Gait Analysis, Assessment and Assistive Devices
A.Y. 2026/2027
Learning objectives
The course aims to provide the necessary foundations for understanding and using different assessment techniques in preventive and adapted contexts. Concomitantly, the course aims to provide the statistical and methodological foundations for evaluating the effectiveness of prescribing preventive and adapted physical activity.
Expected learning outcomes
-To gain skills for the assessment of individuals' physical/cognitive abilities in order to tailor the prescription of preventive and adapted physical activity.
-To understand how assistive devices work and their application.
-To gain skills for assessing scientific study results; data organization; as well as reading and interpreting test data.
-To understand how assistive devices work and their application.
-To gain skills for assessing scientific study results; data organization; as well as reading and interpreting test data.
Lesson period: year
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Lesson period
year
In-person lectures as well as synchronous distance learning
Course syllabus
"Data Analysis" module:
1. Structure of a scientific article and the role of data analysis
2. Descriptive statistics, tables, graphs, and measurement of central tendency and variability (mean, variance, standard deviation, median, percentiles, quartiles)
3. Gaussian distribution
4. Elements of inferential statistics
- Population and sample
- Estimating the mean and variance in a sample
- Central limit theorem
- Distribution of the sample mean and standard error
- Student's t distribution
- Confidence intervals
5. Hypothesis testing and statistical significance. Type I and type II errors
6. Testing the mean
7. Studies in which subjects are observed before and after treatment: Student's t-test for paired data
8. How to determine the difference in the mean values between two distinct groups: Student's t-test for non-paired data
9. The Fisher-F-distribution. Analysis of variance for independent groups and for repeated measures.
10. Overview of alternative to t-tests and analysis of variance on ranks
11. Analyzing the relationship between two variables: correlation and regression
12. Analysis of categorical data. The chi-squared distribution and the chi-squared test for independence.
13. Case study: the actigraphic study for the monitoring of diurnal activity and sleep quality.
14. Case study: analysis of endocrine-metabolic data by means of mathematical modelling.
"Gait analysis, assessment and assistive devices" module:
1. The training process
2. Aims and utility of the functional evaluation
3. The basics of measurement process and data analysis
4. Monitoring and evaluating the level of physical activity
5. Monitoring and evaluating the cardiovascular component
6. Monitoring and evaluating the neuromuscular component
7. Monitoring and evaluating the balance and flexibility component
8. Gait analysis
9. Assistive devices
1. Structure of a scientific article and the role of data analysis
2. Descriptive statistics, tables, graphs, and measurement of central tendency and variability (mean, variance, standard deviation, median, percentiles, quartiles)
3. Gaussian distribution
4. Elements of inferential statistics
- Population and sample
- Estimating the mean and variance in a sample
- Central limit theorem
- Distribution of the sample mean and standard error
- Student's t distribution
- Confidence intervals
5. Hypothesis testing and statistical significance. Type I and type II errors
6. Testing the mean
7. Studies in which subjects are observed before and after treatment: Student's t-test for paired data
8. How to determine the difference in the mean values between two distinct groups: Student's t-test for non-paired data
9. The Fisher-F-distribution. Analysis of variance for independent groups and for repeated measures.
10. Overview of alternative to t-tests and analysis of variance on ranks
11. Analyzing the relationship between two variables: correlation and regression
12. Analysis of categorical data. The chi-squared distribution and the chi-squared test for independence.
13. Case study: the actigraphic study for the monitoring of diurnal activity and sleep quality.
14. Case study: analysis of endocrine-metabolic data by means of mathematical modelling.
"Gait analysis, assessment and assistive devices" module:
1. The training process
2. Aims and utility of the functional evaluation
3. The basics of measurement process and data analysis
4. Monitoring and evaluating the level of physical activity
5. Monitoring and evaluating the cardiovascular component
6. Monitoring and evaluating the neuromuscular component
7. Monitoring and evaluating the balance and flexibility component
8. Gait analysis
9. Assistive devices
Prerequisites for admission
"Data Analysis" module: knowledge of first level secondary school mathematics
"Gait analysis, assessment and assistive devices" module: prior knowledge of the principles of exercise physiology and training methodology.
"Gait analysis, assessment and assistive devices" module: prior knowledge of the principles of exercise physiology and training methodology.
Teaching methods
"Data Analysis" module: frontal lessons alternate theoretical lectures and practical exercises in preparation for the final exam.
"Data Analysis" module: frontal lessons alternate theoretical lectures and practical exercises in preparation for the final exam.
"Gait analysis, assessment and assistive devices" module: frontal lectures and practical exercises." module: frontal lectures and practical exercises.
"Data Analysis" module: frontal lessons alternate theoretical lectures and practical exercises in preparation for the final exam.
"Gait analysis, assessment and assistive devices" module: frontal lectures and practical exercises." module: frontal lectures and practical exercises.
Teaching Resources
The reference material for the "Data Analysis" module consists of the material presented during the lectures (pdf documents uploaded onto the Ariel portal. Additional material for further reading can be found in the Tutorial section on the Ariel portal.
Recommended (not compulsory) reading list:
1) Marc M. Triola e Mario F. Triola - Statistica per le discipline biosanitarie, Pearson Editore.
2) S. A. Glantz- Statistica per le Discipline Biomediche, McGraw-Hill Editore.
3) David S. Moore - Statistica di base, Apogeo Editore.
