Accurate and early detection of sleepiness, fatigue and stress levels in drivers through Heart Rate Variability parameters: a systematic review

Abstract

Sleepiness, fatigue, and stress in drivers are the leading causes of car crashes. In the late two decades, there is an endeavor to monitor vital signs, stress levels, and fatigue using adapted sensors supported by technological advances. To the best of our knowledge, this systematic review is the first to investigate the role of HRV measurement for sleepiness, fatigue, and stress level monitoring in car drivers. A search was performed in PubMed, Embase, and Cochrane databases using prespecified keywords. Studies were considered for inclusion if they reported original data regarding the association between different HRV measurements and drivers' sleepiness, fatigue, or stress levels. Of the retrieved 749 citations, 19 studies were finally included. The sensibility and specificity of HRV significantly varied across studies, respectively 47.1%-95% and 74.6%-98%. Accuracy was also different, ranging from 56.6% to 95%. Nevertheless, in real-world conditions, confounding factors could affect sympathovagal tone and HRV. Multiple HRV parameters measurement rather than one parameter approach seems to be the optimal strategy for evaluating the vigilance state in drivers that it would be possible to achieve a good performance. As all studies were observational, data should be confirmed in randomized controlled trials. In conclusion, HRV represents a potentially valuable marker for sleepiness, fatigue, and stress monitoring in car drivers. HRV measurements could be implemented in future clinical models and sensors to detect early sleepiness and fatigue and prevent car crashes. More studies with larger populations are needed to support this evidence.

Physiological adjustment effect of visual stimulation by fresh rose flowers on sympathetic nervous activity

“…Participants with high initial sympathetic nervous activity values showed a decrease in values upon visual stimulation with fresh roses, whereas participants with low initial values showed an increase in values upon visual stimulation. In previous studies, LF/HF of HRV has been reported to increase in awakening and stress states ( Järvelin-Pasanen et al, 2018 ; Thielmann et al, 2021 ) and decrease in drowsy states ( Alexandru et al, 2021 ). So far, nature therapy research has focused on the high LF/HF in the stress state that is decreased by nature-derived stimuli.…”

“…The obtained a–a interval time series data were analyzed by the maximum entropy method and high frequency (HF, 0.15–0.40 Hz) and low frequency (LF, 0.04–0.15 Hz) power values were calculated ( Kanaya et al, 2003 ). The LF/HF ratio reflects sympathetic nervous activity ( Pagani et al, 1986 ; Camm et al, 1996 ), which increases during arousal and stress states ( Järvelin-Pasanen et al, 2018 ; Thielmann et al, 2021 ) and decreases during drowsy states ( Alexandru et al, 2021 ). LF/HF data were converted to natural logarithms for normalization ( Kobayashi et al, 2012 ).…”

Virtual meeting fatigue: Exploring the impact of virtual meetings on cognitive performance and active versus passive fatigue.

“…Changes in cardiac activity, such as HRV (the variation in time between each heartbeat), have been closely linked with the subjective experience of specific psychophysiological states, such as stress, fatigue, sleepiness, and drowsiness (for reviews, see Burlacu et al, 2021; Lu et al, 2022). The beat-to-beat variations in heart rate are mainly produced and intensified through the interplay of the heart and brain, which occurs via the transmission of neural signals through the afferent and efferent pathways of the sympathetic and parasympathetic branches of the autonomic nervous system (ANS).…”

“…Scholars have used RMSSD, for example, to study sleepiness and drowsiness (for a recent review, see Matuz et al, 2020), self-regulation (e.g., Zahn et al, 2016), and stress recovery (e.g., Loerbroks et al, 2010) . Most of these studies have been conducted in laboratories (e.g., Burlacu et al, 2021; Kaida et al, 2007) and a few in real-road driving studies (Buendia et al, 2019; Jung et al, 2014; Persson et al, 2020). Repeated HRV monitoring in office-based field studies, however, is still scarce.…”

A Review of Methods and Applications for a Heart Rate Variability Analysis

“…For example, Kim et al (2018) described that an HRV analysis can function as a label for psychological stress when necessary [2]. HRV has been explored for monitoring car drivers' drowsiness, exhaustion, and anxiety levels [11]. Similarly, an HRV analysis has also found applications in detecting athletic performance and fatigue and assessing depression, anxiety, and other chronic conditions [12].…”