ECTS credits ECTS credits: 3
ECTS Hours Rules/Memories Hours of tutorials: 4.5 Expository Class: 15 Interactive Classroom: 13 Total: 32.5
Use languages Spanish, Galician
Type: Ordinary subject Master’s Degree RD 1393/2007 - 822/2021
Departments: Electronics and Computing, External department linked to the degrees
Areas: Computer Science and Artificial Intelligence, Área externa M.U en Internet de las Cosas - IoT
Center Higher Technical Engineering School
Call: Second Semester
Teaching: With teaching
Enrolment: Enrollable | 1st year (Yes)
This compulsory subject falls under the Society 5.0 specialty.
It focuses on the intersection of Internet of Things (IoT) technology and healthcare, providing students with the knowledge and skills to innovate in healthcare through smart technologies. It combines knowledge from engineering, computer science, biomedicine and healthcare management to address modern healthcare challenges through innovative technological solutions.
It does this by on the one hand providing a thorough understanding of IoT concepts and technologies, including sensors, devices, data communication and integration platforms. In addition, in parallel, teach techniques for analyzing large volumes of data (Big Data) and the use of artificial intelligence (AI) and machine learning (Machine Learning) to improve decision making in health.
This is a subject with a markedly practical nature
The contents of this subject include the following thematic blocks:
1. Communication technologies for IoT systems for Smart Health.
2. Wearables in health.
3. Personal health devices: examples and protocols.
4. Self-quantification.
5. Participatory health.
6. Localization of personnel, assets, patients and medications in the healthcare environment.
Internships will consist of Facilitating learning through hands-on projects and real case studies that allow students to apply their knowledge in real healthcare settings.
Basic bibliography:
• Awasthi, S., Naruka, M. S., Yadav, S. P., & De Albuquerque, V. H. C. (Eds.). (2023). AI and IoT-based intelligent health care & sanitation. Bentham Science Publishers.
• Chakraborty, C., Banerjee, A., Kolekar, M. H., Garg, L., & Chakraborty, B. (Eds.). (2021). Internet of things for healthcare technologies. Springer.
• Reddy, C. K., & Aggarwal, C. C. (Eds.). (2015). Healthcare data analytics. CRC Press
• Singh, S., Biwalkar, A., & Vazirani, V. (2021). Clinical Decision Support Systems and Computational Intelligence for Healthcare Industries. In Knowledge Modelling and Big Data Analytics in Healthcare (pp. 37-63). CRC Press.
Complementary bibliography:
• Edemekong, P. F., Annamaraju, P., & Haydel, M. J. (2018). Health insurance portability and accountability act.
• GDPR, G. D. P. R. (2018). General data protection regulation. URL: https://gdpr-info. eu/[accessed 2020-11-21].
• International Organization for Standardization. (2013). ISO/IEC 27001: 2013: Information Technology--Security Techniques--Information Security Management Systems--Requirements. International Organization for Standardization
HBL12 - Apply acquired knowledge and solve problems in new or unfamiliar environments within broader, multidisciplinary contexts, being able to integrate knowledge.
S-CN1: Know and understand the basic fundamentals of IoT technologies for communication, traceability and wearables for self-quantified, participatory and intelligent health.
S-HB1: Program and deploy IoT wearables for health.
S-HB2: Apply statistical techniques to large-scale IoT datasets and for Society 5.0 applications.
S-CP2: Implement video processing and analysis algorithms for Society 5.0 applications.
S-CP3: Design and use IoT systems for asset location in healthcare environments.
S-CP4: Design and deploy large-scale IoT data processing systems for Society 5.0 applications.
- Theory classes/Master class
- Practical laboratory classes/Laboratory practicals
- Supervised work/Self-employment work
- Final Exam (Value between 10%-40% of the overall grade)
- Continuous monitoring (5%-35%)
- Evaluation of practical work (10%-40%)
- Evaluation of tutored work (10%-40%)
- Theory classes/Master class - 24 classroom hours + 48 non-classroom hours
- Practical laboratory classes/Laboratory practicals - 12 face-to-face hours + 20 non-face-to-face hours
- Supervised work/Self-guided work - 0 classroom hours + Z non classroom hours
None
None
Sonia Maria Valladares Rodriguez
Coordinador/a- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- sonia.valladares [at] usc.es
- Category
- Professor: LOU (Organic Law for Universities) PhD Assistant Professor
| Tuesday | |||
|---|---|---|---|
| 15:30-17:00 | Grupo /CLE_01 | - | Aula A10 |
| 17:00-18:30 | Grupo /CLIL_01 | Spanish | Classroom A7 |