By the end of the course, students will be able to: 1. Formulate clear research questions and testable hypotheses in biomedical engineering contexts. 2. Identify and critically evaluate different experimental study designs. 3. Understand and apply key concepts of inferential statistics for data analysis. 4. Design and conduct simple experiments involving human subjects in an ethical and methodologically sound manner. 5. Collect and process experimental data using smartphone-based tools and appropriate workflows. 6. Use the R programming language to perform statistical analyses and visualize data. 7. Interpret statistical results and draw scientifically valid conclusions. 8. Communicate findings effectively through graphical and quantitative representations.

No prior programming experience is required, although familiarity with any programming language is beneficial.

The course is structured into four modules. MODULES I, II, and III are delivered sequentially, while MODULE IV is conducted in parallel with MODULE III. MODULE I – Applied Paradigm (6 hours) - Fundamentals of human posture control and stabilometry - Signal processing and extraction of postural metrics - Acquisition of posture-related experimental data using smartphones (PhyPhox) MODULE II – Introduction to R and RStudio (6 hours) - Introduction to R programming for data analysis - Practical tutorial on R and on the RStudio integrated development environment (IDE) MODULE III – Experimental Design and Inferential Methods (30 hours) - Fundamentals of experimental design - Formulation of research questions and hypotheses - Types of study designs (observational and experimental; controlled studies) - Sampling strategies and sources of bias - Data summarization and visualization - Introduction to inferential statistics - Statistical testing and confidence intervals - Generalization from sample to population MODULE IV – Laboratories: From Theory to Practice (18 hours) - Application of statistical methods to experimental datasets - Data analysis using R/RStudio applied to the dataset collected in MODULE I

As part of the laboratory activities, students will collect experimental data on human posture control using their own smartphones (PhyPhox ad hoc developed application). The activity is designed exclusively for educational purposes and does not constitute research involving human subjects. To ensure compliance with ethical standards and data protection regulations (GDPR), the following principles apply: - Voluntary participation: Students may opt out of personal data collection without any academic penalty. Alternative datasets will be provided. - Data anonymization: No personally identifiable information will be collected. Data will be recorded and analyzed in anonymized or pseudonymized form. - Purpose limitation: Data will be used solely for teaching activities within the course and will not be used for research or publication without prior ethical approval and explicit consent. - Data minimization: Only data strictly necessary for the learning objectives will be collected. Use of personal devices: Students will use their own smartphones; no additional personal data beyond the experimental measurements will be accessed or stored. - Safety considerations: The experimental protocol involves low-risk tasks (quiet standing). Students will be instructed to perform the activity in safe conditions and may refrain from participation if they have any condition affecting balance or safety. All activities are conducted in accordance with institutional guidelines and applicable European data protection regulations.

The course consists of lectures and laboratory sessions. Breakdown: ~33 hours: Theoretical lectures (experimental design and inferential statistics) ~24 hours: Practical activities, including: - Data acquisition using smartphones - R programming tutorials - Data processing and statistical analysis ~3 hours: Domain-specific lectures on human posture control and stabilometric data processing The course follows an integrated approach, where data collected early in the semester are progressively analyzed using the methods introduced in lectures.

Course slides (primary reference) Instructor-provided course book (primary supporting material) Selected scientific papers (provided during the course) These materials are sufficient for successful completion of the course.

Lecture slides; Text book; Exercises; Exercise with solutions ; Lab exercises;

Exam: Practical lab skills test; Computer-based written test in class using POLITO platform;

The final assessment consists of: Written quiz: Evaluates understanding of theoretical concepts, including experimental design and inferential statistics. Practical exam (R-based problem): Students are required to analyze a dataset and solve a problem relevant to biomedical engineering using the R programming language. Evaluation criteria include: Correct application of statistical methods Quality and clarity of data analysis Appropriate interpretation of results Ability to link theoretical concepts to practical problems

In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.