Publication

A Bayesian Approach for Pervasive Estimation of Breaststroke Velocity Using a Wearable IMU

Kamiar Aminian, Farzin Dadashi, Grégoire Millet
2015
Journal Articles

Abstract

An ubiquitous assessment of swimming velocity (main metric of the performance) is essential for the coach to provide a tailored feedback to the trainee. We present a probabilistic framework for the data-driven estimation of the swimming velocity at every cycle using a low-cost wearable inertial measurement unit (IMU). The statistical validation of the method on 15 swimmers shows that an average relative error of 0.1±9.6 % and high correlation with the tethered reference system (rX,Y = 0.91) is achievable. Besides, a simple tool to analyze the influence of sacrum kinematics on the performance is provided.

Official source

https://infoscience.epfl.ch/entities/publication/c8c4db63-b2d5-4c6f-b882-b4dbfd1a3846

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Kamiar Aminian, Farzin Dadashi, Grégoire Millet
2015
Journal Articles

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/c8c4db63-b2d5-4c6f-b882-b4dbfd1a3846

About this result

Related concepts (31)

Statistical model validation

In statistics, model validation is the task of evaluating whether a chosen statistical model is appropriate or not. Oftentimes in statistical inference, inferences from models that appear to fit their data may be flukes, resulting in a misunderstanding by researchers of the actual relevance of their model. To combat this, model validation is used to test whether a statistical model can hold up to permutations in the data.

Bayesian statistics

Bayesian statistics (ˈbeɪziən or ˈbeɪʒən ) is a theory in the field of statistics based on the Bayesian interpretation of probability where probability expresses a degree of belief in an event. The degree of belief may be based on prior knowledge about the event, such as the results of previous experiments, or on personal beliefs about the event. This differs from a number of other interpretations of probability, such as the frequentist interpretation that views probability as the limit of the relative frequency of an event after many trials.

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In statistics, mean absolute error (MAE) is a measure of errors between paired observations expressing the same phenomenon. Examples of Y versus X include comparisons of predicted versus observed, subsequent time versus initial time, and one technique of measurement versus an alternative technique of measurement. MAE is calculated as the sum of absolute errors divided by the sample size: It is thus an arithmetic average of the absolute errors , where is the prediction and the true value.

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