Monitoring of mental workload levels

Many patients with severe obstructive sleep apnea (OSA) do not complain of excessive daytime sleepiness (EDS), possibly due to increased sympathetic nervous activity (SNA) and accompanying heightened alertness. We hypothesized that in patients with OSA, those without subjective EDS (Epworth Sleepiness Scale, ESS score < 11) would have higher very low frequency (VLF) heart rate variability (HRV) during sleep, reflecting greater sympathetic heart rate modulation than patients with an ESS score ≥ 11. Patients with severe OSA (AHI ≥ 30: 26 with and 65 without heart failure) were divided into those with and without EDS. Heart rate (HR) signals were acquired in stage 2 sleep during periods of recurrent apneas and hypopneas and submitted to coarse graining spectral analysis, which extracts harmonic, neurally mediated contributions to HRV from total spectral power. Because the apnea-hyperpnea cycle entrains muscle SNA at VLF (0 to 0.04 Hz), VLF power was our principal between-group comparison. Subjects without EDS had higher harmonic VLF power (944 ± 839 vs 447 ± 461 msec(2), p = 0.003) than those with EDS, irrespective of the presence or absence of heart failure (1218 ± 944 vs 426 ± 299 msec(2), p = 0.043, and 1029 ± 873 vs 503 ± 533 msec(2), p = 0.003, respectively). ESS scores correlated inversely with VLF power in all (r = -0.294, p = 0.005) and in heart failure subjects (r = -0.468, p = 0.016). Patients with severe OSA but without EDS have higher VLF-HRV than those with EDS. This finding suggests that patients with severe OSA but without EDS have greater sympathetic modulation of HRV than those with EDS that may reflect elevated adrenergically mediated alertness. Taranto Montemurro L; Floras JS; Picton P; Kasai T; Alshaer H; Gabriel JM; Bradley TD. Relationship of heart rate variability to sleepiness in patients with obstructive sleep apnea with and without heart failure.

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.

Electrocardiographic features for the measurement of drivers' mental workload

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

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.

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.

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.

Early and noninvasive identification of heart failure progression is an important adjunct to successful and timely intervention. Severity of heart failure (HF) was assessed by Left Ventricular Ejection Fraction (LVEF). In this paper, we explore the circadian (24-hour) heart rate variability (HRV) features from ''normal" (EF >50%), "at-risk" (EF <40%), and "border-line" (40% ≤ EF ≤ 50%) patient data to determine whether HRV features can predict the stage of heart failure. All coronary artery disease (CAD) 24-hour circadian heart rate data were fitted by a cosinor analysis algorithm. Hourly HRV features from time- and frequency-domains were then extracted from all 24-hour patient data. A one-way ANOVA test was performed followed by a Tukey post-hoc multiple comparison test to investigate the differences among the three groups. The results showed a statistically significant difference between the three groups when using the normalized high frequency (HF Norm), low frequency peak (LF Peak), and the normalized very-low frequency (VLF Norm) for the 05:00-06:00 and 18:00-19:00 time periods. These results highlight a possible link between the circadian variation of sympathetic and parasympathetic nervous system activity and LVEF for CAD patients. The results could be useful in differentiating the various degrees of LVEF by using only noninvasive HRV features derived over a 24-hour period.Clinical relevance- The proposed method could be clinically useful to estimate the extent of LVEF associated with the severity of heart failure by recording the circadian variation of the heart rate in CAD patients. However, further clinical trials on a larger cohort of patients and controls are required.

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 To investigate the effect of aerobatic flight on high performance fighter pilots' physiological characteristics and to analyze their work loads in such flight.Methods Ten healthy male military pilots executed 17 aerobatic flights.Electrocardiograph was recorded during flight,and heart rate (HR) and heart rate variability (HRV) were analyzed.Results During aerobatic flight,HR and HRV showed increasing sympathetic nerve regulation.The mean HR,max HR at aerobatic stage was (92.3 ±13.2),(101.2 ±15.9) beats / min and significantly higher than those in other stages respectively(F=58.479,35.209; P＜0.01).The ratio of LF and HF (LF/HF) significantly increased in aerobatic stage,and the difference with that in acrobatic stages were statistic meaning(F=19.893,P＜0.01).Conclusions High performance fighter pilots endure high G and high G onset in aerobatic flight.The increasing sympathetic nerve regulation indicates pilot's high physical and mental workload. Key words: Acceleration; Heart rate; Workload; Heart rate variability

Effect of varying levels of mental workload on startle eyeblink modulation

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.

We have used machine learning techniques to analyze functional near-infrared spectroscopy (fNIRS) data from the brain of human subjects to classify different levels of mental workload. Preliminary results show potential for fNIRS in human-computer interaction research.

Dissociation between ACE activity and autonomic response to ACE inhibition in patients with heart failure

Does COVID‐19 influence the sympathetic regulation of blood pressure?