Using near infrared spectroscopy and heart rate variability to detect mental overload

Mental workload (MWL) has a negative effect on the functional states of watchkeeping officers that ultimately causes collisions and groundings at sea. The aim of this study is to measure the MWL of officers during different navigation conditions. This study was conducted in a bridge simulator with 11 participants. Heart rate variability (HRV) measurements were taken during the 4 steps which have different difficulty levels and subjective assessments were taken at the end of each step by using NASA-TLX. The results of the measurements showed that different levels of navigation tasks caused significantly different levels of MWL and HRV values and MWL and HRV increased when task difficulty increased. Additionally, the correlation between MWL perceived by the participants and the heart rate variability values of the participants was found statistically significant. This study provides an example of predicting MWL for routine navigation operations by using physiological measures in maritime transportation.

Driving in monotonous road environments may cause drivers to become bored and impair their alertness. This inattention may increase the risk of road accidents. Drivers usually respond to visual signals on the road. A large body of research has shown that the level of a driver’s visual attention is closely related to traffic accidents. Visual attention is often studied by tracking drivers’ eye movements. However, few studies to date have explored eye-movement patterns in different road environments such as open roads and tunnel expressways. This dissertation aims to address that research gap by studying drivers’ eye movements and visual characteristics in different driving conditions in real and simulated environments. In the pilot study, drivers’ eye movement patterns were recorded during high-speed driving in tunnels and on expressways in Singapore. Twenty-two drivers participated in the study; they drove a total of 55 km, which included a 9-km section of tunnel. The results of the study showed that drivers experienced longer fixation duration when driving in tunnels than on the open road but experienced a higher number of fixations, higher instantaneous velocity, acceleration, and dispersion on open roads than in tunnels. These findings suggest that drivers’ eye movements are more concentrated and have longer fixation durations in tunnels than in open road driving, indicating that drivers have a greater mental workload when driving in tunnels. However, testing drivers in a real environment has limitations. For example, weather and road characteristics cannot be manipulated. To address this limitation, Experiment 1 was conducted in a driving simulator using virtual reality simulations in UC-win/road. Experiment 1 sought to investigate drivers’ eye movements in various driving environments. Eighteen drivers participated in the experiment and drove on normal roads, expressways, and through underground tunnels in both sunny and rainy weather. The comparisons of their eye-movement patterns (e.g., saccadic parameters and pupil size) in the simulated environments were consistent with those in real open-road environments and tunnels in sunny conditions. The findings suggest that the eye-movement patterns of drivers in a simulator are comparable to those in real environments, indicating its ecological validity. Previous literature has suggested a relationship between eye movement patterns and mental workload. To verify that relationship, mental workloads in Experiments 2 and 3 were directly measured using questionnaires. Dynamic and static hazardous scenarios were simulated to induce different levels of mental workload. Experiment 2 investigated eye movements, driving performance, and mental workload of drivers who encountered static and dynamic obstacles while manual driving. The results from 23 drivers found that their mental workload in dynamic conditions was higher than in static hazardous conditions; it was also higher before encountering hazards than afterwards. The results indicated that pupil size and saccade numbers are effective indicators of mental workload. Experiment 3 was conducted under conditionally automated driving conditions, where participants were involved in tasks related to or not related to driving and requested to resume control manually when their vehicle was in semi-automated driving mode. Drivers’ eye movements and mental workloads were measured when they were driving, especially during the transitions from semi-automated to manual driving. Similar to Experiment 2, the results from 32 drivers found that mental workload was higher for dynamic hazards than for static hazards and higher before hazards were encountered than after. The results also indicated that pupil size is the only effective indicator of mental workload. Taken together, the results of these experiments reveal that pupil size is the most reliable way to measure drivers’ mental workload. Furthermore, in line with the load theory of attention and De Waard (1996)’s theory of workload and performance, it can be concluded that mental workload (indicated by pupil size) and visual workload (by saccadic dispersion) are the key determinants of driving performance and that drivers can only respond to emergencies during periods of medium levels of mental and visual workload (not underload or overload) with available attentional capacity. The findings from this thesis on eye movement and mental workload may be used to monitor drivers’ real-time attention and workload and thus provide guidance for the design of safe-driving autonomous vehicles that consider drivers’ capacity for information processing when driving. Knowledge about the limitations and capacity of human drivers in tandem with automated systems may also help improve user acceptance of autonomous vehicles in the future.

