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 
• describe the underlying control using the appropriate
  • analytical tools
  • modeling tools 

PREREQUISITES

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

TEACHING METHODS

The course combines lectures, guided lab activities, and analysis of movement data.

SYLLABUS/CONTENT

Part I: movement

• Human movement. Levels of description of movement: kinematic, kinetic, muscle mechanics. Terminology for describing human movements (limb movements, hand movements). Manipulation. Oral cavity and speech
• Movement kinematics. Rigid body kinematics. Kinematic chains. Reference frames and their parameterization. Quaternions as representations of 3D spatial geometry
• Motion capture technologies. Introduction to projective geometry. Stereophotogrammetry. Inertial measurement units. Markerlesssystems. Analysis of movement kinematics. Signal processing of kinematic data (low-pass filters, derivative filters)
• Movement dynamics. Rigid body mechanics. Newton-Euler and Lagrange Methods. Multi-joint movement statics. Dynamics: equations of motion of kinematic chains. Dynamometry. Force platforms. Mechanical impedance and its measure.
• Case study: locomotion. Gait kinematics and kinetics. Running, cycling, swimming.
• Muscle mechanics. Reminder of muscle physiology. Fiber types: intrafusal, extrafusal. Hill’s model of muscle force generation. Muscles and contraction types. Measures of muscle strength 

Part II: neural control of movement

• Neuroanatomy of the sensorimotor system. Cortico-spinal tract. Extrapyramidal system: cerebellum, basal ganglia. Somatosensory cortex, frontal cortex.
• Electromyography. Surface vs intramuscular. Analysis of EMG signals. Motor units and the size principle. 
• Computational motor control. Historical overview. The reflex arc. Central pattern generators. The reafference principle. Bernstein’s physiology of activity: motor equivalence. Marr’s computational 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.

FURTHER INFORMATION

Ask the teacher for any other information not provided here