The reference material for the "Gait analysis, assessment and assistive devices" module consists of scientific papers to explain each topic. Such papers will be uploaded and made available in the Course's website (www.ariel.unimi.it).
Recommended (not compulsory) reading list:
1) Marc M. Triola e Mario F. Triola - Statistica per le discipline biosanitarie, Pearson Editore.
2) S. A. Glantz- Statistica per le Discipline Biomediche, McGraw-Hill Editore.
3) David S. Moore - Statistica di base, Apogeo Editore.
The reference material for the "Gait analysis, assessment and assistive devices" module consists of scientific papers to explain each topic. Such papers will be uploaded and made available in the Course's website (www.ariel.unimi.it).
Assessment methods and Criteria
"Data Analysis" module: written exam, 1 hour allowed, 31 multiple choice questions. Grading: 30 and with honors for 31 correct answers, 30 for 30 correct answers, and so on down. The questions will cover the entire course content. Students are required to bring Student's Z and T tables, a calculator, and correction fluid to the exam; use of a mobile phone calculator is not allowed.
"Gait analysis, assessment and assistive devices" module: Written report (in APA style) detailing a protocol of physical activity monitoring and evaluation according to the principles discussed throughout the course. This assignment is due by the end of the course itself (or before the beginning of the exam sessions). The exam will be marked out of 30 and the student will be evaluated as following:
LITERATURE REVIEW AND ANALYSIS (max 4 points): self-explaining.
CRITICAL ANALYSIS (max 4 points): student's enrolment in the problem formulation, in the ability to understand cultural and technical issues throughout the work and in the ability to find/imagine new solution to solve them.
RELEVANCE TO THE COURSE'S OBJECTIVES (max 4 points): self-explaining.
comprehensiveness (max 4 points): report's quality with respect to the topic context.
CLARITY AND APPROPRIATENESS OF WORDS (max 3 points): self-explaining.
REPORT'S QUALITY (max 3 punti): perceived quality taking into consideration, wherever possible, the state-of-art of the topic.
DEEPENING OF THE TOPIC (max 3 points): background knowledge particularly in relation to the number of publications related to the topic.
QUALITY OF FIGURES, TABLES AND REFERENCES USED (max 3 points): self-explaining.
QUALITY OF THE REPORT'S ORGANIZATION AND STYLE (max 3 points): self-explaining.
The "Data Analysis" is a prerequisite for the "Gait analysis, assessment and assistive devices".
The final grade will be the arithmetic mean of the grades from the two modules.
"Gait analysis, assessment and assistive devices" module: Written report (in APA style) detailing a protocol of physical activity monitoring and evaluation according to the principles discussed throughout the course. This assignment is due by the end of the course itself (or before the beginning of the exam sessions). The exam will be marked out of 30 and the student will be evaluated as following:
LITERATURE REVIEW AND ANALYSIS (max 4 points): self-explaining.
CRITICAL ANALYSIS (max 4 points): student's enrolment in the problem formulation, in the ability to understand cultural and technical issues throughout the work and in the ability to find/imagine new solution to solve them.
RELEVANCE TO THE COURSE'S OBJECTIVES (max 4 points): self-explaining.
comprehensiveness (max 4 points): report's quality with respect to the topic context.
CLARITY AND APPROPRIATENESS OF WORDS (max 3 points): self-explaining.
REPORT'S QUALITY (max 3 punti): perceived quality taking into consideration, wherever possible, the state-of-art of the topic.
DEEPENING OF THE TOPIC (max 3 points): background knowledge particularly in relation to the number of publications related to the topic.
QUALITY OF FIGURES, TABLES AND REFERENCES USED (max 3 points): self-explaining.
QUALITY OF THE REPORT'S ORGANIZATION AND STYLE (max 3 points): self-explaining.
The "Data Analysis" is a prerequisite for the "Gait analysis, assessment and assistive devices".
The final grade will be the arithmetic mean of the grades from the two modules.
BIOS-12/A - Human Anatomy - University credits: 1
IBIO-01/A - Bioengineering - University credits: 2
MEDF-01/B - Sport Sciences and Methodology - University credits: 5
MEDS-19/A - Orthopaedics - University credits: 1
IBIO-01/A - Bioengineering - University credits: 2
MEDF-01/B - Sport Sciences and Methodology - University credits: 5
MEDS-19/A - Orthopaedics - University credits: 1
Technical-Practical Activities (ATP): 60 hours
Exercises: 48 hours
Exercises: 48 hours
Professors:
Bonato Matteo, Caumo Andrea, Coratella Giuseppe, Mangiavini Laura, Montaruli Angela, Rampichini Susanna, Vernillo Gianluca
Shifts:
Turno
Professors:
Bonato Matteo, Caumo Andrea, Coratella Giuseppe, Mangiavini Laura, Montaruli Angela, Rampichini Susanna, Vernillo GianlucaProfessor(s)
Reception:
To be arranged via e-mail
Via Colombo 71, 20133 Milano
Reception:
To be agreed via email.
Via Giuseppe Colombo 71, Basement 2, 1st floor, Milano
Reception:
Appointments can be arranged by email
Reception:
I'm appointment only
In-presence appointments take place at Building 2, via Giuseppe Colombo, 71 - Milan. Alternatively, on-line appointments can be arranged through Teams platform
Reception:
Appointment by email
Appointment by email