350154 VU BD2II - Biomechanical Motion Analysis in Practice (2023W)
Continuous assessment of course work
Labels
Registration/Deregistration
Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
- Registration is open from Mo 04.09.2023 09:00 to We 20.09.2023 12:00
- Registration is open from Mo 02.10.2023 09:00 to Fr 06.10.2023 12:00
- Deregistration possible until Tu 31.10.2023 12:00
Details
max. 20 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
The class of 12th October will not take place
Thursday
05.10.
12:30 - 14:00
ZSU - USZ II, Biomechanisches Labor, 2. Stock
ZSU - USZ II, EDV Raum, 2. Stock
ZSU - USZ II, EDV Raum, 2. Stock
Thursday
12.10.
12:30 - 14:00
ZSU - USZ II, Biomechanisches Labor, 2. Stock
ZSU - USZ II, EDV Raum, 2. Stock
ZSU - USZ II, EDV Raum, 2. Stock
Thursday
19.10.
12:30 - 14:00
ZSU - USZ II, Biomechanisches Labor, 2. Stock
ZSU - USZ II, EDV Raum, 2. Stock
ZSU - USZ II, EDV Raum, 2. Stock
Thursday
09.11.
12:30 - 14:00
ZSU - USZ II, Biomechanisches Labor, 2. Stock
ZSU - USZ II, EDV Raum, 2. Stock
ZSU - USZ II, EDV Raum, 2. Stock
Thursday
16.11.
12:30 - 14:00
ZSU - USZ II, Biomechanisches Labor, 2. Stock
ZSU - USZ II, EDV Raum, 2. Stock
ZSU - USZ II, EDV Raum, 2. Stock
Thursday
23.11.
12:30 - 14:00
ZSU - USZ II, Biomechanisches Labor, 2. Stock
ZSU - USZ II, EDV Raum, 2. Stock
ZSU - USZ II, EDV Raum, 2. Stock
Thursday
30.11.
12:30 - 14:00
ZSU - USZ II, Biomechanisches Labor, 2. Stock
ZSU - USZ II, EDV Raum, 2. Stock
ZSU - USZ II, EDV Raum, 2. Stock
Thursday
07.12.
12:30 - 14:00
ZSU - USZ II, Biomechanisches Labor, 2. Stock
ZSU - USZ II, EDV Raum, 2. Stock
ZSU - USZ II, EDV Raum, 2. Stock
Thursday
14.12.
12:30 - 14:00
ZSU - USZ II, Biomechanisches Labor, 2. Stock
ZSU - USZ II, EDV Raum, 2. Stock
ZSU - USZ II, EDV Raum, 2. Stock
Thursday
11.01.
12:30 - 14:00
ZSU - USZ II, Biomechanisches Labor, 2. Stock
ZSU - USZ II, EDV Raum, 2. Stock
ZSU - USZ II, EDV Raum, 2. Stock
Thursday
18.01.
12:30 - 14:00
ZSU - USZ II, Biomechanisches Labor, 2. Stock
ZSU - USZ II, EDV Raum, 2. Stock
ZSU - USZ II, EDV Raum, 2. Stock
Thursday
25.01.
