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As exposure to psychosocial hazard at work represents a substantial risk factor for employee health in many modern occupations, being able to accurately assess how employees cope with their working environment is crucial. The workplace is generally accepted as being a dynamic environment, therefore, consideration should be given to the interaction between employees and the acute environmental characteristics of their workplace. The purpose of this paper is to investigate the effects of both acute demand and chronic work-related psychosocial hazard upon employees through ambulatory assessment of heart rate variability and blood pressure.

A within-subjects repeated measures design was used to investigate the relationship between exposure to work-related psychosocial hazard and ambulatory heart rate variability and blood pressure in a cohort of higher education employees. Additionally the effect of acute variation in perceived work-related demand was investigated.

Two dimensions of the Management Standards were found to demonstrate an association with heart rate variability; more hazardous levels of “demand” and “relationships” were associated with decreased standard deviation of the normal-to-normal interval. Significant changes in blood pressure and indices of heart rate variability were observed with increased acute demand.

This is the first attempt to combine the Health and Safety Management Standards Indicator Tool with physiological assessment of employees. The results provide evidence of associations between scores on the indicator tool and ambulatory heart rate variability as well as demonstrating that variation in acute perceived work-related demand is associated with alterations to autonomic and cardiovascular function. This has implications not only for employee health and workplace design but also for future studies employing ambulatory physiological monitoring.

The purpose of this study is to investigate the relationship between selected physical-physiological parameters and efficiency of pistol shooting.

In all, 237 male volunteers, studying at a Turkish Police Academy within the age range of 19-20 years old were investigated. The physical fitness levels were evaluated by valid and reliable test batteries. The efficiency of pistol shooting was evaluated by the total points of the bullets which hit the target from 10 m.

Significant differences were found between the sets of students according to shot accuracy groups for wrist circumference, biceps circumference, femur diameter, hand grip strength, flexibility, aerobic capacity, reaction time, balance, coordination, state anxiety level, anxiety variability, average heart rate, maximal heart rate and heart rate changes. The correlation coefficient between the pistol shooting result and change in heart rate, anxiety variability, mean heart rate during shooting, coordination, state anxiety, maximal heart rate during shooting, balance, hand-grip strength, biceps circumference, femur diameter, wrist circumference and flexibility was significant.

This research identified parameters which were important in profiling a good shooter. In addition, this result could be used for choosing marksmen and guiding shooting training for athletes and police in which shooting skills are important. It was reported that joining simple field physical fitness tests and simple devices such as heart rate polar meters may have an advantage in selecting good shooters. These would save police departments both time and money.

This is the most comprehensive study to date which has evaluated physical fitness in relation to shooting efficiency.

The purpose of this paper is to clarify the relationship between dynamic clothing pressure of women's sports bras and heart rate variation indexes during playing basketball.

Totally, 35 healthy females aged between 20 and 24 were employed as subjects. Their heart rate variability (HRV) data and the clothing pressure values when wearing sports bras of different bust sizes during playing basketball were measured using Polar RS800CX heart rate monitor and Flexi Force201 thin film type pressure sensor with MFF series pressure test system. Their subjective comfort evaluations were conducted by five-point Likert scale. Grey correlation analysis is used to determine the correlation degree of heart rate variation indexes and subjective comfort evaluations to identify the indexes preferably associated with subjective comfort. Multiple regression analysis is applied to investigate multiple correlations between pressure of test points and the indexes selected above.

Combined subjective comfort evaluation with objective HRV evaluation the heart rate variation indexes preferably associated with subjective comfort of these bras were identified, which includes mHR, mRR, RMSSD, pNN50. By the relationship between dynamic clothing pressure and the indexes above, this study finds that the clothing pressure of the chest area during basketball exercise should be controlled in the range of 2.01-4.74 kPa.

The subjects of different body type should be taken into account. Further, the mathematic models from this study show low R2-value so the models should be provided more reliable prediction.

The present study indicates that there is an inevitable connection between heart rate variation indexes and subjective comfort of basketball sports bras and creatively points out that heart rate variation indexes can partly assess the comfortable clothing pressure. The study can offer theoretical basis for sports apparel industry to develop the comfortable basketball sports bra.

