scholarly journals Effects on Heart Rate Variability of Stress Level Responses to the Properties of Indoor Environmental Colors: A Preliminary Study

2021 ◽  
Vol 18 (17) ◽  
pp. 9136
Author(s):  
Jiyoung Oh ◽  
Haengwoo Lee ◽  
Heykyung Park
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Stress Level ◽  
Well Being ◽  
Human Vision ◽  
Stress Factors ◽  
Indoor Space ◽  
Stress Levels ◽  
Measurement Record ◽  
Reliable Scale

Color is the most potent stimulating factor affecting human vision, and the environmental color of an indoor space is a spatial component that affects the environmental stress level. As one of the methods of assessing the physiological response of the autonomic nervous system that influences stress, heart rate variability (HRV) has been utilized as a tool for measuring the user’s stress response in color environments. This study aims to identify the effects of the changes of hue, brightness, and saturation in environmental colors on the HRV of two groups with different stress levels—the stress potential group (n = 15) and the healthy group (n = 12)—based on their stress level indicated by the Psychosocial Well-being Index (PWI). The ln(LF), ln(HF), and RMSSD values collected during the subjects’ exposure to 12 environments colors of red and yellow with adjusted saturation and brightness, were statistically analyzed using t-test and two-way ANOVA. The results show that the HRV values in the two groups did not significantly vary in response to the changes in hue, brightness and saturation. The two groups’ stress factors distinguished according to the stress levels by the PWI scale affected the In(LF) parameter, which demonstrates that the PWI index can be utilized as a reliable scale for measuring stress levels. The ultra-short HRV measurement record and the use of a sole In(LF) parameter for stress assessment are regarded as the limitations of this study.

scholarly journals Continuous measurement of stress levels in naturalistic settings using heart rate variability: An experience-sampling study driving a machine learning approach

ACTA IMEKO ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 239
Author(s):  
Pietro Cipresso ◽  
Silvia Serino ◽  
Francesca Borghesi ◽  
Gennaro Tartarisco ◽  
Giuseppe Riva ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Psychological Stress ◽  
Stress Level ◽  
Experience Sampling ◽  
Mobile System ◽  
Stress Levels ◽  
Decision Module ◽  
Leave One Out ◽  
Research Challenge

<p class="Abstract"><span id="page629R_mcid43" class="markedContent"><span dir="ltr">Developing automatic methods to measure psychological stress in everyday life has become an important research challenge. Here, we describe the design and implementation of a personalized mobile system for the detection of psychological stress episodes based on Heart-Rate Variability (HRV) indices. The system’s architecture consists of three main modules: a mobile acquisition module; an analysis-decision module; and a visualization-reporting module. Once the stress level is calculated by the mobile system, the visualization-reporting module of the mobile application displays the current stress level of the user. We carried out an experience-sampling study, involving 15 participants, monitored longitudinally, for a total of 561 ECG analyzed, to select the HRV features which best correlate with self-reported stress levels. Drawing on these results, a personalized classification system is able to automatically detect stress events from those HRV features, after a training phase in which the system learns from the subjective responses given by the user. Finally, the performance of the classification task was evaluated on the empirical dataset using the leave one out cross-validation process. Preliminary findings suggest that incorporating self-reported psychological data in the system’s knowledge base allows for a more accurate and personalized definition of the stress response measured by HRV indices.</span></span></p>

scholarly journals Trends in Heart-Rate Variability Signal Analysis

2021 ◽  
Vol 3 ◽  
Author(s):  
Syem Ishaque ◽  
Naimul Khan ◽  
Sri Krishnan
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Electrodermal Activity ◽  
Well Being ◽  
Good Health ◽  
Machine Learning Techniques ◽  
Video Gaming ◽  
Clinical Events ◽  
Learning Techniques

Heart rate variability (HRV) is the rate of variability between each heartbeat with respect to time. It is used to analyse the Autonomic Nervous System (ANS), a control system used to modulate the body's unconscious action such as cardiac function, respiration, digestion, blood pressure, urination, and dilation/constriction of the pupil. This review article presents a summary and analysis of various research works that analyzed HRV associated with morbidity, pain, drowsiness, stress and exercise through signal processing and machine learning methods. The points of emphasis with regards to HRV research as well as the gaps associated with processes which can be improved to enhance the quality of the research have been discussed meticulously. Restricting the physiological signals to Electrocardiogram (ECG), Electrodermal activity (EDA), photoplethysmography (PPG), and respiration (RESP) analysis resulted in 25 articles which examined the cause and effect of increased/reduced HRV. Reduced HRV was generally associated with increased morbidity and stress. High HRV normally indicated good health, and in some instances, it could signify clinical events of interest such as drowsiness. