Parallel Statistical and Machine Learning Methods for Estimation of Physical Load

TL;DR

This paper introduces statistical and machine learning techniques using kurtosis-skewness diagrams to estimate physical load and fatigue from wearable sensor data, enabling real-time monitoring and risk assessment.

Contribution

It presents novel methods combining statistical analysis and machine learning for estimating physical load and fatigue, with applications in real-time activity monitoring.

Findings

Different activity levels can be distinguished from kurtosis-skewness diagrams.

Metrics for instant and accumulated physical fatigue estimation are proposed.

Methods can be extended for modeling complex human activity patterns.

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.