To evaluate neck muscle coactivation across different levels of mental workload during simulated flight tasks. Neck pain (NP) is highly prevalent among military aviators. Given the complex nature within the flight environment, mental workload may be a risk factor for NP. This may induce higher levels of neck muscle coactivity, which over time may accelerate fatigue, increase neck discomfort, and affect flight task performance. Three counterbalanced mental workload conditions represented by simulated flight tasks modulated by interstimulus frequency and complexity were investigated using the Modifiable Multitasking Environment (ModME). The primary measure was a neck coactivation index to describe the neuromuscular effort of the neck muscles as a system. Additional measures included perceived workload (NASA TLX), subjective discomfort, and task performance. Participants (n = 60; 30M, 30F) performed three test conditions over 1hr each while seated in a simulated seating environment. Neck coactivation indices (CoA) and subjective neck discomfort corresponded with increasing level of mental workload. Average CoAs for low, medium, and high workloads were: .0278(SD = .0232), .0286(SD = .0231), and .0295(SD = .0228), respectively. NASA TLX mental, temporal, effort, and overall scores also increased with the level of mental workload assigned. For ModME task performance, the overall performance score, monitoring accuracy, and resource management accuracy decreased while reaction times increased with the increasing level of mental workload. Communication accuracy was lowest with the low mental workload but had higher reaction times relative to increasing workload. Mental workload affects neck muscle coactivation during combinations of simulated flight tasks within a simulated helicopter seating environment. The results of this study provide insights into the physical response to mental workload. With increasing multisensory modalities within the work environment, these insights may assist the consideration of physical effects from cognitive factors.

Mobile healthcare applications offer new opportunities to prevent long-term health damage due to increased mental workload by continuously monitoring physiological signs related to prolonged high workload and providing just-in-time feedback. In order to achieve a day-by-day quantification of mental load, first different load levels which occur during a workday have to be discriminated. This work goes one step towards this goal: we present our experiment design and preliminary results in discriminating different levels of mental workload based on heart rate features obtained from a mobile ECG system. Based on the subjective ratings of the participants under study, we show that all participants perceived the induced load levels as intended from the experiment design. The heart rate variability (HRV) features under investigation could be classified into two distinct groups. Features in the first group, representing markers associated with parasympathetic nervous activity, show a decrease in their values with increased workload. Features in the second group, representing markers associated with sympathetic nervous activity, show an increase of their values with increased workload. These results provide evidence that a mobile ECG system is suited to discriminate different levels of mental workload. This would enable the development of mobile applications to monitor mental workload levels and to prevent long-term damage by giving early warning signs in case of prolonged high workload.