The purpose fo this paper is to Monitor and sense the sysmptoms of COVID-19 as a preliminary measure using electronic wearable devices. This variability is sensed by electrocardiograms observed from a multi-parameter monitor and electronic wearable. This field of interest has evolved into a wide area of investigation with today’s advancement in technology of internet of things for immediate sensing and processing information about profound pain. A window span is estimated and reports of profound pain data are used for monitoring heart rate variability (HRV). A median heart rate is considered for comparisons with a diverse range of variable information obtained from sensors and monitors. Observations from healthy patients are introduced to identify how root mean square of difference between inter beat intervals, standard deviation of inter-beat intervals and mean heart rate value are normalized in HRV analysis.

The function of a human heart relates back to the autonomic nervous system, which organizes and maintains a healthy maneuver of inter connected organs. HRV has to be determined for analyzing and reporting the status of health, fitness, readiness and possibilities for recovery, and thus, a metric for deeming the presence of COVID-19. Identifying the variations in heart rate, monitoring and assessing profound pain levels are potential lives saving measures in medical industries.

Experiments are proposed to be done in electrical and thermal point of view and this composition will deliver profound pain levels ranging from 0 to 10. Real time detection of pain levels will assist the care takers to facilitate people in an aging population for a painless lifestyle.

The presented research has documented the stages of COVID-19, symptoms and a mechanism to monitor the progress of the disease through better parameters. Risk factors of the disease are carefully analyzed, compared with test results, and thus, concluded that considering the HRV can study better in the presence of ignorance and negligence. The same mechanism can be implemented along with a global positioning system (GPS) system to track the movement of patients during isolation periods. Despite the stringent control measurements for locking down all industries, the rate of affected people is still on the rise. To counter this, people have to be educated about the deadly effects of COVID-19 and foolproof systems should be in place to control the transmission from affected people to new people. Medications to suppress temperatures, will not be sufficient to alter the heart rate variations, and thus, the proposed mechanism implemented the same. The proposed study can be extended to be associated with Government mobile apps for regular and a consortium of single tracking. Measures can be taken to distribute the low-cost proposal to people for real time tracking and regular updates about high and medium risk patients.

Cardiac arrest is a severe heart anomaly that results in billions of annual casualties. Smoking is a specific hazard factor for cardiovascular pathology, including coronary heart disease, but data on smoking and heart death not earlier reviewed. The Heart Rate Variability (HRV) parameters used to predict cardiac arrest in smokers using machine learning technique in this paper. Machine learning is a method of computing experience based on automatic learning and enhances performances to increase prognosis. This study intends to compare the performance of logistical regression, decision tree, and random forest model to predict cardiac arrest in smokers. In this paper, a machine learning technique implemented on the dataset received from the data science research group MITU Skillogies Pune, India. To know the patient has a chance of cardiac arrest or not, developed three predictive models as 19 input feature of HRV indices and two output classes. These model evaluated based on their accuracy, precision, sensitivity, specificity, F1 score, and Area under the curve (AUC). The model of logistic regression has achieved an accuracy of 88.50%, precision of 83.11%, the sensitivity of 91.79%, the specificity of 86.03%, F1 score of 0.87, and AUC of 0.88. The decision tree model has arrived with an accuracy of 92.59%, precision of 97.29%, the sensitivity of 90.11%, the specificity of 97.38%, F1 score of 0.93, and AUC of 0.94. The model of the random forest has achieved an accuracy of 93.61%, precision of 94.59%, the sensitivity of 92.11%, the specificity of 95.03%, F1 score of 0.93 and AUC of 0.95. The random forest model achieved the best accuracy classification, followed by the decision tree, and logistic regression shows the lowest classification accuracy.

A human navigator attempts to handle the ship for safe navigation by judging navigational information on own ship’s condition, targets and current‐wind effects. He/she has the responsibility of human lives and the economic values to judge. The human navigator maintains high mental workload during the navigational watch keeping. Therefore, we need to develop a support system to reduce the mental workload with human‐system cooperation based on the navigator’s KANSEI, and we must research an index to assess the mental workload for the first step, as the research on the KANSEI of ship’s navigator is not yet available in the world. In addition we depend on the professional’s experience for the assessment. The purpose of this paper is to find characteristics of the mental workload using heart rate variability. The experiment is carried out in six types of sea area on the west side of Japan. The subject is the chief officer of a training ship at Kobe University.

Drowsiness is a common cause of severe road accidents. Therefore, numerous drowsiness detection methods were developed and explored in recent years, especially concepts using physiological measurements achieved promising results. Nevertheless, existing systems have some limitations that hinder their use in vehicles. To overcome these limitations, this paper aims to investigate the development of a low-cost, non-invasive drowsiness detection system, using physiological signals obtained from conventional wearable devices.

