Mobile platform for fatigue evaluation: Hrv analysis

Abstract

Recent studies report that heart rate variability (HRV) is related to increased self-control abilities, greater social skills, and fatigue where the decision support systems often use RR interval signal data directly. The RR interval data filtering is compared by using different data sets and artefact removal methods when data were recorded during various training intensity. The results of artefact analysis indicate the intensity of the exercise which increases as the amount of artefact RR interval data increase. Furthermore, for artefact evaluation and fatigue estimation the Poincare plots were selected. The SD1 and SD2 indexes (standard deviation in two orthogonal directions of the Poincare plot) carry similar information to the spectral analysis and simple statistical means (like average pulse and standard error) but are easier calculated and lesser stationarity dependence. In this article Poincare diagram parameters were used to analyze HRV parameters during five stage training sessions for stages with less signal artefacts and evaluate the training impact on human fatigue. The process of HRV signal analysis described in this article is a basis for RR data processing which is important in parameter expertise and method repeatability. Systematic comparison of multiple signal sources enables different RR interval sensor evaluation based on their signal artefacts by using current findings. This research contains different methods of HRV analysis that will be used to support development of a mobile fatigue evaluation system.