12:30 - 14:00
ZSU - USZ II, Biomechanisches Labor, 2. Stock
ZSU - USZ II, EDV Raum, 2. Stock
ZSU - USZ II, EDV Raum, 2. Stock
Information
Aims, contents and method of the course
Assessment and permitted materials
In the case of courses with continuous assessment in the winter semester, the entire performance by students must be completed by the following 30 April at the latest, and by 30 September at the latest in the case of courses with continuous assessment in the summer semester. Students who are not interested in the ggst. have deregistered from the course, are to be assessed. In the event of a negative assessment, a board examination is inadmissible, the attendance of the course must be repeated. Source of law: Statutes of the University of Vienna §10 (4, 5, 6).You are expressly informed that if a fraudulent service is detected (e.g.: copying, plagiarism, use of unauthorized aids, forgery, ghostwriting, etc.), the entire PI-LV will be considered cheated and counts as an attempt. (Entry in U:SPACE: X = not judged)
Minimum requirements and assessment criteria
25% attendance + quiz (2.5% per class)75% final abstract (60%) + presentation (15%)
- Groups of 2-3 for the experiments
- Topics to be chosen by the students
- 1 page abstract (1 per student)
- 3-5 min presentation (1 per student)
- Groups of 2-3 for the experiments
- Topics to be chosen by the students
- 1 page abstract (1 per student)
- 3-5 min presentation (1 per student)
Examination topics
Students chose between:
- Ultrasonography
- Electromyography / muscle stimulation
- Motion analysis (kinematics and kinetics)
- Strength measurements
- Musculoskeletal modelling
- Machine learning
- Ultrasonography
- Electromyography / muscle stimulation
- Motion analysis (kinematics and kinetics)
- Strength measurements
- Musculoskeletal modelling
- Machine learning
Reading list
Topic reads:
Vigotsky, A. D., Halperin, I., Lehman, G. J., Trajano, G. S., & Vieira, T. M. (2018). Interpreting signal amplitudes in surface electromyography studies in sport and rehabilitation sciences. Frontiers in Physiology, 8(JAN). https://doi.org/10.3389/fphys.2017.00985Franchi, M. V., Raiteri, B. J., Longo, S., Sinha, S., Narici, M. V., & Csapo, R. (2018). Muscle Architecture Assessment: Strengths, Shortcomings and New Frontiers of in Vivo Imaging Techniques. Ultrasound in Medicine and Biology. https://doi.org/10.1016/j.ultrasmedbio.2018.07.010Lloyd, D. (2021). The future of in-field sports biomechanics: wearables plus modelling compute real-time in vivo tissue loading to prevent and repair musculoskeletal injuries. Sports Biomechanics, 1–29. https://doi.org/10.1080/14763141.2021.1959947Saxby, D. J., Killen, B. A., Pizzolato, C., Carty, C. P., Diamond, L. E., Modenese, L., Fernandez, J., Davico, G., Barzan, M., Lenton, G., da Luz, S. B., Suwarganda, E., Devaprakash, D., Korhonen, R. K., Alderson, J. A., Besier, T. F., Barrett, R. S., & Lloyd, D. G. (2020). Machine learning methods to support personalized neuromusculoskeletal modelling. Biomechanics and Modeling in Mechanobiology, 19(4), 1169–1185. https://doi.org/10.1007/s10237-020-01367-8
Vigotsky, A. D., Halperin, I., Lehman, G. J., Trajano, G. S., & Vieira, T. M. (2018). Interpreting signal amplitudes in surface electromyography studies in sport and rehabilitation sciences. Frontiers in Physiology, 8(JAN). https://doi.org/10.3389/fphys.2017.00985Franchi, M. V., Raiteri, B. J., Longo, S., Sinha, S., Narici, M. V., & Csapo, R. (2018). Muscle Architecture Assessment: Strengths, Shortcomings and New Frontiers of in Vivo Imaging Techniques. Ultrasound in Medicine and Biology. https://doi.org/10.1016/j.ultrasmedbio.2018.07.010Lloyd, D. (2021). The future of in-field sports biomechanics: wearables plus modelling compute real-time in vivo tissue loading to prevent and repair musculoskeletal injuries. Sports Biomechanics, 1–29. https://doi.org/10.1080/14763141.2021.1959947Saxby, D. J., Killen, B. A., Pizzolato, C., Carty, C. P., Diamond, L. E., Modenese, L., Fernandez, J., Davico, G., Barzan, M., Lenton, G., da Luz, S. B., Suwarganda, E., Devaprakash, D., Korhonen, R. K., Alderson, J. A., Besier, T. F., Barrett, R. S., & Lloyd, D. G. (2020). Machine learning methods to support personalized neuromusculoskeletal modelling. Biomechanics and Modeling in Mechanobiology, 19(4), 1169–1185. https://doi.org/10.1007/s10237-020-01367-8
Association in the course directory
BD2II
Last modified: Mo 23.10.2023 11:08