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Bionic Systems

Code 13509
Year 1
Semester S1
ECTS Credits 6
Workload TP(60H)
Scientific area Biomedical Sciences
Entry requirements None
Mode of delivery Face-to-face.
Work placements N/A.
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.
Apply the knowledge about control and electronics to bionic systems.
Understand the human movement patterns, in order to design systems which repeat it.
Know the different type of materials with application in bionics.
Know the different type of drives with application in bionics.
Understand the design of artificial limbs.
Understand and apply different control methods.
Interpret experimental results.
Use systems simulation software.
Know and apply the principles of ethics and bioethics.
Work individually and within a team.
Develop autonomy and leadership abilities.
Know how to read and write scientific works.
Syllabus • Introduction to Bionics.
• The human being in a technological perspective. Intelligent clothing and wearable wireless sensors.
• The ortophedic engineering.
• Human tissue substitution. Materials used in bionic systems. Bio-inspired materials - recyclable materials and intelligent materials. Selection of materials.
• Technologies to design sensorial systems. Sensors in the movement system and in artificial organs.
• The study of the human motor system, particularly the muscle and nervous reflex functioning with respect to movement. Micromachines used in the movement systems.
• Movement pattern recognition in view of its reproduction.
• The control and design of artificial limbs. The Fuzzy Control.
• Electrical stimulation.
• Neural recording.
• Computer control. Artificial Neural networks(RNA)
• The use of implants for artificial hearing and vision.
• Electromagnetic interferences.
• Ethical aspects. Bioethics.
Main Bibliography “Pattern Recognition and Machine Learning (Information Science and Statistics)”, Christopher M. Bishop, Springer, 1st Ed., 2007.
“Building a Digital Human (Graphics Series)”, Ken Brilliant, Charles River Media, 1st Ed., 2003.
“Control Theory for Humans: Quantitative Approaches To Modeling Performance”, Richard J. Jagacinski, John M. Flach, CRC, 1st Ed., 2002.
“Artificial Intelligence, A Modern Approach”, Stewart Russell & Peter Norvig, 2nd Ed, Pearson Education Inc., 2003.
“Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering”, MIT Press, Nikola K. Kasabov, Cambridge, MA, USA, Hardcover, Oct 1996.
“Biomechanics and Motor Control of Human Movement”, David A. Winter, Wiley, 4th Ed, 2009.
“The Human Nervous System: Structure and Function”, Charles R. Noback (Editor), David A. Ruggiero, Robert J. Demarest, Norman L. Strominger (Editors), Humana Press, 6th Ed., 2005.
“Biomedical Ethics”, Thomas Mappes, David DeGrazia, McGraw-Hill, 6th Ed., 2005.
Teaching Methodologies and Assessment Criteria The theoretical classes will cover the topics of the program and the student will be evaluated by several short tests along of the teaching period, and that will constitute the ‘continuous evaluation’ (CE) of the student.
Every student has to prepare during the semester, under the supervision of the lecturer of the discipline, a work/small project (PR) and make a PowerPoint presentation in class to the other students attending the discipline.
The practical-laboratory classes (PL) are devoted to performing several experiments/tests and simulations in computer using software such as Matlab, Excel and others.
The evaluation consists in 3 parts (already described above):
a) student work/project (PR, 60%, 12 values in 20),
b) continuous evaluation (CE, 30%, 6 values in 20), and
c) practical-laboratorial (PL, 10%, 2 values in 20).
Language Portuguese. Tutorial support is available in English.
Last updated on: 2020-01-17

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