Electrocardiographic features for the measurement of drivers' mental workload

Studies have assessed changesin autonomic variables in terms of heart rate variability (HRV) during meditation, but their results have been conflicting. Recently, Telles and colleagues’ excellent study 1 compared changes in HRV measures during the first four meditative states—cancalata (random thinking), ekagrata (nonmeditative focusing), dharana (meditative focusing), and dhyana (effortless meditation)— described in traditional yoga texts. Their results revealed that the discrepancy in changes in HRV measures might be due to the different stages. At the end of the Discussion section, the authors mention the ultimate state in yoga, samadhi, which manifests with breath suspension. They did not explore this advancedmeditativestateintheirstudy.Therefore,wewould like to share our results about changes in HRV measures during breath suspension. We collected and compared HRV measures in time and frequency domains, along with heart rate and blood pressure, during random thinking, breath meditation, and the advanced meditative stage with breath suspension in 10 meditative sessions conducted by an experienced meditator. The results (Table 1) show that none of HRV measures of time and frequency domains differed significantly during meditation. In contrast, heart rate and systolic blood pressure reliably decreased as meditation advanced. Because the meditation technique used in this study is close to meditative focusing, the results are concordant with the dharana state described in Telles and colleagues’ study. By using a device to monitor the chest circumstance of the meditator, the current study confirmed that the timing of breath suspension coincided with the subjective experience of breath suspension, complete quiescence, and absence of self-boundary. HRV measures, on the other hand, did not concord with the subjective experience of pure quiescence. Because other authors have argued that HRV is not an index of autonomic control of the circulation, 2 whether HRV can be an ideal index of sympathetic or parasympathetic activities remains questionable. According to the current results, the variations of HRV measures, manifested as the ratio of standard deviation to mean, are much greater than those of heart rate and blood pressure. This has also been shown in previous studies. 1,3,4 According to this view, HRV measures cannot be accurate indexes. The current results suggest that heart rate itself is a better HRV measure for meditation and agree with findings from Mukherjee et al. 5 These authors examined the sensitivity to mental effort and reliability of various HRV measures. Their

We have applied functional near-infrared spectroscopy (fNIRS) to the human forehead to distinguish different levels of mental workload on the basis of hemodynamic changes occurring in the prefrontal cortex. We report data on 3 subjects from a protocol involving 3 mental workload levels based on to working memory tasks. To quantify the potential of fNIRS for mental workload discrimination, we have applied a 3-nearest neighbor classification algorithm based on the amplitude of oxyhemoglobin ( HbO 2) and deoxyhemoglobin ( HbR ) concentration changes associated with the working memory tasks. We have found classification success rates in the range of 44–72%, which are significantly higher than the corresponding chance level (for random data) of 19.1%. This work shows the potential of fNIRS for mental workload classification, especially when more parameters (rather than just the amplitude of concentration changes used here) and more sophisticated classification algorithms (rather than the simple 3-nearest neighbor algorithm used here) are considered and optimized for this application.

Objective. Estimation of mental workload (MWL) levels by electroencephalography (EEG)-based mental state monitoring systems has been widely explored. Using event-related potentials (ERPs), elicited by ignored auditory probes, minimizes intrusiveness and has shown high performance for estimating MWL level when tested in laboratory situations. However, when facing real-world applications, the characteristics of ERP waveforms, like latency and amplitude, are often affected by noise, which leads to a decrease in classification performance. One approach to mitigating this is using spatial covariance, which is less sensitive to latency and amplitude distortion. In this study, we used ignored auditory probes in a single-stimulus paradigm and tested Riemannian processed covariance-based features for MWL level estimation in a realistic flight-control task. Approach. We recorded EEG data with an eight-channel system from participants while they performed a simulated drone-control task and manipulated MWL levels (high and low) by task difficulty. We compared support vector machine classification performance based on frequency band power features versus features generated via the Riemannian log map operator from spatial covariance matrices. We also compared accuracy of using data segmented as auditory ERPs versus non-ERPs, for which data windows did not overlap with the ERPs. Main results. Classification accuracy of both types of features showed no significant difference between ERPs and non-ERPs. When we ignore auditory stimuli to perform continuous decoding, covariance-based features in the gamma band had area under the receiver-operating-characteristic curve (AUC) of 0.883, which was significantly higher than band power features (AUC = 0.749). Significance. This study demonstrates that Riemannian-processed covariance features are viable for MWL classification under a realistic experimental scenario.

Monitoring collegiate soccer players during a congested match schedule: Heart rate variability versus subjective wellness measures.

