Performance site: Campus Biotech, Geneva
The human movement is defined by a complex and redundant musculoskeletal system. Yet, the central nervous system (CNS) controls it in a robust and precise manner for healthy people. To provide such a level of control, the CNS possibly simplify the control of movement via an activation of group of muscle synergies instead of activating each muscle independently where the muscle synergies would be specifying a defined balance of activation across a set of muscles. Such a low dimensional control method for the movement of a frog hindlimb was developed by Berniker (2008) in an efficient manner, whereas Neptune (2009) developed for the human walking locomotion a strategy involving five co-active muscles that could be sufficient to perform a walking task.
The goal of our research would then to build a synergy-based controller for the human upper limbs to get a clearer idea of how human motor control works. For that purpose, as no model existing in the state of the art provides an EMG based forward dynamics with a full level of control, such a musculoskeletal model of the upper limb needs to be developed.
Having a synergy based controller would then open us to a large range of applications, from the scientific verification of the CNS use of muscle synergies, the evaluation of the alteration of numbers and structure of muscle synergies after a neurological disease, its use with functional electrical therapies for the generation of useful forces in an efficient way, to the to a simplification of motor control without impairing performances.
Project description: In the overall forward dynamic framework needed for the synergy-based controller, muscles parametrization development in order to obtain correct EMG signals from muscles forces and vice versa is needed. The student’s main task will be to extend the existing muscle model to each of the predefined muscles acting on a specified joint by obtaining and tuning the muscle parameters as described by Garner and Pandy (2003)
- Familiarize with the hill type muscle model, the different muscle force-length, force-velocity, muscle passive force-length and tendon force length relationships as well as the different parameters acting on them.
- Familiarize with the musculotendon properties estimation method developed by Garner and Pandy (2003)
- Define for each parameter a realistic value interval
- Familiarize with the existing model code
- Develop innovative strategies to optimize each parameter
- Adapt the existing code to newer Matlab versions if possible
- Basic knowledge of biomechanics
- Basic Matlab skills
- Experience in non-linear control is a plus
Best for: semester / master project (to be discussed)
Contact: [email protected]
- Sarshari, E. (2018). A Closed-Loop EMG-Assisted Shoulder Model (Thesis No. 8658). EPFL.
- A. Garner, M. G. Pandy, Estimation of musculotendon properties in the human upper limb, Annals of biomedical engineering 31 (2) (2003) 207–220.
- Hill, The heat of shortening and the dynamic constants of muscle, in: Proc. R. Soc. Lond. B, Vol. 126, The Royal Society, 1938, pp. 136–195.
- Ingram, Musculoskeletal model of the human shoulder for joint force estimation, Ph.D. thesis (2015).
If none of the projects suit you but you are interested in musculo-skeletal models in general, please feel free to contact us to discuss potential opportunities.
Tristan Barjavel ([email protected])