Two simulator studies, the first study in a low-level driving simulator (N = 10) to check feasibility and efficiency, and the second study in a high-fidelity driving simulator (N = 30) including two age groups, were conducted. An algorithm was developed to extract features from the heart rate signals and a data set was created by labelling these features according to the identified driver state in the simulator study. Using this data set, binary classifiers were trained and tested using various machine learning algorithms.

The trained classifiers reached a classification accuracy of 99.9%, which is similar to the results obtained by the studies which used intrusive electrodes to detect ECG. The results revealed that heart rate patterns are sensitive to the drivers’ age, i.e. models trained with data from one age group are not efficient in detecting drowsiness for another age group, suggesting to develop universal driver models with data from different age groups combined with individual driver models.

This work investigated the feasibility of driver drowsiness detection by solely using physiological data from wrist-worn wearable devices, such as smartwatches or fitness trackers that are readily available in the consumer market. It was found that such devices are reliable in drowsiness detection.

In light of the growing interest in neuroscience within the managerial and organizational cognition (MOC) scholarly domain at large, this chapter advances current knowledge on core neuroscience methods. It does so by building on the theoretical analysis put forward by Healey and Hodgkinson (2014, 2015), and by offering a thorough – yet accessible – methodological framework for a better understanding of key cognitive and social neuroscience methods. Classifying neuroscience methods based on their degree of resolution, functionality, and anatomical focus, the chapter outlines their features, practicalities, advantages and disadvantages. Specifically, it focuses on functional magnetic resonance imaging, electroencephalography, magnetoencephalography, heart rate variability, and skin conductance response. Equipped with knowledge of these methods, researchers will be able to further their understanding of the potential synergies between management and neuroscience, to better appreciate and evaluate the value of neuroscience methods, and to look at new ways to frame old and new research questions in MOC. The chapter also builds bridges between researchers and practitioners by rebalancing the hype and hopes surrounding the use of neuroscience in management theory and practice.

This paper aims to propose that the nasal temperature is an effective index to evaluate the mental workload of a navigator for effective navigation.

The evaluation comes from the actual on‐board experiment, not simulation. The subject is real bridge teammates; captain, duty officer, and quarter master. The mental workload is evaluated for a lot of navigational situations.

The nasal temperature responds when the navigator makes a decision regarding ship‐handling and collision avoidance, and shows well the whole trend of his decision‐making. Then the nasal temperature takes effect to evaluate the bridge team work among captain, duty officer and quarter master.

Future research is to make cross‐indices with the nasal temperature and the heart rate variability (R‐R interval) complementary to each other where the nasal temperature registers the trend and the R‐R interval registers the quick response of the mental workload.

The paper describes the effective index which is useful to evaluate bridge teammates’ mental workload for effective navigation.

Navigator's skill has been evaluated according to behavior (performance) and a questionnaire as a quantitative evaluation; moreover, the mental workload tries to do it using nasal temperature and heart rate variability.

To add to the existing body of knowledge on the relationship between stress and job performance in policing, we monitored police officers' physiology using Hexoskin shirts while they responded to simulated scenarios.

We employed mixed repeated measures (baseline, intervention, post-intervention), between groups (treatment vs control group) design. Using this approach, our aims were (1) to determine whether an individualized physiological stress profile—a combination of heart rate (HR), heart rate variability (HRV), sympathetic nervous system (SNS) index and parasympathetic nervous system (PNS) index—could be developed for each participant; (2) to investigate the association between physiological stress and scenario performance and (3) to pilot test an intervention for decreasing physiological stress in real time.

We found that it was possible to individualize physiological stress profiles for each participant that alerted us when the participant was becoming stressed. We also found that physiological stress was significantly and negatively/inversely associated with scenario performance. However, our intervention to try and decrease participants' stress in real time was not successful. Several key lessons can be taken from our attempt that could inform future efforts in this area.

This was a small pilot study, precluding generalizability of results. Furthermore, our intervention was simplistic and potentially affected by an experimenter effect. Future research should explore better ways to intervene when officers are becoming physiologically stressed to help them overcome stress in real time and safeguard against the cumulative effects of stress on health and performance.

This research adds to the body of knowledge on physiological stress and job-task performance in police officers.