Electrocardiographic features for the measurement of drivers' mental workload

Heine, Tobias; Lenis, Gustavo; Reichensperger, Patrick; Beran, Tobias; Dössel, Olaf; Deml, Barbara

doi:10.1016/j.apergo.2016.12.015

Cited by 97 publications

(71 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The experimental result indicated that mean of participants' HR and HRV components increased when the task complexity increased. These findings were consistent with previous studies [13,44]. The participants in driving task needed to continuously exert mental effort to keep alert, and fatigue may have reduced the participants' attention.…”

Section: Discussionsupporting

confidence: 82%

“…Although central nervous system measures (i.e. electrocardiogram) has high reliability in measurement of driver's MWL [13], the applicability of these measures is limited due to the expensive instruments so it was not suitable to the conditions of this experiment. Therefore, the central nervous system measures were not used in this study.…”

Section: Work Performance and Mental Workload Measuresmentioning

confidence: 99%

“…Spectral analysis of HRV enables investigators to decompose HRV into components associated with different biological mechanisms, such as the sympathetic/ parasympathetic ratio or the low frequency power/high frequency power (LF/HF) ratio, the mean inter-beat (RR), the standard deviation of normal RR intervals (SDNN), etc. The SDNN reflects the level of sympathetic activity about parasympathetic activity and has been found to increase with an increase in the level of MWL [13,25].…”

Section: Work Performance and Mental Workload Measuresmentioning

confidence: 99%

“…Thus, the continuous and objective measures (e.g. physiological signal) to assess the MWL in addition to evaluating the overall workload in driving tasks is necessary [13].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Predicting Driver’s Work Performance in Driving Simulator Based on Physiological Indices

Tran

Habiyaremye

et al. 2017

Intelligent Human Computer Interaction

View full text Add to dashboard Cite

Abstract.Developing an early warning model based on mental workload (MWL) to predict the driver's performance is critical and helpful, especially for new or less experienced drivers. This study aims to investigate the correlation between human's MWL and work performance and develop the predictive model in the driving task using driving simulator. The performance measure (number of errors), subjective rating (NASA Task Load Index) as well as six physiological indices were assessed and measured. Additionally, the group method of data handling (GMDH) was used to establish the work performance model. The results indicate that different complexity levels of driving task have a significant effect on the driver's performance, and the predictive performance model integrates different physiological measures shows the validity of the proposed model is well with R 2 = 0.781. The proposed model is expected to provide a reference value of their work performance by giving physiological indices. Based on this model, the driving lesson plans will be proposed to sustain the appropriate MWL as well as improve work performance.

show abstract

Section: Discussionsupporting

confidence: 82%

Section: Work Performance and Mental Workload Measuresmentioning

confidence: 99%

Section: Work Performance and Mental Workload Measuresmentioning

confidence: 99%

“…Thus, the continuous and objective measures (e.g. physiological signal) to assess the MWL in addition to evaluating the overall workload in driving tasks is necessary [13].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Predicting Driver’s Work Performance in Driving Simulator Based on Physiological Indices

Tran

Habiyaremye

et al. 2017

Intelligent Human Computer Interaction

View full text Add to dashboard Cite

show abstract

“…Hence, improvisation, thinking and deciding to solve the problem are needed by drivers [24]. [25] and [18] has supported that drivers need to focus to operate the vehicle and concentrate on the road such as a sign, lamp, announcement, etc. In addition, the brake is not lightly handled and traffic jam rises fatigue and physical demand [14,18,23].…”

Section: Subjective Analysismentioning

confidence: 99%

A Comparative Analysis of Mental Workload between Train and Bus Drivers

Soewardi

Putra

2018

MATEC Web of Conferences

View full text Add to dashboard Cite

Abstract. Human error is one of the crucial problems in land transport, sea transport, and air transport that inflicting some undesirable occurrence such as delay, cancellation, or accident. This incident causes inconvenience and apprehension for passengers. Hence, drivers should avoid some errors in driving by improving alertness and reducing the excessive workload. This study presents a comparative study about mental workload between train and bus drivers based on subjective and objective criteria. NASA-TLX method is used to measure subjective variables by distributing questionnaires. While the content of a drivers' Salivary α-amylase (SAA) is measured as an objective parameter before and after driving. 30 drivers are participated in this study who have experience for more than 3 years. Statistical analysis is conducted to test the hypothesis for a difference. The result of this study shows that bus drivers' SAA level is significantly higher (Asymp. Sig. 0.49) than train drivers for both before (91.99 kU/l) and after (83.19 kU/l) driving activity. Similarly, weighted workload subjectively of bus drivers' mental workload (1051) is higher than train drivers (1031) insignificantly (Asymp. Sig. 0.561).

show abstract

Evaluating mental workload during multitasking in simulated flight

Xie

et al. 2022

Brain and Behavior

View full text Add to dashboard Cite

Background: Pilots must process multiple streams of information simultaneously.Mental workload is one of the main issues in man-machine interactive mode when dealing with multiple tasks. This study aimed to combine functional near-infrared spectroscopy (fNIRS) and electrocardiogram (ECG) to detect changes in mental workload during multitasking in a simulated flight.Methods: Twenty-six participants performed three multitasking tasks at different mental workload levels. These mental workload levels were set by varying the number of subtasks. fNIRS and ECG signals were recorded during tasks. Participants filled in the national aeronautics and space administration task load index (NASA-TLX) scale after each task. The effects of mental workload on scores of NASA-TLX, performance of tasks, heart rate (HR), heart rate variability (HRV), and the prefrontal cortex (PFC) activation were analyzed.Results: Compared to multitasking in lower mental workload conditions, participants exhibited higher scores of NASA-TLX, HR, and PFC activation when multitasking in high mental workload conditions. Their performance was worse during the high mental workload multitasking condition, as evidenced by the higher average tracking distance, smaller number of response times, and longer response time of the meter. The standard deviation of the RR intervals (SDNN) was negatively correlated with subjective mental workload in the low task load condition and PFC activation was positively correlated with HR and subjective mental workload in the medium task load condition. Conclusion:HR and PFC activation can be used to detect changes in mental workload during simulated flight multitasking tasks.

show abstract

Electrocardiographic features for the measurement of drivers' mental workload

Cited by 97 publications

References 50 publications

Predicting Driver’s Work Performance in Driving Simulator Based on Physiological Indices

Predicting Driver’s Work Performance in Driving Simulator Based on Physiological Indices

A Comparative Analysis of Mental Workload between Train and Bus Drivers

Evaluating mental workload during multitasking in simulated flight

Contact Info

Product

Resources

About