Heart rate variability (HRV) is a noninvasive methodology for evaluating the autonomic nervous system modulation of the sinoatrial node and to identify patients at risk of cardiac disorders. The contemporary therapeutic use for the HRV analysis is for risk stratification in patients with post-myocardial infarction or heart failure who are prone to have arrhythmic death and who would benefit from implantable cardioverter-defibrillators (ICDs). Although multiple HRV measurements have been developed to achieve a better risk stratification, the HRV measurements have been rarely tested to see whether they harbor a significant power to serve as a practical risk predictor. To date, the only reliable metric to predict the benefit from an ICD is a severely reduced ejection fraction; however, the predictive value of the ejection fraction is relatively low. Because of the high negative predictive value of the HRV, a combination with the ejection fraction may be helpful to identify candidates who are unlikely to benefit from ICD therapy. A more sophisticated risk approach that combines the HRV and other known clinical measures should be developed to provide accurate estimates of the risk to allow patients to make informed treatment decisions. Another requirement is to explore the novel HRV measurements for atrial fibrillation. The prevalence of atrial fibrillation rapidly increases among the aging population and is independently associated with a higher risk of ischemic strokes and excess mortality. The HRV measurements specific for atrial fibrillation may facilitate the risk stratification in such a high-risk population.

BackgroundSex differences and causality of the association between heart rate variability (HRV) and atrial fibrillation (AF) in the general population remain unclear.Methods12,334 participants free of AF from the population-based Rotterdam Study were included. Measures of HRV including the standard deviation of normal RR intervals (SDNN), SDNN corrected for heart rate (SDNNc), RR interval differences (RMSSD), RMSSD corrected for heart rate (RMSSDc), and heart rate were assessed at baseline and follow-up examinations. Joint models, adjusted for cardiovascular risk factors, were used to determine the association between longitudinal measures of HRV with new-onset AF. Genetic variants for HRV were used as instrumental variables in a Mendelian randomization (MR) analysis using genome-wide association studies (GWAS) summary-level data.ResultsDuring a median follow-up of 9.4 years, 1302 incident AF cases occurred among 12,334 participants (mean age 64.8 years, 58.3% women). In joint models, higher SDNN (fully-adjusted hazard ratio (HR), 95% confidence interval (CI) 1.24, 1.04–1.47, p = 0.0213), and higher RMSSD (fully-adjusted HR, 95% CI 1.33, 1.13–1.54, p = 0.0010) were significantly associated with new-onset AF. Sex-stratified analyses showed that the associations were mostly prominent among women. In MR analyses, a genetically determined increase in SDNN (odds ratio (OR), 95% CI 1.60, 1.27–2.02, p = 8.36 × 10–05), and RMSSD (OR, 95% CI 1.56, 1.31–1.86, p = 6.32 × 10–07) were significantly associated with an increased odds of AF.ConclusionLongitudinal measures of uncorrected HRV were significantly associated with new-onset AF, especially among women. MR analyses supported the causal relationship between uncorrected measures of HRV with AF. Our findings indicate that measures to modulate HRV might prevent AF in the general population, in particular in women.Graphical abstractAF; atrial fibrillation, GWAS; genome-wide association study, IVW; inverse variance weighted, MR; Mendelian randomization, MR-PRESSO; MR-egger and mendelian randomization pleiotropy residual sum and outlier, RMSSD; root mean square of successive RR interval differences, RMSSDc; root mean square of successive RR interval differences corrected for heart rate, SDNN; standard deviation of normal to normal RR intervals, SDNNc; standard deviation of normal to normal RR intervals corrected for heart rate, WME; weighted median estimator.aRotterdam Study n=12,334bHRV GWAS n=53,174cAF GWAS n=1,030,836

Despite the physiological response of the body to heat has been modeled and described well, the effects of heat stress on cognitive ability are still not well understood. This study aims at measuring mental work load and specifically the effects of hot and humid weather on the level of mental work load among managers and supervisors working on a project of South Pars phases in Bushier (one of the province in south of Iran) . This study is descriptive---analytic and was carried out in two phases: the first phase in winter (February and March 2012) and the second one in summer (July and August 2013). A total of 98 managers and supervisors completed the questionnaires. Method of implementing was the same in both phases, and the same sample took part in both phases. The temperature and humidity information were obtained from synoptic stations. Results of this study showed that there is a direct relationship between the prevailing weather conditions in summer and mental work load. With increasing temperature and relative humidity and in sultry conditions, mental work load of managers and supervisors increased. Based on the results obtained, mental work load of managers and supervisors in South Pars phase projects is high. Nowadays, along with new jobs, work environments are getting more complex and require higher mental activities. Therefore, in addition to assessing physical work load, evaluating mental work load is very important. Also, to reduce mental work load, identifying its causes and factors affecting it is essential. Climate is a potential factor influencing mental work load.

