Parallel Statistical and Machine Learning Methods for Estimation of Physical Load

Abstract

Several statistical and machine learning methods are proposed to estimate the type and intensity of physical load and accumulated fatigue . They are based on the statistical analysis of accumulated and moving window data subsets with construction of a kurtosis-skewness diagram. This approach was applied to the data gathered by the wearable heart monitor for various types and levels of physical activities, and for people with various physical conditions. The different levels of physical activities, loads, and fitness can be distinguished from the kurtosis-skewness diagram, and their evolution can be monitored. Several metrics for estimation of the instant effect and accumulated effect (physical fatigue) of physical loads were proposed. The data and results presented allow to extend application of these methods for modeling and characterization of complex human activity patterns, for example, to estimate the actual and accumulated physical load and fatigue, model the potential dangerous development, and give cautions and advice in real time.

Prediction of Physical Load Level by Machine Learning Analysis of Heart Activity after Exercises

“…For example, several statistical and machine learning methods were proposed recently to classify the type and estimate the intensity of physical load and accumulated fatigue. The data for various types and levels of physical activities and for people with various physical conditions were collected by the wearable heart monitor [21]. That is why some of the wellknown ML methods were applied here to find the potential correlation between the training internal loads during training (in-exercise load) and after training (post-exercise load).…”