Course syllabus for Digitalization in sports

The course syllabus contains changes
See changes

Course syllabus adopted 2026-03-12 by Head of Programme (or corresponding).

Overview

  • Swedish nameDigitalisering inom sport
  • CodeTRA300
  • Credits7.5 Credits
  • OwnerTRACKS
  • Education cycleSecond-cycle
  • ThemeMTS 7.5 c
  • DepartmentTRACKS
  • GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail

Course round 1

  • Teaching language

    English

  • Application code

    97117

  • Minimum participants

    8

  • Open for exchange students

    Yes

Credit distribution

Module
Sp1
Sp2
Sp3
Sp4
Summer
Not Sp
Examination dates
0123 Project 7.5 c
Grading: TH		3.5 c4 c

In programmes

Examiner

Course round 3

  • Teaching language

    English

  • Application code

    97191

  • Open for exchange students

    No

Credit distribution

Module
Sp1
Sp2
Sp3
Sp4
Summer
Not Sp
Examination dates
0123 Project 7.5 c
Grading: TH		3.5 c4 c

    Examiner

    Information missing

    Eligibility

    General entry requirements for Master's level (second cycle)

    Specific entry requirements

    Applicants needs to have 90 ECTS at the time for application.
    English 6/B.

    Course specific prerequisites

    In addition to the general requirements to study at the second-cycle level at Chalmers, necessary subject or project specific prerequisite competences (if any) must be fulfilled. Alternatively, the student must obtain the necessary competences during the course. The examiner will formulate and check these prerequisite competences.
    Selection is based on an overall assessment of the applicants’ merits and letter of motivation.

    Aim

    The course provides a platform to work and solve challenging cross-disciplinary authentic problems from different stakeholders in society such as the academy, industry or public institutions. Additionally, the aim is that students from different educational programs practice working efficiently in multidisciplinary development teams

    The aim of this course is to introduce students to digital technologies applied in sports and health-promoting applications.

    Learning outcomes (after completion of the course the student should be able to)

    General learning outcomes for Tracks courses:
    • critically and creatively identify and/or formulate advanced architectural or engineering problems
    • master problems with open solution spaces, which include handling uncertainties and limited information
    • lead and participate in the development of new products, services, processes and/or systems by following a design process and/or a systematic development process
    • communicate and convey information, problems, methods, and development processes, both orally and in writing
    Course specific learning outcomes:
    • explain how basic mechanical concepts such as power, friction, balance of forces, and conservation of linear and angular momentum and total energy can be used to study athletic performance
    • explain basic mechanical concepts of loading rate-dependent (viscoelastic) materials and how they can be used for energy absorption in sports (impact, damping etc.)
    • describe main digital tools and techniques used for motion tracking in preventive health care and sport applications
    • explain basic principles behind widely used sensor technologies
    • describe some of the main concepts from artificial intelligence (AI), e.g., data-driven methods and machine learning, and how they can be applied in sports and health-promoting applications

    Content

    The course introduces mechanical principles relevant to sports applications, including solid mechanics, structural dynamics, and biomechanics, together with sensor technologies and measurement systems used in sports and health. Both commercial sensors (e.g., inertial measurement units (IMUs), photo sensors, GPS, and barometers in consumer devices) and customized sensors integrated into sports equipment and healthcare applications (e.g., strain gauges, load cells, and bioelectrical sensors) are covered. Students work in a problem-based learning environment where they apply knowledge from mechanics, electronics, physics, mathematics, and data science to real measurement problems. Data analysis includes model-based methods grounded in first principles as well as introductory data-driven and machine learning approaches, with emphasis on uncertainty quantification, error propagation, and the relationship between measured signals and underlying physical variables. The course also introduces methods for user feedback and interaction design, including tools for virtual and augmented reality in sports and health contexts.

    Organisation

    The course is run by a teaching team. The main part of the course is a challenge-driven project. The challenge may range from being broad societal to profound research driven. The project task is solved in a group. The course is supplemented by teaching and learning of the skills necessary for the project. The project team will have one university examiner, one or a pool of university supervisors and one or a pool of external co-supervisors if applicable.

    The course is organized in two study periods. The first study period focuses on the introduction of the course content and the development of the project plan, while the second study period focuses on the execution of the project and the final presentation and report.

    Literature

    Relevant literature is retrieved and acquired by the students as a part of the project.

    Examination including compulsory elements

    The course is examined through the following components:
    • Project outcome (60%): Assessed based on a written report and relevant demonstrator material, such as developed hardware prototypes or software implementations.
    • Lecture-based knowledge (30%): Assessed through quizzes covering key concepts and methods presented in the course.
    • Presentation skills (10%): Assessed based on the students’ oral presentation of the project, evaluated by the examiner and teaching staff.

    The course examiner may assess individual students in other ways than what is stated above if there are special reasons for doing so, for example if a student has a decision from Chalmers about disability study support.

    The course syllabus contains changes

    • Changes to course:
      • 2026-03-12: Examination Examination changed by UOL/Adm
        Updated information about examination
      • 2026-03-12: Aim Aim changed by UOL/Adm
        Updated purpose
      • 2026-03-12: Prerequisites Prerequisites changed by UOL/Adm
        Updated prerequisites
      • 2026-03-12: Learning outcomes Learning outcomes changed by UOL/Adm
        Updated information about learning objectives
      • 2026-03-12: Content Content changed by UOL/Adm
        Updated content
      • 2026-03-12: Organization Organization changed by UOL/Adm
        Updated information about organization