The relationship between HRV parameters and stressful driving situation in the real road

Lee

et al. 2018

Sensors

Routine stress monitoring in daily life can predict potentially serious health impacts. Effective stress monitoring in medical and healthcare fields is dependent upon accurate determination of stress-related features. In this study, we determined the optimal stress-related features for effective monitoring of cumulative stress. We first investigated the effects of short- and long-term stress on various heart rate variability (HRV) features using a rodent model. Subsequently, we determined an optimal HRV feature set using support vector machine-recursive feature elimination (SVM-RFE). Experimental results indicate that the HRV time domain features generally decrease under long-term stress, and the HRV frequency domain features have substantially significant differences under short-term stress. Further, an SVM classifier with a radial basis function kernel proved most accurate (93.11%) when using an optimal HRV feature set comprising the mean of R-R intervals (mRR), the standard deviation of R-R intervals (SDRR), and the coefficient of variance of R-R intervals (CVRR) as time domain features, and the normalized low frequency (nLF) and the normalized high frequency (nHF) as frequency domain features. Our findings indicate that the optimal HRV features identified in this study can effectively and efficiently detect stress. This knowledge facilitates development of in-facility and mobile healthcare system designs to support stress monitoring in daily life.

Section: Introductionmentioning

confidence: 99%

Determination of Optimal Heart Rate Variability Features Based on SVM-Recursive Feature Elimination for Cumulative Stress Monitoring Using ECG Sensor

Lee

et al. 2018

Sensors

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

“…For this goal the estimation of stress and fatigue in first responders is a main concern to prevent cardiac failure and addressed by using a wearable technology (Fig.1) to obtain information on the firefighter's cardiovascular status via electrocardiography (ECG). So far the estimation of stress in real environments using wearable sensors has been mostly focused on driving scenarios [2,3] or to estimate stress during high performance jet flights [4]. Some interesting results were obtained by analyzing the sympathovagal balance between sympathetic and parasympathetic activity using heart rate variability (HRV) measurements [7].…”

mentioning

confidence: 99%

Associating ECG features with firefighter's activities

Pallauf

Gomes

Brás

et al. 2011

In this paper we associate features obtained from ECG signals with the expected levels of stress of real firefighters in action when facing specific events such as fires or car accidents. Five firefighters were monitored using wearable technology collecting ECG signals. Heart rate and heart rate variability features were analyzed in consecutive 5-min intervals during several types of events. A questionnaire was used to rank these types of events according to stress and fatigue and a measure of association was applied to compare this ranking to the ECG features. Results indicate associations between this ranking and both heart rate and heart rate variability features extracted in the time domain. Finally, an example of differences in inter personal responses to stressful events is shown and discussed, motivating future challenges within this research field.

Proceedings of the Human Factors and Ergonomics Society Annual

“…However, other HRV parameters have been shown to decrease in value for increasing stress. These include time-domain standard deviation of the selected RR interval series -SDNN, root mean square of differences of successive RR intervals -RMSSD, and percentage value of consecutive RR intervals that differ more than 50 ms -pNN50 (Lee, et al, 2007a;Lee, et al, 2007b).…”

Section: Introductionmentioning

confidence: 99%

Variations in Road Conditions on Driver Stress

Miller

Boyle

2013

A number of studies have used heart rate variability (HRV) measures to estimate driver stress across different driving conditions. Understanding the constructs of driver stress can provide insights regarding the underlying reasons for safety-critical events. The intent of this study is to evaluate the electrocardiogram (ECG) activities of drivers along a pre-defined route with varying roadway conditions. Heart rate, standard deviation of selected RR interval series (SDNN) and low frequency to high frequency ratio (LF/HF) were examined. An examination of short interval stress at evenly distributed points along the route indicates that some impact was detected from long duration driving and the statistical model was adjusted accordingly to reflect this. The findings suggest that temporal and frequency domain parameters generated similar characteristics regardless of the direction of travel (clockwise or counterclockwise direction). That is, these two parameters provided fairly consistent outcomes regarding driver workload. However, heart rate was not as good an indicator suggesting that this measure may be picking up some other unexplained effects.