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Learning outcomes |
To study and look to the human being in a technological prospective, referring the movement pattern recognition, the project and control of artificial limbs, the computer based control, the nervous systems and memory, the orthopedic and tissue engineering, the implants and ethical aspects.
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Main Bibliography |
1. Fundamentals of Machine Learning and Deep Learning in Medicine, Reza Borhani, Soheila Borhani and Aggelos K. Katsaggelos, Springer; 1st Edition, 2022.
2. Building a Digital Human (Graphics Series), Ken Brilliant, Charles River Media, 1st Ed., 2003.
3. Control Theory for Humans: Quantitative Approaches To Modeling Performance, Richard J. Jagacinski, John M. Flach, CRC, 1st Ed., 2002.
4. Artificial Intelligence: What You Need to Know About Machine Learning, Robotics, Deep Learning, Recommender Systems, Internet of Things, Neural Networks, Reinforcement Learning, and Our Future, Neil Wilkins, 2019.
5. Biomechanics and Motor Control: Defining Central Concepts, Mark L. Latash, Vladimir Zatsiorsky, Kindle, 1st Edition, Independently published, 2015.
6. The Digital Health Revolution, Kevin Pereau and Barry Lenson, TranscendIT Health, 2019.
7. Biomedical Sensing and Analysis: Signal Processing in Medicine and Biology, Iyad Obeid, Joseph Picone and Ivan Selesnick,? Springer, 1st Edition, 202
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Teaching Methodologies and Assessment Criteria |
The theoretical-practical classes aim to transmit theoretical knowledge and provide examples with practical applications. Students develop their learning not only in an accompanied way, but also in a self-sustained way, using the pedagogical resources available (books, support texts, websites and simulation and circuit design software). Furthermore, the student will develop a project of a concept, technology or system. Students are evaluated throughout the semester and following the model: 5 seminars and reports (CM - average classification of the 5 works); 1 small project (CP). The final classification (CF) will be: CF=70% CM + 30% CP.
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