We proposed new approaches to estimate runners’ performances, and even predict their future performance, based on their past running speeds during exercises and their CS, using AI. This has been integrated into FlitCoach for internal evaluation by FlitSport.
We first implemented existing algorithms to estimate endurance athletes’ performances, i.e. CPW and W’ balance algorithms – based on runners’ speed, and assessed them on FlitSport’s runners database. We used the critical speed as metric of athletes’ performances, which is a threshold on the running speed below which a runner can theoretically run without stopping. We aimed at estimating and predicting this performance metric precisely.
The first algorithm (CPW) – considered the gold standard - can estimate a runner’s critical speed, but requires data from numerous (months of) exercises from that runner to do so and often underestimate the runners’ performance. Thus, it is not suitable to estimate performance from few data, nor to predict short term future performance, which is required for personalization, as proposed in FlitCoach. We thus proposed the CPW’ balance algorithm, combining CPW and the W’ balance algorithm. W’ balance tracks the expenditure of energy during an exercise, estimated from the runner’s speed. CPW’ balance can provide an estimate for the critical speed of a runner with substantially less data, typically from 3 weeks of data only. Its estimation performances, in terms of a mean squared error (MSE - difference with the CPW ground truth – the lower the better) is 0.28.
Then we proposed a 3rd algorithm based on Riemannian geometry AI, using the runners’ CS. It uses as input a representation of specific running exercises as covariance matrices, integrating CS and running data. Here, the heart rate (BPM, beat per minutes ) was estimated from runners’ smart watches or cardiac belts. Using this new approach, the critical speed can be estimated robustly with the data of a short period of exercises only. It proved very robust, reaching an MSE of around 0.13 only.
The last and main goal for physical performance prediction, was to predict the runner’s performance in the distant future (~6 months in the future), to prepare a competition. A model to do so is available in literature, the Banister model, but fitting its parameters works fine only for some runners but absolutely not for others. Moreover, this model is not very good at predicting performances in a far future, with an MSE of 0.34. Thus, we developed a new approach using AI taking as input the volume of training for the last month and predicting the critical speed for the next month. Thus, if the runner knows his/her planned training volume for each coming month, his/her performance can be predicted in a far future. Moreover, contrary to the Banister model, our new AI model does not require to be trained and fit on each runner’s data, it works across runners, and can thus predict performance for a new runner without data from that runner. This makes it more applicable in practice. It also proved very robust, with an MSE of around 0.05 only.
We also conducted pilot work on cognitive performance prediction. To do so, we had a user perform the so-called N-back tasks. With an N-back task, letters are displayed on screen one by one, and the user is required to identify if the current letter is the same as the letter N characters before. The user was wearing a consumer grade cardiac belt to record BPM. Then, an AI classifier was used to predict the cognitive performance of the user from BPM features, i.e. how well he successfully identified the letters. Our preliminary results suggest that CS can be used to predict – to some extent - success or failure at these cognitive tasks, with a classification accuracy of 62%.
