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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.

Research on workplace stress measurements varied without much accuracy and effectiveness. The purpose of this paper is to introduce a new quantitative assessment tool emWave Pro Plus (Institute of HeartMath) and compare heart rate variability (HRV) results with the Personal and Organizational Quality Assessment (POQA) and the Perceived Stress Scale (PSS).

This research opted for a correlational study which involves 85 full-time employees who were working at least 40 h per week in a large corporation participated in this study. The POQA and PSS were used to correlate with HRV.

Astonishing findings emerged in this study. Significant positive correlations were found between emotional stress and HRV, and between intention to quit and HRV. In other words, the researchers have to make sense the following surprising findings: the higher the emotional stress an employee faces, the healthier they are. Healthier employees may have higher intentions of quitting their jobs. The surprising results may be attributed to personality, culture, emotional regulation and age among others.

This research fulfills an identified need to validate quantifiable stress measurements especially in a corporate environment. The research also shows promising results, and future studies should continue to tap into HRV as an objective measure of mental health and workplace stress.

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.

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.

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.

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.

This study was performed to confirm that autonomic nervous activity is affected by breathing speed. I hypothesized that prolonged expiratory breathing would promote parasympathetic dominance, whereas rapid breathing would promote sympathetic dominance. Ten healthy men, ages 21-28 years old, were instructed to perform prolonged expiratory breathing (6 seconds expiration, 4 seconds inspiration) after spontaneous breathing and rapid breathing (1 second expiration, 1 second inspiration) after spontaneous breathing; changes in high frequency (HF) and low frequency (LF)/HF of heart rate variability (HRV) were measured during each type of breathing. During prolonged expiratory breathing, parasympathetic nervous function was significantly activated. Conversely, during rapid breathing, parasympathetic nervous function was significantly suppressed. The HRV method assessing sympathetic and parasympathetic modulation in this study is an indirect, noninvasive method with clear limitations. The use of additional techniques should be considered to clarify the relationships between the breathing speed and the mind.

The identification of fatigue status and early intervention to mitigate fatigue can reduce the risk of workplace injuries. Off-the-shelf wearable sensors capable of assessing multiple parameters are available. However, using numerous variables in the fatigue prediction model can elicit data issues. This study aimed at identifying the most relevant variables for measuring occupational fatigue among entry-level construction workers by using common wearable sensor technologies, such as electrocardiogram and actigraphy sensors.

Twenty-two individuals were assigned different task workloads in repeated sessions. Stepwise logistic regression was used to identify the most parsimonious fatigue prediction model. Heart rate variability measurements, standard deviation of NN intervals and power in the low-frequency range (LF) were considered for fatigue prediction. Fast Fourier transform and autoregressive (AR) analysis were employed as frequency domain analysis methods.

The log-transformed LF obtained using AR analysis is preferred for daily fatigue management, whereas the standard deviation of normal-to-normal NN is useful in weekly fatigue management.

This study was conducted with entry-level construction workers who are involved in manual material handling activities. The findings of this study are applicable to this group.

This is the first study to investigate all major measures obtainable through electrocardiogram and actigraphy among current mainstream wearables for monitoring occupational fatigue in the construction industry. It contributes knowledge on the use of wearable technology for managing occupational fatigue among entry-level construction workers engaged in material handling activities.

The evaluation of website usability is the precondition and a critical step for website design and optimization. The purpose of this paper is to investigate and provide empirical evidence of the interrelationships between human physiological metrics and website usability. This study examines how eye-movement metrics and heart rate variability (HRV) evaluate website usability, and then affect users’ online surfing behavior.

A physiological measurement experiment is designed to collect participants’ physiological metrics. This paper proposes an objective measurement model for website usability, and partial least squares is used to analyze the measurement and structural models, based on data collected from 200 participants who had experienced online surfing at least four times.

The analysis supports partially or fully 28 of the 31 hypotheses formulated. The study reveals that human physiological metrics (i.e. fixation duration, fixation count, blink count, HRV) have a strong explanatory ability for website usability.

Data for this study were collected only from mainland China. Therefore, participants may have been influenced by Chinese cultures. The generalizability of this study may be enhanced by collecting data from more diverse samples and validating the model on different cultures.

This study contributes significantly to the industry by providing empirical evidence of the interrelationship between human physiological metrics and website usability. The findings also provide managers with valuable insight into better understanding of the nature of these interrelationships.