Coaching | EPFL Graph Search

Le coaching, ou accompagnement, est une méthode d'accompagnement personnalisé destinée à améliorer les compétences et la performance d'un individu, d'un groupe ou d'une organisation, grâce à l'amélioration des connaissances, l'optimisation des processus et des méthodes d'organisation et de contrôle. Il s'est développé à l'origine dans le milieu du sport (le coach pouvant être entraîneur ou compléter celui-ci pour un encouragement de type plus psychologique), mais son usage a dépassé de ce contexte à partir de la fin du pour apparaître dans le milieu de l'entreprise, puis celui du développement personnel de manière moins cadrée et souvent contestée. Il est aujourd'hui présent dans de nombreux domaines de la vie, professionnel, nutritionnel, parental, scolaire Le coaching est le métier de l'accompagnement fondé sur un dialogue entre le client et son coach. Il permet au client, par la construction de ces échanges, de trouver les solutions les plus adaptées à ses capacités, croyances et

Monitoring weekly progress of front crawl swimmers using IMU-based performance evaluation goal metrics

Kamiar Aminian, Farzin Dadashi, Vincent Gremeaux, Mahdi Hamidi Rad, Fabien Massé

Technical evaluation of swimming performance is an essential factor in preparing elite swimmers for their competitions. Inertial measurement units (IMUs) have attracted much attention recently because they can provide coaches with a detailed analysis of swimmers' performance during training. A coach can obtain a quantitative and objective evaluation from IMU. The purpose of this study was to validate the use of a new phase-based performance assessment with a single IMU worn on the sacrum during training sessions. Sixteen competitive swimmers performed five one-way front crawl trials at their maximum speed wearing an IMU on the sacrum. The coach recorded the lap time for each trial, as it remains the gold standard for swimmer's performance in competition. The measurement was carried out once a week for 10 consecutive weeks to monitor the improvement in the swimmers' performance. Meaningful progress was defined as a time decrease of at least 0.5 s over a 25 m lap. Using validated algorithms, we estimated five goal metrics from the IMU signals representing the swimmer's performance in the swimming phases (wall push-off, glide, stroke preparation, free-swimming) and in the entire lap. The results showed that the goal metrics for free-swimming phase and the entire lap predicted the swimmer's progress well (e.g., accuracy, precision, sensitivity, and specificity of 0.91, 0.89, 0.94, and 0.95 for the lap goal metric, respectively). As the goal metrics for initial phases (wall push-off, glide, stroke preparation) achieved high precision and specificity (& GE;0.79) in progress detection, the coach can use them for swimmers with satisfactory free-swimming phase performance and make further improvements in initial phases. Changes in the values of the goal metrics have been shown to be correlated with changes in lap time when there is meaningful progress. The results of this study show that goal metrics provided by the phase-based performance evaluation with a single IMU can help monitoring swimming progress. Average velocity of the lap can replace traditional lap time measurement, while phase-based goal metrics provide more information about the swimmer's performance in each phase. This evaluation can help the coach quantitatively monitor the swimmer's performance and train them more efficiently.

FRONTIERS MEDIA SA2022

SmartSwim, a Novel IMU-Based Coaching Assistance

Kamiar Aminian, Farzin Dadashi, Vincent Gremeaux, Mahdi Hamidi Rad, Fabien Massé

Swimming coaches provide regular timed and technical feedback to swimmers and guide them efficiently in training sessions. Due to the complexity of swimmers' performance, which is not visible in qualitative observation, quantitative and objective performance evaluation can better assist the coach in this regard. Inertial measurement units (IMUs) are used in swimming for objective performance evaluation. In this study, we propose a new performance evaluation feedback (SmartSwim) using IMU and investigate its effects on the swimmer's weekly progress. Measurements were conducted each week with 15 competitive swimmers for 10 weeks using a Sacrum IMU. The SmartSwim report included a comprehensive representation of performance based on goal metrics of each phase extracted from the IMU signals. The swimmers were divided into two groups: the experimental and control groups. The SmartSwim report for each swimmer in the experimental group was given to the coach, who used it to adjust the training accordingly. The results showed that the experimental group outperformed the control group when comparing each swimmer, each session and the whole sessions. At the level of each individual, more members of the experimental group showed significant downward trend of average lap time (Mann-Kendall trend test, 95% confidence level). While comparing the sessions, the experimental group showed significantly lower lap time than the control group from the sixth session onwards (p-value < 0.05 from t-test). Considering all sessions, the experimental group showed significantly higher progress, lower average lap time, and more consistent records (Mann-Whitney U test at 95% confidence level) than the control group. This study demonstrated that SmartSwim can assist coaching by quantitatively assessing swimmers' performance, leading to more efficient training.

MDPI2022

From mirroring to guiding: A review of the state of art technology for supporting collaborative learning

Patrick Jermann

We review systems that support the management of collaborative interaction, and propose a classification framework built on a simple model of coaching. Our framework distinguishes between mirroring systems, which display basic actions to collaborators, metacognitive tools, which represent the state of interaction via a set of key indicators, and coaching systems, which offer advice based on an interpretation of those indicators. The reviewed systems are further characterized by the type of interaction data they assimilate, the processes they use for deriving higher-level data representations, and the type of feedback they provide to users.

2001

Monitoring weekly progress of front crawl swimmers using IMU-based performance evaluation goal metrics

Kamiar Aminian, Farzin Dadashi, Vincent Gremeaux, Mahdi Hamidi Rad, Fabien Massé

FRONTIERS MEDIA SA2022