MOTOR CONTROL AND HUMAN PERFORMANCE ASSESSMENT

MOTOR CONTROL AND HUMAN PERFORMANCE ASSESSMENT

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iten
Code
80580
ACADEMIC YEAR
2017/2018
CREDITS
6 credits during the 1st year of 8725 Bioengineering (LM-21) GENOVA

6 credits during the 2nd year of 9913 DIGITAL HUMANITIES - COMMUNICATION AND NEW MEDIA (LM-92) GENOVA

SCIENTIFIC DISCIPLINARY SECTOR
ING-INF/06
LANGUAGE
Italian
TEACHING LOCATION
GENOVA (Bioengineering)
semester
2° Semester
Teaching materials

AIMS AND CONTENT

AIMS AND LEARNING OUTCOMES

To provide the essential physiological background and knowledge of

– experimental methods

– computational tools

for the analysis of human movements, their neural correlates and other related aspects of human performance

Teaching methods

Combination of lectures, exercises, guided lab activities (App Inventor)

SYLLABUS/CONTENT

•    Introduction (wk 1). Levels of description of movement: kinematic, kinetic, muscle mechanics, physiology. Overview of movement analysis techniques. Reminder of the physiological basis of movement, perception and cognition. Neuroanatomy of the sensorimotor system. Motor control theories.
•    Muscle mechanics and control (wk 2-3). Hill’s model of muscle force generation. The size principle. Muscle fatigue. Electromyography and EMG-related measures of performance.
•    Single-joint neuromechanics (wk 4). Joint kinematics, joint dynamics, agonist and antagonist muscles, the motor servo, neuromuscular viscoelasticity.
•    Multi-joint movements (wk 5-6). Kinematics: trajectories, joint rotations, forward and inverse kinematics, Jacobians. Dynamics: equations of motion of kinematic chains,  mechanical impedance. Examples: arm movements and manipulation; orofacial control.
•    Computational motor control (wk 7-8). Trajectory formation. Dynamics control. Multisensory integration and Sensorimotor integration. Feedforward and feedback control modalities.
•    Motor learning (wk 9-10). Sensorimotor adaptation to kinematic/dynamic disturbances. Computational models of adaptation. Motor skill learning. Computational models of skill learning
•    Movement kinetics (wk 11-12). Case studies in locomotion: gait, running, cycling, swimming. Energy-related performance measurements: metabolic energy consumption.
•    App Design Lab (lab activity). Four guided lab activities plus individual project work

TEACHERS AND EXAM BOARD

Ricevimento: on demand, by e-mail to: vittorio.sanguineti@unige.it or mobile phone at: 3292104393. Teacher office: via All’Opera Pia 13, building E, fourth floor. Office direct phone number: 010-3536487

Exam Board

VITTORIO SANGUINETI (President)

MARCO MASSIMO FATO

MAURA CASADIO

LESSONS

Teaching methods

Combination of lectures, exercises, guided lab activities (App Inventor)

ORARI

L'orario di tutti gli insegnamenti è consultabile su EasyAcademy.

Vedi anche:

MOTOR CONTROL AND HUMAN PERFORMANCE ASSESSMENT

EXAMS

Assessment methods

•    Written examination (weight 50%)
•    Project work (individuals or couples, weight 50%):  using App Inventor, develop a smartphone/tablet app for monitoring some form of user’s perceptual or motor performance