Detecting driver fatigue using heart rate variability: A systematic review

Lu, Ke; Dahlman, Anna Sjörs; Karlsson, Johan; Candefjord, Stefan

doi:10.1016/j.aap.2022.106830

Cited by 59 publications

(24 citation statements)

References 73 publications

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“…The definitions and calculation methods for these HRV parameters were adapted from a previous study (Shaffer & Ginsberg, 2017). These HRV metrics were selected because they have been commonly used to infer the occurrence of driver fatigue (Lu et al, 2022). Notably, participants were prohibited from smoking cigarettes while driving and permitted to drink only water to minimize the effect of biochemical factors on HR and HRV.…”

Section: Methodsmentioning

confidence: 99%

Predicting Fatigue-Associated Aberrant Driving Behaviors Using a Dynamic Weighted Moving Average Model With a Long Short-Term Memory Network Based on Heart Rate Variability

et al. 2023

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Objective This study proposed a moving average (MA) approach to dynamically process heart rate variability (HRV) and developed aberrant driving behavior (ADB) prediction models by using long short-term memory (LSTM) networks. Background Fatigue-associated ADBs have traffic safety implications. Numerous models to predict such acts based on physiological responses have been developed but are still in embryonic stages. Method This study recorded the data of 20 commercial bus drivers during their routine tasks on four consecutive days and subsequently asked them to complete questionnaires, including subjective sleep quality, driver behavior questionnaire and the Karolinska Sleepiness Scale. Driving behaviors and corresponding HRV were determined using a navigational mobile application and a wristwatch. The dynamic-weighted MA (DWMA) and exponential-weighted MA were used to process HRV in 5-min intervals. The data were independently separated for training and testing. Models were trained with 10-fold cross-validation strategy, their accuracies were evaluated, and Shapley additive explanation (SHAP) values were used to determine feature importance. Results Significant increases in the standard deviation of NN intervals (SDNN), root mean square of successive heartbeat interval differences (RMSSD), and normalized spectrum of high frequency (nHF) were observed in the pre-event stage. The DWMA-based model exhibited the highest accuracy for both driver types (urban: 84.41%; highway: 80.56%). The SDNN, RMSSD, and nHF demonstrated relatively high SHAP values. Conclusion HRV metrics can serve as indicators of mental fatigue. DWMA-based LSTM could predict the occurrence of the level of fatigue associated with ADBs. Application The established models can be used in realistic driving scenarios.

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Section: Methodsmentioning

confidence: 99%

Predicting Fatigue-Associated Aberrant Driving Behaviors Using a Dynamic Weighted Moving Average Model With a Long Short-Term Memory Network Based on Heart Rate Variability

et al. 2023

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“…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).…”

Section: Measuring Active and Passive Fatigue With Cardiac Measuresmentioning

confidence: 99%

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

Nurmi,

Pakarinen

2023

Journal of Occupational Health Psychology

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In this study, we challenge the commonly held belief that virtual meeting fatigue manifests as exhaustion (i.e., active fatigue) resulting from overloading demands and instead suggest that participation in virtual meetings may lead to increased drowsiness (i.e., passive fatigue) due to underload of stimulation. Using subjective and cardiac measures (heart rate variability), we investigated the relationships between virtual versus face-to-face meetings and different types of fatigue (active and passive) among 44 knowledge workers during real-life meetings (N = 382). Our multilevel path analysis revealed a link between virtual meetings and higher levels of passive fatigue, which then impacted cognitive performance. Additionally, our results suggest that work engagement may act as an individual-level moderator, explaining why some knowledge workers are affected, while others are not. Given the growing amount of time spent in virtual meetings, these findings emphasize the risks to mental energy and cognitive performance and highlight the protective role of high general work engagement.

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“…Furthermore, they can provide valuable information: to evaluate the effect of drugs [ 8 ]; to monitor stress levels and obtain clear evidence of emotion disorders (e.g., anxiety, depression, etc.) [ 1 , 14 ]; to promote lifestyle awareness in people and improve their well-being [ 1 , 8 ]; and to enhance road safety by detecting driver fatigue [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

ECG-Free Heartbeat Detection in Seismocardiography Signals via Template Matching

Centracchio

Parlato

Esposito

et al. 2023

Sensors

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Cardiac monitoring can be performed by means of an accelerometer attached to a subject’s chest, which produces the Seismocardiography (SCG) signal. Detection of SCG heartbeats is commonly carried out by taking advantage of a simultaneous electrocardiogram (ECG). SCG-based long-term monitoring would certainly be less obtrusive and easier to implement without an ECG. Few studies have addressed this issue using a variety of complex approaches. This study proposes a novel approach to ECG-free heartbeat detection in SCG signals via template matching, based on normalized cross-correlation as heartbeats similarity measure. The algorithm was tested on the SCG signals acquired from 77 patients with valvular heart diseases, available from a public database. The performance of the proposed approach was assessed in terms of sensitivity and positive predictive value (PPV) of the heartbeat detection and accuracy of inter-beat intervals measurement. Sensitivity and PPV of 96% and 97%, respectively, were obtained by considering templates that included both systolic and diastolic complexes. Regression, correlation, and Bland–Altman analyses carried out on inter-beat intervals reported slope and intercept of 0.997 and 2.8 ms (R2 > 0.999), as well as non-significant bias and limits of agreement of ±7.8 ms. The results are comparable or superior to those achieved by far more complex algorithms, also based on artificial intelligence. The low computational burden of the proposed approach makes it suitable for direct implementation in wearable devices.

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Detecting driver fatigue using heart rate variability: A systematic review

Cited by 59 publications

References 73 publications

Predicting Fatigue-Associated Aberrant Driving Behaviors Using a Dynamic Weighted Moving Average Model With a Long Short-Term Memory Network Based on Heart Rate Variability

Predicting Fatigue-Associated Aberrant Driving Behaviors Using a Dynamic Weighted Moving Average Model With a Long Short-Term Memory Network Based on Heart Rate Variability

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

ECG-Free Heartbeat Detection in Seismocardiography Signals via Template Matching

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