Teachers
Federico Gennaro
Department of Biomedical Sciences, Università degli Studi di Padova,
federico.gennario[at]unipd.it
M-EDF/01

Aim
This course aims to provide basic cross-disciplinary knowledge based on the intersection of different topics: human movement sciences, neurophysiology, and computer sciences. Theory and, when possible, practical hands-on sessions will provide the necessary toolkit for a sound basic understanding of wearable sensors and big data analysis methods applied to sport performance analysis. On successful completion of this course, students should be able to:
1) Provide a brief overview of sport techniques and technologies applied to sport performances.
2) Demonstrate basic knowledge on the materials and methods employed for sport performance analysis, including but not limited to neurophysiological and kinematic wearable sensors as well as big data analysis approaches (e.g., machine learning, artificial intelligence).
3) Develop a sound basic understanding on how to interpret and compare different outcomes from diverse techniques and technologies for sport performances analyses.

Syllabus
 - 5 “Ws” of sport technology for sport performance analysis in team and individual sports. Theory session. 
- Techniques and technologies of sport performances analysis by external workload (e.g., Inertial Measurement Units). Theory and/or practical session.
- Techniques and technologies of sport performances analysis by internal workload (e.g., wireless surface electromyography and electroencephalography). Theory and/or practical session.
- Big Data, Machine Learning and Artificial Intelligence applied to sport technology for sport performance analysis. Theory and/or practical session. 
- Choosingtherightsporttechniqueandtechnologyforperformanceanalysis: focus on the interpretation of different outcomes. Theory session.

Introductory readings
Pino-Ortega, J., & Rico-González, M. (Eds.). (2021). The use of applied technology in team sport. Routledge. Lectures’ presentations slides will be provided. A supplementary reading list will be provided during the course, which details the book(s) chapter(s) or research article(s) that complement each lecture.

Course requirements
None

Examination modality
None

Course material, enrollment and last minute notifications
Made available by the teacher at this Moodle address

Schedule
14 May 2023, 9:30-13:30
15 May 2023, 9:30-13:30
16 May 2023, 9:30-13:30

Location
Room 2AB40 at the Dept. of Mathematics

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