It is recommended that you have completed courses related to the project topic, as well as skills in document research and the basics of research methodology and data analysis. In addition, teamwork and communication skills are valued.
1. Apply the knowledge acquired during the degree to solve a real need in the healthcare sector from the perspective of medical and health engineering.
2. Address assigned projects by integrating sex and gender variables when relevant to the problem.
3. Design 3D printing models using SolidWorks.
4. Plan, develop, and present research work applied to Health Engineering.
The course is structured into two parallel components that converge in the final assessment:
1. Part 1: SolidWorks Module
- Introduction to the CAD environment and basic tools.
- Modeling of parts and assemblies.
- Generation of technical drawings and design documentation.
- Mini-project on a 3D model with a biomedical application.
2. Part 2: Team Research and/or Development Project
- Presentation of project proposals by tutors and selection based on preferences using a digital questionnaire.
- Team formation and role assignment.
- Initial methodological design: definition of the scientific/technological problem, objectives, task distribution, and timeline.
- Development phase: execution of experimental tasks in the project's research lines (AI and Data Science, Speech Processing, Computer Vision, Interaction with AI, Biomaterials/Nanotechnologies).
- Follow-up and mentoring: periodic control and feedback meetings.
- Midterm evaluation: oral presentation and progress report.
- Final presentation: technical/scientific report and public defense before the teaching panel.
The course operates according to the following schedule:
- Project and tutor assignment: the tutoring faculty presents the project proposals. Students indicate their preferences through a digital questionnaire; the assignment is made seeking the best possible match between preferences and group balance.
- Team formation: teams will consist of 2 to 5 students with a tutor. Working channels are defined (Moodle/ Teams /Drive, Git repositories if applicable, databases, etc.).
- Phase 1 - Development of 3D models and methodological design of the research project
- Phase 2 - Project Development
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.).
- Practical learning in the laboratory: guided SolidWorks sessions (learning by doing or hands-on ) with evaluation based on performance and results.
- Short seminars and micro-lessons to cover just - in -time topics teaching ?) the key concepts that allow progress.
- Academic and technical tutoring: formative follow-up with actionable feedback aimed at improvement.
- Cooperative learning: rotating roles (by weeks and/or by types of assessments), co-responsibility (each role has its own and shared tasks : the result belongs to everyone), collaborative problem solving (use of dynamics to think and decide in a group: brainwriting , brainstorming , etc.) and co-evaluation (anonymous assessment of each presentation both individually and by peers).
- Continuous assessment: three main checkpoints (methodological design, mid-term, final) + individual and group follow-up.
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).
Training activities are conducted both in person and remotely.
Face-to-face: SolidWorks workshops (15 hours), Project tutorials: planning, report review, lab work, bottleneck resolution, presentations and defenses.
Non-face-to-face/autonomous: Literature search and analysis (scientific databases, critical review), Methodological design (objectives, hypotheses, variables, tasks, schedule, risks/solutions), Technical/experimental development (implementation, testing, prototyping, validation), Documentation and communication (project log, technical/scientific report, poster/slides), Project management (internal meetings and agreements).
The assessment of the subject integrates its two parts: the first which deals with 3D functional modeling with Solidworks (contributes 20% of the grade) and the second which addresses the research project (contributes 80% of the grade).
To pass the course, you must pass Part 1 (Grade_P1) and Part 2 (Grade_P2) separately. The final grade for the course will be calculated as follows:
If Grade_P1 is >= 5 and Grade_P2 is >= 5, the final grade for the subject will be Final_Grade = 0.2 · Grade_P1 + 0.8 · Grade_P2
If Grade_P2 < 5, the final grade for the subject will be FINAL_GRADE = Grade_P2.
If Grade_P1 < 5, the final grade for the subject will be FINAL_GRADE = MIN(4, Grade_P2).
1. Evaluation of Part 1:
- The grade for Part P1 (N_P1) is calculated by weighting three grades: the attitude grade (10%), the grade for practical exercises in class or outside of class (30%), and the grade for the final assignment (60%). The attitude grade will be calculated based on attendance, attitude, and participation in class.
The final deliverable consists of the design of a 3D model. Although it will not be printed, it must be correctly dimensioned, assembled (if applicable), and documented.
2. Evaluation of Part 2
The grade for Part P2 (N_P2) is calculated by weighting four grades: the grade for the submission/presentation of the project's methodological design (15%), the grade for the intermediate submission/presentation (25%), the grade for individual and group progress (20%), and the grade for the final project submission/presentation (40%). Each participant's individual progress grade takes into account their attendance, participation in meetings, and creativity. The group progress grade considers the group's overall adherence to the schedule, group organization and cohesion, peer evaluation (if deemed necessary by the tutor), and other elements that contribute to achieving the project objectives.
Students who do not pass the subject in the regular examination period will have an extraordinary examination period in which they can retake the grade(s) for the part(s) they failed. If, for example, a student failed only Part 1, they can keep the grade for Part 2, or vice versa. If they failed both parts, they will have to retake them.
In the ordinary call, all components of the grades in parts 1 and 2 are taken into account.
In the extraordinary meeting of both parties, all notes will be retained except:
- The grade for the new extraordinary final assignment for Part 1 (60%). This new assignment will be assigned by the professor between 1 and 2 months before the extraordinary exam, and students must bring the solution on the day of the extraordinary exam.
- The grade for the new, extraordinary final submission/presentation of the Part 2 project (40%). For the extraordinary session, it will be necessary to submit and present a new version of the project with the methods/results/solutions of the individual and group tasks in which the student participates. If more than one student on the team failed, a single document with the team's final version may be submitted and presented.
1. SolidWorks Basics: A Project-Based Approach? Fred Fulkerson
2. Top SolidWorks Books (3DEngineer, 2023)
3. Creswell, J.W., & Creswell, J.D. (2023). Research design: Qualitative, quantitative, and mixed methods approach (6th ed.). SAGE.
4. Hernández-Sampieri, R., & Mendoza, C. (2018). Research Methodology: The quantitative, qualitative and mixed routes. McGraw-Hill.
5. Yin, R. K. (2018). Case study research and applications: Design and methods (6th ed.). SAGE.
6. Browner, WS, Newman, TB, Cummings, SR, & Grady, DG (2022). Designing clinical research (5th ed.). Wolters Kluwer.
7. Vijayarani, S., Ilamathi, M.J., & Nithya, M. (2015). Preprocessing techniques for text mining - an overview. International Journal of Computer Science & Communication Networks, 5(1), 7-16.
8. Khder, M.A. (2021). Web scraping or web crawling: State of art, techniques, approaches and applications. International Journal of Advances in Soft Computing & Its Applications, 13(3).
9. A.J. Nihart, et al. NatMed 2025; 31 (4): 1114-1119. doi:10.1038/s41591-024-03453-1.
10. Ng, SI, Xu, L., Siegert, I., Cummins, N., Benway, NR, Liss, J., & Berisha, V. (2024). A tutorial on developing clinical speech AI: from data collection to model validation. arXiv preprint arXiv : 2410.21640.
11. Botelho, C., Abad, A., Schultz, T. and Trancoso, I. (2024). Speech as a biomarker for disease detection. IEEE Access.
See electronic folder of the subject.