MAIStroPE1: 1st Workshop on Action Modelling for Interaction and Analysis in Smart Sports and Physical Education Utrecht, Netherlands, October 25-29, 2020 |
Conference website | http://smart-sports-exercises.nl/maistrope/ |
Submission link | https://easychair.org/conferences/?conf=maistrope1 |
The workshop intends to bring together the research in Machine Learning, Interactive Technology and Sports & Movement Science to propose multimodal and interactive systems which can be utilized by coaches/trainers and players during their routine training sessions to analyse performance and provide real time interactive feedback.
Performance in sports depends on training programs designed by team staff, with a regime of physical, technical, tactical and perceptual-cognitive exercises. Depending on how participants perform, exercises are adapted, or the program may be redesigned. State of the art data science methods have led to ground breaking changes. Data is collected from sources such as tracking position and motion of players in basketball, baseball & football match statistics and volleyball. It is not just limited to sports training; but can also be extended to any physical exercise based activity such as dance lessons, aerobics, yoga training etc.
There are multiple examples of using both sensor and computer vision based approaches for automatic recognition of sports action in numerous sports e.g. soccer, tennis, table tennis , hockey , basketball and rugby. However, sports trainers still rely on manual effort to collect and analyse events of interests related to intended learning focus. The workshop aims to fill the gap between the state of the art in technological development and the state of the art in sports and physical education. We aim to bring together the researchers from these areas and solicit ideas regarding, how the recent technological advances can be applied in real life sports and physical education (PE) scenarios in a multidisciplinary and user centric way and enhance the overall user experience with PE.
We invite papers for frameworks, system ideas (architecture), user studies, data sets and models, which are or have the potential to be readily applied in real life training scenarios for sports or recreational physical activities. The areas of interest include but not limited to:
- Multimodal sensors.
- Machine Learning.
- Computer Vision.
- Virtual/Augmented Reality.
- Sport and Movement Science
- Rehabilitation and injury prevention
- Exertion and playful interaction
- eSports
- Sports and Physical Education and Pedagogy