstruction', Computer Methods and Programs in Biomedicine. https://doi.org/10.1016/j.cmpb.2026.109309

Wu, P, Fang, J, Ding, Z, Guan, Z, Wang, J, He, Y, Zhang, Y & Zhang, H 2026, 'Comparative study of demographic information, clinical scales and questionnaires, and mobility tests for fall risk assessment in older adults', Healthcare and Rehabilitation, vol. 2, no. 1, 100069. https://doi.org/10.1016/j.hcr.2026.100069

Wang, H, Bian, Q, Zhou, H, Ge, Q, Lu, Z & Ding, Z 2026, 'Design Factors Affecting IMU-based Joint Torque Estimation Using Deep Learning', IEEE Sensors Journal. https://doi.org/10.1109/JSEN.2026.3688181

Rahman, SM, Khalil, MI, Zhou, H, Ding, Z & Guo, Y 2026, 'Exploring physical and functional EEG connectivity with multilayer graph transformer convolutional networks for emotion recognition', Frontiers in Human Neuroscience, vol. 19, 1715410. https://doi.org/10.3389/fnhum.2025.1715410

Wu, P, Dong, H, Ding, Z, Gao, T, Kong, W, Liu, Y, Song, R & Zhang, H 2026, 'Hierarchical multi-task learning for comprehensive gait assessment using wearable inertial sensors', npj Digital Medicine. https://doi.org/10.1038/s41746-026-02988-6

Hu, J, Ding, Z, Bian, Q, Alsayed, K, Bull, AMJ, Singleton, C & Ramamurthy, PH 2026, 'Model-driven design of electrical stimulation therapy improves gait dynamics in individuals with limb amputations', IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/TBME.2026.3669954

Bian, Q, Wang, H, Alsayed, K & Ding, Z 2026, 'OrientationNN: A physics-informed lightweight neural network for real-time joint kinematics estimation from IMU data', Frontiers in Bioengineering and Biotechnology, vol. 13, 1737916. https://doi.org/10.3389/fbioe.2025.1737916

Li, Z, Huang, X, Yuan, H, Wu, Z & Ding, Z 2026, 'Robust optimization of anisotropic porous hip implants for stress-shielding mitigation', International Journal of Mechanical Sciences, vol. 325, 111852. https://doi.org/10.1016/j.ijmecsci.2026.111852

Lukomiak, A, Bulathsinghala, R, Varga, J, Meng, S, Marasinghe, K, Refai, I, Fernando, B, Patel, A, Halloluwa-Arachchige, CM, Oliveira, C, Shahidi, AM, Rahemtulla, Z, Turner, A, Ding, Z, Liew, BXW, Kerr, A, Preatoni, E, Hughes-Riley, T, Dharmasena, I & Lugoda, P 2026, 'Texoskeletons: Developing the Fundamental Technologies for Creating Intelligent Soft Robotic Clothing With Integrated 1D Sensors and Actuators', Advanced Functional Materials. https://doi.org/10.1002/adfm.75714

Conference contribution

Guo, M, Cheng, X, Lu, W, Meng, Q, Yang, G, Cheng, Y, Ding, Z, Frangi, AF & Duan, J 2026, Foundation-Guided Representation Alignment for Multimodal Medical Image Registration. in Computer Vision – ECCV 2026: 19th European Conference, Malmö, Sweden, September 8-12, 2026, Proceedings, Part .... Lecture Notes in Computer Science, Springer, The 19th European Conference on Computer Vision, Malmö, Sweden, 8/09/26.

Review article

Kim, JM, Balthazaar, SJT, Alsayed, K, Nightingale, T, Falla, D, Yeo, S-H & Ding, Z 2026, 'Assessment of pain and functional outcomes after lower limb amputation: a scoping review', BMJ Open, vol. 16, e110319. https://doi.org/10.1136/bmjopen-2025-110319

M Atoar Rahman, S, Ibrahim Khalil, M, Zhou, H, Guo, Y, Ding, Z, Gao, X & Zhang, D 2025, 'Advancement in Graph Neural Networks for EEG Signal Analysis and Application: A Review', IEEE Access. https://doi.org/10.1109/ACCESS.2025.3549120

View all publications in research portal