  The new method is proposed to monitor the level of current physical load and
accumulated fatigue by several objective and subjective characteristics. It was
applied to the dataset targeted to estimate the physical load and fatigue by
several statistical and machine learning methods. The data from peripheral
sensors (accelerometer, GPS, gyroscope, magnetometer) and brain-computing
interface (electroencephalography) were collected, integrated, and analyzed by
several statistical and machine learning methods (moment analysis, cluster
analysis, principal component analysis, etc.). The hypothesis 1 was presented
and proved that physical activity can be classified not only by objective
parameters, but by subjective parameters also. The hypothesis 2 (experienced
physical load and subsequent restoration as fatigue level can be estimated
quantitatively and distinctive patterns can be recognized) was presented and
some ways to prove it were demonstrated. Several "physical load" and "fatigue"
metrics were proposed. The results presented allow to extend application of the
machine learning methods for characterization of complex human activity
patterns (for example, to estimate their actual physical load and fatigue, and
give cautions and advice).
