Are you an EPFL student looking for a semester project?
Work with us on data science and visualisation projects, and deploy your project as an app on top of Graph Search.
The goal of this study is to assess the possibility of accurate on-line instantaneous velocity estimation in swimming. Having an on-line tool, coaches could provide immediate feedback about performance to trainees. More importantly, by on-line monitoring of velocity anomaly in open-water swimming, the safety of events can be significantly improved. We have previously introduced a method, using a wearable IMU, to estimate swimming instantaneous velocity, though information about pool length and a complete lap data were needed to correct the integration drift of IMU signals. In the present study, we used our previous algorithm for cycle’s mean velocity estimation, as a criterion for drift correction in instantaneous velocity estimation without the knowledge about pool length. Using a simple within-cycle linear drift model, the relative error of the algorithm tested on 8 swimmers is 0.1±15.4%. As a result, the instantaneous velocity is available at the end of every cycle.
Kamiar Aminian, Salil Apte, Farzin Dadashi, Benoît Mariani
Daniel Kressner, Axel Elie Joseph Séguin, Gianluca Ceruti