Contact details

• examiner: Dan Kuylenstierna, danku@chalmers.se, tel: +46 31 772 17 98
• lecturer: Marco L. Della Vedova, marco.dellavedova@chalmers.se , +46 31 772 36 10
• lecturer: Martin Fagerström, martin.fagerstrom@chalmers.se , +46 31 772 13 00
  

 

Our society is under a digital transformation. We do not know where it will bring us but it will affect us all and everywhere both professionally and in our spare time. Although the digital transformation is everywhere, the sport sector is one of the business sectors that is quickest in the adaption of new technology. In recent years we have seen numerous innovations emanating from the omnipresent data that is enabled by novel sensor technologies and quickly shared through high-speed communication devices.

 

In this course we exploit user needs in the sport sector as an innovation arena. As a student in the course, you will work on a challenge defined by a stakeholder holder in the sport sector, it may be from an athlete, a federation or an industrial actor. Typically, our challenges require competence from different disciplines. Most innovations rely on competences from many different programs at Chalmers. Specifically, experience in sensors, measurement methodology, mathematics and data analysis, mechanical engineering, and electronics are keys behind digital innovations in sports.

Aims

The aim of the course is to introduce the students to digital technologies applied in sports and health applications and to give students practical training in this field by means of real-life challenges defined by professional stakeholders in the sport business sector.

 

The scope covers on the one hand side hardware sensors for measurements and data acquisition and on the other hand side the process of data analysis including machine learning and artificial intelligence.

 

In the course, the students will face several examples of sensors and measurement data collected in the sport and health sectors. In a problem-based learning environment we will get the chance to acquire new knowledge but also to apply previously acquired knowledge, e.g., in mechanics, electronics, physics, mathematics, and data science.

 

Sensor technologies covered include common commercial sensors, e.g., inertial measurement units (IMUs), photo-sensors, gps, and barometers available in modern consumer electronics such as smart phones, watches and other devices. Customized sensors integrated in equipment for sports or health care, e.g., strain-gauge sensors and load cells for registration of forces as well as various bioelectrical sensors are also covered.

 

The part on data analysis covers firstly fundamental methods based on mechanics and first principle calculus but also more modern data driven methods and machine learning. The students will be introduced to methods for error-propagation with a clear focus on understanding the link between measured property and studied variable.

 

Learning objectives

After completed course all students are expected to

1. be familiar with main concepts describing the society’s digital transformation and able to discuss its implications for humans

2. be familiar with the main digital tools and techniques used for motion tracking in preventive health care and sport applications
3. understand basic principles behind widely used sensor technologies.
4. be familiar with principles of error propagation and assessment of measurement uncertainties
5. be able to apply engineering skills on human motion analysis, specifically from a biomechanical perspective treating work, energy, power, and efficiency
6. be familiar with some of the main concepts from artificial intelligence (AI), e.g., data-driven methods and machine learning
7. orally and in writing explain and discuss information, problems, methods, design/development processes and solutions
8. fulfill project specific learning outcomes
9. critically and creatively identify and/or formulate advanced architectural or engineering problems
10. master problems with open solutions spaces which includes to be able to handle uncertainties and limited information.
11. lead and participate in the development of new products, processes and systems using a holistic approach by following a design process and/or a systematic development process.

Organization and schedule

The course is running during study periods 1 and 2. The bulk of the course is the project carried out in groups by the students under supervision and guidance of course teachers.  

 

In addition to the project, ten two-hour lecture/tutorials will be arranged. The lectures can be physical or on-line and are planned according to the table below.

Table 1: Lecture plan for Digitalization in Sports

 

#	Topic	Time	Room
1	Introduction: Digitalization of the society and its impact in the sport sector. Problem formulation, presentation of projects, challenges and methods, Dan Kuylenstierna	September 4, 8-10	SB3-L111
2	Measurements, sensors and error propagation, Dan Kuylenstierna	September6, 8-10	SB3-L110
3	AI and Machine learning in sports part I, Marco Della Vedova	September 11, 8-10	SB3-L110
4	AI and Machine learning in sports part II, Marco Della Vedova	September 13, 8-10	SB3-L110
6	Mechanics in sports part I, Martin Fagerström	September 18, 8-10	SB3-L110
7	Mechanics in sports part II, Martin Fagerström	September 20, 8-10	SB3-L110
8	Guest lecture 1	TBD	TBD
9	Guest lecture 2	TBD	TBD
10	Guest lecture 3	TBD	TBD
11	Project presentations	December 12, 8-12	SB3-L110

 

 

Literature

Lecture slides and recommended scientific papers, beside what is identified by the students during the course of the project work.

 

Examination

The course will be examined based on

• Project outcome, summarized in a written report as well as relevant demonstrator material in terms of hardware prototypes or software developed, 60%

• Learnings from lectures, examined in quizzes, 30%
• Students presentation skills assessed by the examiner and teachers in the course, 10%

Prerequisites

The course is open to Bachelor students and Master students from all programs at Chalmers. In the application, students are expected to declare which project they are interested in primarily. Depending on program and the students’ previous courses, some fields may be more suitable.

 

Course duration:
September 2023– January 2024*.

*It is possible to take the course as 15 ECT course with an extended project scope that is defined together with supervisors and examiners. The general target is that an extended project should have such a quality in both result and reporting that it is good enough for publication at a peer-reviewed Scientific conference or alternatively results in fully operational demonstrator.

Examiner: Dan Kuylenstierna

Lecturers: Dan Kuylenstierna, Marco L. Della Vedova, Martin Fagerström and guest lecturers.