Projects / Programmes source: ARIS

AiCoachU – Artificial intelligence is coaching you

Research activity

Code Science Field Subfield
5.10.00  Social sciences  Sport   

Code Science Field
3.03  Medical and Health Sciences  Health sciences 
running, recreation, health, injury risk, wearable sensors, biomechanics, deep learning, signal analysis, modelling
Evaluation (rules)
source: COBISS
Data for the last 5 years (citations for the last 10 years) on April 20, 2024; A3 for period 2018-2022
Data for ARIS tenders ( 04.04.2019 – Programme tender, archive )
Database Linked records Citations Pure citations Average pure citations
WoS  363  5,970  5,144  14.17 
Scopus  463  7,732  6,619  14.3 
Researchers (16)
no. Code Name and surname Research area Role Period No. of publicationsNo. of publications
1.  26454  PhD Matjaž Depolli  Computer science and informatics  Researcher  2021 - 2024  99 
2.  11805  PhD Simon Dobrišek  Computer science and informatics  Researcher  2021 - 2024  284 
3.  55242  Miha Drobnič  Educational studies  Researcher  2021 - 2024  11 
4.  52909  Mitja Jančič  Computer science and informatics  Researcher  2022 - 2024  22 
5.  28366  PhD Gregor Kosec  Computer science and informatics  Researcher  2021 - 2024  161 
6.  39398  Miha Mohorčič    Technical associate  2022 - 2024  18 
7.  50843  Jon Natanael Muhovič  Computer science and informatics  Researcher  2021 - 2024  23 
8.  20183  PhD Boštjan Murovec  Systems and cybernetics  Researcher  2022 - 2024  208 
9.  58357  Irinej Papuga    Technical associate  2023 - 2024 
10.  21310  PhD Janez Perš  Systems and cybernetics  Researcher  2021 - 2024  238 
11.  32441  PhD Aleksandra Rashkovska Koceva  Computer science and informatics  Researcher  2021 - 2024  82 
12.  31550  PhD Samo Rauter  Sport  Researcher  2022 - 2024  186 
13.  04959  PhD Vojko Strojnik  Educational studies  Researcher  2021 - 2024  585 
14.  20755  PhD Matej Supej  Sport  Head  2021 - 2024  347 
15.  39158  PhD Nina Verdel  Sport  Researcher  2022 - 2024  70 
16.  22502  PhD Goran Vučković  Educational studies  Researcher  2021 - 2024  351 
Organisations (3)
no. Code Research organisation City Registration number No. of publicationsNo. of publications
1.  0587  University of Ljubljana, Faculty of Sport  Ljubljana  1627040  19,172 
2.  0106  Jožef Stefan Institute  Ljubljana  5051606000  90,682 
3.  1538  University of Ljubljana, Faculty of Electrical Engineering  Ljubljana  1626965  27,758 
Physical activity is one of the key contributors to health and quality of life. Running is popular and an efficient and affordable modality of physical activity. However, if done improperly, it may induce injuries leading to lower life quality and additional health and social costs. Therefore, it is important to provide tools for effective and injury-free physical activity. In the present study, a new generation of IMU sensors (smart) patches with considerably smaller dimensions and weight will be employed for rearfoot and pelvis stability measurement and their changes due to fatigue during running at different velocities and surface inclinations. This will be analysed through the pelvis and rearfoot motion patterns employing deep learning. Results of the present study will show the eligibility for development of an on-line virtual running coach for safe running and for choosing the proper running shoes. The overall objective of the project is to demonstrate a successful recognition of fatigue onset and excessive pelvic and rearfoot mechanics at different running velocities and surface inclines using deep learning. WP1: A software platform for capturing, storing, synchronization and processing of the captured data will be developed and will present an integral part of the project. The platform will be connected to the hardware data acquisition platform and will enable both automatic access to the data as well as access through a user interface. The majority of the platform will be located in the cloud, but its parts will also extend all the way to the acquisition hardware and computers on which the development will take place. Ultimately, the platform will be a key part of the demonstrators, where it will provide coaching based on an analysis in near real-time. WP2: Conduct key measurements for project implementation needs. First, data will be collected by placing sensors on different locations to optimally capture motion patterns and their changes in deep learning. Then, measurements required for biomechanical analysis (WP3) and Deep Learning procedures and their verification (WP4) will be performed. WP3: Provide information on the optimal placement of IMU sensors and signal conditioning to reliably track pelvic and heel movements, and set criteria for determining fatigue levels as input for deep learning. WP4: Test state-of-the-art (SOTA) deep recurrent networks on the task of inferring expert biomechanical annotations from noisy data, obtained from wearable sensors. Adapt SOTA networks to improve performance on this particular problem. WP5: Demonstrators will be developed to show the applicability of the newly developed machine learning procedures based on smart patch measurements (AiCoachU) in line with the main objective of the project.
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