Parallel Statistical and Machine Learning Methods for Estimation of Physical Load
This work addresses real-time monitoring and advice for physical activity and fatigue in individuals, but it appears incremental as it builds on existing statistical analysis techniques.
The paper tackled the problem of estimating physical load and fatigue by proposing statistical and machine learning methods based on kurtosis-skewness diagrams from wearable heart monitor data, enabling distinction of activity levels and monitoring of evolution.
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.