M2S Laboratory

Research topic

My research lies at the intersection of sports science, biomechanics, and data science. I focus on modeling movement in swimming, with particular emphasis on the use of inertial measurement units (IMUs), which offer a more mobile, ecological, and accessible alternative to traditional optical motion capture systems.

The main goal of my work is to develop innovative signal processing methods based on deep neural network architectures, capable of automatically extracting relevant biomechanical variables from raw IMU data. These approaches not only provide a better understanding of swimmers’ techniques but also lead to the creation of practical tools that athletes and coaches can use directly in the field.

Through this project, I aim to bridge the gap between scientific research and the practical needs of sports performance by enabling more accurate, autonomous, and environment-adapted motion analysis in aquatic settings.

Keywords 

Swimming, IMU, Times Series, Deep Learning, Signal Processing, Motion Capture

Publications