OVERVIEW

The course provides the essential physiological background and knowledge of experimental methods and computational tools for understanding human movements and their neural control.

AIMS AND CONTENT

LEARNING OUTCOMES

The course covers the technologies, the analytical methods, the modeling approaches used for the analysis and quantification of human movement and its neural correlates. Specific topics include three-dimensional analysis of movements, muscle and body mechanics, physiology and physiological signals in motor control, computational motor control.

AIMS AND LEARNING OUTCOMES

By the end of the course the students will be able to record and analyse human movements (kinematics, kinetics, muscle activity) and to describe the underlying control, using the appropriate devices and analytical and modeling tools.

PREREQUISITES

There are no formal prerequisites, but students are expected to know mechanics, geometry, calculus and signal analysis. 

TEACHING METHODS

The course combines lectures, guided experimental sessions, and analysis of movement data.

SYLLABUS/CONTENT

Part I: movement

A. Human movement (wk 1). Levels of description of movement: kinematic, kinetic, muscle  mechanics. Terminology for describing human movements (limb movements, hand  movements). Manipulation. Skeletal anatomy.

​B. Movement kinematics (wk 2). Rigid body kinematics. Link-segment model. Kinematic chains. Reference frames and  their parameterization. Quaternions as representations of 3d spatial geometry.

C. Motion capture technologies (wk 3-4). Introduction to projective geometry. Stereophotogrammetry.Inertial measurement units. Markerless systems. Analysis of movement kinematics. Signal processing of kinematic data (low-pass filters, derivative filters)

D. Movement dynamics (wk 5-7). Reminder of muscle physiology. Fiber types: intrafusal, extrafusal. Hill’s model of muscle force generation. Muscles and contraction types. Measures of muscle strength. Rigid body mechanics: Newton-Euler and Lagrange Methods. Multi-joint movement statics. The redundancy problem. Dynamics: equations of motion of kinematic chains. Mechanical impedance and its measure.  Dynamometry. Case study: locomotion. Gait kinematics and kinetics. Running, cycling, swimming.

Part II: neural control of movement

E. Movement neurophysiology (wk 8-10) . Neuroanatomy of the sensorimotor system. Cortico-spinal tract. Extrapyramidal system: cerebellum, basal ganglia. Somatosensory cortex, frontal cortex.  Neuromuscular conrrol and size principle.  Electromyography. Surface vs intramuscular. Analysis of EMG signals.

F. Computational motor control (wk10-12). Historical overview. The reflex arc. Central pattern generators. The reafference principle. Bernstein’s physiology of activity: motor equivalence. Marr’scomputational framework. Movement psychophysics: movement time, spatio-temporal invariances. Multi-sensory and sensorimotor integration. Taking decisions and taking actions. Optimality in motor control. Motor learning. Sensorimotor adaptation. Implicit vs explicit learning. Savings. Joint Action. Optimality in joint action: game theory and Nash equilibria. Theory of mind. Learning in joint action.

RECOMMENDED READING/BIBLIOGRAPHY

Uchida TK, Delp SL (2021) Biomechanics of Movement. MIT Press. 

TEACHERS AND EXAM BOARD

LESSONS

LESSONS START

https://corsi.unige.it/11159/p/studenti-orario

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

Written exam (weight 60%) 

Lab Activities (weight 40%)

ASSESSMENT METHODS

The written exam will consist of questions and problems on lecture topics. 

There will be four lab activities which will be carried out as teams. Each activity will involve recording a specific movement, analyzing the recorded data and preparing and submitting a technical report.