Heart Rate Variability Signal Processing for Safety Driving Using Hilbert-Huang Transform

被引：2

作者：

Hsu, Chih-Ming ^{[1
]}

Lian, Feng-Li ^{[1
]}

Huang, Cheng-Ming ^{[2
]}

Chou, Jen-Hsiang ^{[2
]}

机构：

[1] Natl Taiwan Univ, Dept Elect Engn, Taipei, Taiwan

[2] Natl Taipei Univ Technol, Dept Elect Engn, Taipei 106, Taiwan

来源：

2014 INTERNATIONAL SYMPOSIUM ON COMPUTER, CONSUMER AND CONTROL (IS3C 2014) | 2014年

关键词：

Heart Rate Variability; Time-Frequency Analysis; Empirical Mode Decomposition; DRIVER FATIGUE; SLEEP;

D O I：

10.1109/IS3C.2014.120

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Many studies show that there are a lot of traffic accidents due to drowsiness while driving. Drowsiness is a complex psychophysiology phenomenon whose mechanism has not been explicitly explored. A variety of psychophysiology parameters have been used in previous researches as indicators of drowsiness. In general, the analysis of heart rate variability (HRV) signals is a major approach for detecting driver drowsiness. That approach can analyse the autonomic nervous system, which allows the evaluation of the balance between the sympathetic and parasympathetic influences on heart rhythm of drivers. Time-Frequency Analysis (TFA) of HRV is a powerful skill to make it easier to evaluate how this balance varies with time. Hilbert-Huang Transform (HHT) is a new method of time-frequency analysis, and is applicable to non-linear and non-stationary processes. This work presents a case study for time-frequency domain analysis of heart rate variability for driver fatigue. The experiment results show that HHT of HRV can be characterized to identify physiological features of human body.

引用

页码：434 / 437

页数：4