  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.
