Digital Sport Sciences

The digital revolution in sports and exercise is already underway and creates a demand for skilled professionals able to bridge the gaps between the different scientific fields covered by DIGISPORT :
sport science, computer science, data science, electronics, human and social sciences.


The Digital Sport Sciences master degree is based on research and innovation to train professionals capable of :

  • Apprehend the challenges of the digital transformation of sport regarding performance, health, physical activity
  • Develop a cohesive vision of the principal scientific and technical issues that arise from the integration of new technologies in sport and exercice, from their use as well as structuring changes and innovations they bring
  • Master the concepts, methods, tools, et technological know-how supporting the current development of this field

The Digital Sport Sciences master degree delivered by DIGISPORT focuses on modularity, allowing students to tailor their training program in accordance with their professional project. The master is divided into majors and minors during the first three semesters. The 4th and last semester is dedicated to the end-of-studies intership.

The major (90 ECTS), includes core courses mandatory for all students as well as specialisation courses to elect according to the student’s targeted work profile.

The minor (30 ECTS) enables the acquisition of complementary skills in another field and allow students to build a unique profile tailored to their aspirations and competences. It also includes a project course, allowing students to develop their research, interdisciplinarity and collaboration skills.

delivered by DIGISPORT
is built on a competency-based approach.

These competencies can be grouped into 4 different interdiscplinary profiles.

Metrology of human movement and innovative sensors

This profile leads to careers in motion capture, especially in the development of new generations of sensors to overcome sport and exercise constraints : miniaturization, energy consumption, use in extreme conditions, data transfer, communication between sensors, signal processing, etc.

Analysis, modeling & simulation
of human mouvement

This profile is particularly suited for students wanting to analyze, model and simulate the human movement in order to comprehend and optimize sport performance. Through this profile, students will develop competencies in biomechanics and physiology as well as in complex and multifactorial modeling.

Data science
applied to sport

This profile focuses on exploiting digital data in sport using mathematical and statistical methodologies in order to extract indicators of performance and risk injury among others. Students will learn to master statistics and artificial intelligence as well as digital data in sport and how they can be used in association with sensors.

Digital solutions for
interaction in sport

This profile focuses on the latest scientific and technical methodologies of computer science coupled with the underlying processes of performance in order to develop new generations of tools to analyze and build better movement in sport. This profile connects digital science with methods of interaction, sensory feedback, individualized and adaptative learning, and artificial intelligence.

Structure of the master degree



Students have to complete two periods of interships in a company or laboratory during their training program.


Lasting 4 to 6 weeks,
the first intership period must be
completed during the 2nd semester (Year 1).


Lasting 4 to 6 months,
the second intership period must be
completed during the 4th semester (Year 2).

Examples of interships performed by DIGISPORT students :

  • Monitoring and modeling of performance in swimming
  • Biomechanical and physiological adaptations to novel sensori-motor congruences during virtual cycling
  • Assessment of a VR-based training tool for the optimization of perceptual-motor processes of boxers
  • Sport video contents quality assessment
  • Embedded active vision for body-localization in learning-based swimming gesture analysis