PurposeAlthough obesity has been associated with imbalances in cardiac autonomic nervous system, it is unclear whether there are differential relationships between adiposity measures and heart rate variability (HRV) measures. We aimed to examine differences in the relationship between adiposity measures and HRV indices in a healthy Korean population.Materials and MethodsIn all, 1409 non-smokers (811 males, 598 females) without known histories of cardiovascular (CV), endocrine, or neurological diseases underwent adiposity measurements [(body mass index (BMI), percentage of body fat mass (PBF), and waist-to-hip ratio (WHR)], the HRV assessment (SDNN, RMSSD, LF, HF, LF/HF, and pNN50), and examination for CV risk factors (fasting glucose, LDL-cholesterol, HDL-cholesterol, triglycerides, hs-CRP, and blood pressure).ResultsCompared with BMI and PBF, WHR was more strongly correlated with each HRV index and more likely to predict decreased HRV (<15 percentile vs. ≥15 percentile of each HRV index) in ROC curves analysis. In linear regression analysis, all adiposity measures were inversely associated with each HRV measure before adjusting for age, gender, and CV risk factors (p<0.05). After adjusting for the covariates, WHR was inversely related to RMSSD, LF, and pNN50; PBF with RMSSD, HF, and pNN50; BMI with RMSSD (p<0.05). The inversed association between HRV indices and the gender-specific WHR tertile was significant for subjects with BMI ≥25 kg/m2, but not for those with BMI <25 kg/m2.ConclusionWHR and PBF appear to be better indicators for low HRV than BMI, and the association between abdominal adiposity and HRV may be stronger in overweight subjects.

Long-term heart rate variability (HRV) measures, including the standard deviation of means of successive 5-minute epochs of R-R interval intervals (SDANN) and the power law slope (beta), are important prognostic measures, yet their physiologic basis is unknown. We tested the hypothesis that long-term HRV arises from physical activity in a randomized cross-over study in patients with rate-responsive pacemakers. Ten patients with complete heart block and dual-chamber pacemakers underwent 24-hour periods of ambulatory ECG in each of three pacing modes: atrially tracked, fixed-rate, and rate-responsive pacing. SDANN, ultra low frequency (ULF; frequencies <0.0033 Hz), and beta slope were calculated; and high-frequency power and root mean square of consecutive normal R-R intervals (rMSSD) were calculated as measures of short-term HRV, which have autonomic origins. Long-term HRV measures were similar with atrially tracked and rate-responsive pacing and were much greater than in fixed-rate pacing (SDANN P = 0.0001; ULF P = 0.0001; beta slope P = 0.0002). Short-term HRV measures were similarly low in fixed-rate and rate-responsive pacing (P = NS) and were significantly lower than with atrially tracked pacing (P = 0.0034). Rate-responsive pacing reproduces long-term, but not short-term, measures of HRV, suggesting that they may be markers of heart rate responses to patient activity.

The application of ergonomics in the workplace is one concept that can be done to provide comfort and safety to workers when doing work. This research was conducted on UD workers. Arafat Meubel which is a furniture manufacturing factory. The problem for workers is the number of targets from the company, which causes working time outside the normal working limits and makes workers experience fatigue and excessive workload. This study aims to analyze the level of fatigue and mental workload of workers, and determine the relationship between the two. The methods used are Subjective Self Rating Test to determine the level of fatigue and Nasa-Tlx to determine the level of mental workload, and using the Spearman Rank Test to determine the relationship between fatigue and mental workload. The results obtained from data processing and analysis were that 2 workers experienced high levels of fatigue and 5 workers experienced high levels of mental workload. In addition, there is a relationship between fatigue and mental workload with a correlation coefficient of 0.559, including in the medium relationship category.