In this course, you learn the mechanisms/processes (and their interactions) used by the Central Nervous System to deal with the major challenges in controlling human movement. Hereto your will get introduced to the recent theories and models on how humans integrate sensory information to improve the estimation of their body posture, and how this information is used to estimate, control and adapt movement.|
More specifically, after this course the student is able to:
- Predict the response of the human sensors and actuators to different inputs/stimuli using their working mechanisms
- Explain the effect of changes in each of the two pathways that are involved in controlling posture on the stability of the system and the sensitivity for perturbations.
- Explain how Bayesian inference, multisensory integration and Kalman filtering improve the body state estimation and indicate their differences and similarities
- Predict the course of motor adaptation to kinematic and dynamic perturbations using the multi-rate and V-shaped learning model
- Explain how movements are controlled within the scope of optimal feedback control and how changes in costs can affect the movement control
- Reason how impairments of different processes involved in motor control due to neuromuscular disorders affect postural and movement control and motor adaptation.
- Derive mathematical models of postural control and control of movement and use these models in MATLAB to increase insight in these processes and make predictions.
Different neuromuscular systems are involved in the control of human movement. These systems include the different sensory systems (visual, proprioceptive, vestibular), the central nervous system and the muscles. This course discusses the role of the separate systems and their interactions in motor control with an emphasis on integration of sensory information, postural control and control and adaptation of reaching movements. Obtained knowledge can be applied in the development of treatments and assessment methods for diagnosis in neurology and (neuro)rehabilitation. In this course student will obtain knowledge about the physiological and computational mechanisms involved in movement control through lectures and self study and will learn to make use of engineering skills/tools in assignments and a practical to better understand the importance of the different involved processes.|
Regarding the required background skills and knowledge, you should be able to
- Make a free body diagram of a particular system including all forces and moments working on the system
- derive the equations of motions for a particular system,
- linearize a nonlinear differential equation around an operating point
- transform the different representations of a linear time invariant (LTI) system in each other (differential equation, state space, transfer function),
- know how the Bode Diagram and step response of standard LTI systems (integrator, differentiator, time delay, 1st order system) look like,
- indicate the effect of mass, damping coefficient and spring constant of a mass-spring-damper system on the response of this system in the time and frequency domain,
- know how relative damping, the system gain and eigen frequency can be recognized in a time and/or frequency response,
- determine the stability of a system using the Bode diagram/Nyquist diagram and poles and zeros.