https://www.research.manchester.ac.uk/portal/en/researchers/lei-ren(39bc9c39-5f83-4aa9-a144-118c8bdff337).html
Email: lei.ren@manchester.ac.uk
Lei Ren received his B.Eng., M.Eng. in Mechanical Engineering and Ph.D. in Vehicle Engineering from National Laboratory of Automotive Dynamic Simulation, Jilin University, China. He then came to Centre for Rehabilitation and Human Performance Research, University of Salford, and worked on a UK Ministry of Defense project on human locomotor biomechanics, and received a Ph.D. in Biomechanics. Thereafter, he worked at Structure and Motion Laboratory, Royal Veterinary College, University of London, as a BBSRC research fellow on comparative musculoskeletal biomechanics. From 2007 to 2010, he was with Centre for Robotics Research, Division of Engineering, King's College London as a lecturer. Dr. Ren joined the School of MACE, University of Manchester in 2010, and also works as a research scientist at Structure and Motion Laboratory, University of London. He is the founding member of the International Society of Bionic Engineering, and also a member of the International Society of Biomechanics, European Society of Biomechanics, the Society of Experimental Biology and European Society for Movement Analysis in Adults and Children.
As a mechanical engineer with biomedical background, I am fascinated by the studies of biomechanics and neural control of human and animal movements using mathematical, physical, robotic and physiological approaches. The long term aim is to develop biologically sound computational, physical and robotic models to reveal the fundamental musculoskeletal, neuromotor and sensorimotor principles underlying human and animal movements, and also to apply this knowledge to medical and health sciences, clinical diagnosis and interventions, biologically inspired robotics, healthcare robotics, medical devices and assistive technologies etc. Some of the areas that we are particularly interested in recently are:
Biomechanics and Neural Control
Fundamental biomechanical and neuromotor control principles underlying human locomotion and hand manipulation; Three-dimensional motion analysis of biological movements using infrared camera array, portable sensing systems and dual plane X-ray systems; Biomechanics of tactile sensing during hand manipulation; Predictive musculoskeletal modelling of human locomotion; Continuum mechanics modelling of musculoskeletal complexes, e.g. hand, foot, knee, shoulder etc.; Multi-domain modelling of neuromechanical dynamics and sensorimotor control.
Biologically Inspired Robotics
Humanoid walking robots inspired from human musculoskeletal biomechanics; Biologically inspired anthropomorphic robotic hands; Biologically inspired energy-efficient lower limb prosthetics; Biologically inspired prosthetic hand system driven by muscle EMG signal; CNS-controlled lower limb prosthesis driven by muscle EMG signals; Biologically inspired exoskeleton system capable of enhancing human walking; Muscle-like soft actuators for biomedical applications; Biologically inspired soft robotics for biomedical applications.
Orthopaedic Biomechanics
In-vivo diagnosis of musculoskeletal disorders using integrated approaches based on data fusion from multiple physical domains; Biomechanical testing of orthopaedic implants and arthroplasty; Computational modelling of orthopaedic implants and arthroplasty; Computer-aided surgical planning based on musculoskeletal modelling; Computer aided design of implants and arthroplasty based on musculoskeletal modelling.
1.Biomechanical effect of threading on proximal humeral locking screws: a finite element study
Le, L., Jabran, A., Peach, C. & Ren, L., 2019, In : Medical Engineering and Physics. 63, p. 79-87
2.Biomimetic Shape-Color Double-Responsive 4D Printing.Wang, J., Wang, Z., Song, Z., Ren, L., Liu, Q. & Ren, L., 2019, In : Advanced Materials Technologies. p. 1900293
Research output: Contribution to journal › Article
3.High Performance Ionic Polymer Metal Composite: Toward Large Deformation Fast Response Artificial Muscles. Ma, S., Zhang, Y., Liang, Y., Ren, L., Tian, W. & Ren, L., 2019, In : Advanced Functional Materials. p. 1-9 9 p., 1908508.
4.Intent Prediction of Multi-axial Ankle Motion Using Limited EMG Signals
Gregory, U. & Ren, L., 2019, In : Frontiers in Bioengineering and Biotechnology. 7
Research output: Contribution to journal › Article
5.Non-invasive quantitative assessment of muscle force based on ultrasonic shear wave elastography.Liu, J., Qian, Z., Wang, K., Wu, J., Jabran, A., Ren, L. & Ren, L., 2019, In : Ultrasound in Medicine and Biology. 45, 2, p. 440-451 11 p.
