It is advisable to have taken all the subjects of the subject in which the collaboration is taking place. The subjects of the Health Engineering degree (GES) are: Mathematics, Computer Science, Physics, Biochemistry, Electronics, Communications, Medical Signals and Images, Medical Informatics, Biostatistics, Clinical Fundamentals, Management and Research.
1. Apply the knowledge acquired during the degree program in an integrated manner to solve a real need in the healthcare sector from the perspective of medical and health engineering.
2. Be able to approach assigned projects by integrating the relevant variables of sex and gender into the problem or topic presented.
3. Plan, develop, and present research projects applied to Health Engineering.
4. Be able to work collaboratively and creatively in multidisciplinary environments.
The content for each student can vary depending on the purpose of the project or task, focusing on a health-related application. Examples include the development of hardware prototypes, algorithms, software tools, and theoretical-practical teaching materials; research into anatomical, physiological, and pathophysiological processes; the design of experiments; signal and data acquisition/recording; signal and image processing; database management; 3D modeling/printing; biomaterial characterization; and computational simulation, among others. The course also provides an opportunity to integrate new content from related subjects within Health Engineering that are not included in the curriculum, allowing students to explore these topics during academic exchanges at national and international institutions
This course has a strong practical component and aims to prepare students to collaborate in professional settings, addressing real-world challenges in the healthcare sector. It is developed by combining guidance and support from the instructor with practical activities to solve challenges presented to groups of 2 to 4 students.
The following teaching methodologies are used:
- Project-Based Learning (PBL): each team develops a realistic project with verifiable results (hardware and/or software prototype, experiment, model, etc.).
- Hands-on laboratory learning: experimental sessions focused on learning by doing.
- Short seminars and micro-lessons to cover key concepts just in time ("just-in-time teaching") to facilitate progress.
- Academic and technical tutoring: formative follow-up with actionable feedback geared towards improvement. - Cooperative learning: rotating roles (by week and/or by type of assessment), co-responsibility (each role has its own and shared tasks: "the result belongs to everyone"), collaborative problem-solving (use of group thinking and decision-making techniques: brainwriting, brainstorming, etc.), and peer assessment (anonymous evaluation of each presentation, both individually and by peers).
- Continuous assessment through monitoring of individual and group performance.
Use of AI in learning: allowed with explicit declaration of its use and prompts included in annexes of deliverables. Work must be original, traceable, and understandable by its author(s).
Each student's work is evaluated based on their ability to respond to the problems presented, considering their capacity to define how to guide the work, its development, and the results. Personal involvement and attitude are also valued. The evaluation will be carried out by the person responsible for supervising the student groups. This person will monitor and provide ongoing individual assessment of each student's contribution.
The course grade is calculated based on the completion of project tasks, both individually (90%) and in groups (10%). Individual assessment is distributed as follows: 5% for each participant's individual attitude (meeting attendance, active participation, and communication skills), 80% for the results obtained in assigned tasks, 5% for the final individual presentation, and 10% for the overall achievement of the project objectives (including group organization and cohesion).
Students who do not pass the course in the regular examination period will have an extraordinary examination period. In this period, it will be necessary to submit and present a new version of the project, including the methods, results, and solutions for the individual and group tasks in which the student participates. If more than one student on the team has failed, a single document with the team's final version may be submitted and presented. If the supervisor deems it necessary, they may, as an alternative, evaluate the extraordinary call, conducting a theoretical and practical exam on the knowledge of the project contents.
1. Creswell, J. W., & Creswell, J. D. (2023). Research design: Qualitative, quantitative, and mixed methods approach (6th ed.). SAGE.
2. Hernández-Sampieri, R., & Mendoza, C. (2018). Metodología de la investigación: Las rutas cuantitativa, cualitativa y mixta. McGraw-Hill.
3. Browner, W. S., Newman, T. B., Cummings, S. R., & Grady, D. G. (2022). Designing clinical research (5th ed.). Wolters Kluwer.
4. Ng, S. I., Xu, L., Siegert, I., Cummins, N., Benway, N. R., Liss, J. y Berisha, V. (2024). Un tutorial sobre el desarrollo de IA clínica del habla: desde la recopilación de datos hasta la validación del modelo. Preimpresión de arXiv arXiv: 2410.21640.
5. Ahluwalia, A., De Maria, C., & Díaz Lantada, A. (eds.) (2022). Engineering Open-Source Medical Devices: A Reliable Approach for Safe, Sustainable and Accessible Healthcare. Springer Cham. SpringerLink
6. Tranquillo, J., Goldberg, J., & Allen, R. (2022). Biomedical Engineering Design. Elsevier.
7. Bajaj, V., Sinha, G. R., & Chakraborty, C. (eds.) (2022). Biomedical Signal Processing for Healthcare Applications. CRC Press,
8. Señales biomédicas (EEG, EMG, ECG), machine learning aplicado a salud, procesamiento de datos.
9. Obeid, I., Selesnick, I., & Picone, J. (eds.) (2021). Biomedical Signal Processing: Innovation and Applications. Springer.
10. Sahin, F., & Pérez-Castillejos, P. (2023). Instrumentation Handbook for Biomedical Engineers. Routledge.
11. Kunal Pal, Bala Chakravarthy Neelapu, J. Sivaraman (2024). Advances in Artificial Intelligence: Biomedical Engineering Applications in Signals and Imaging. Elsevier.
See electronic folder of the subject.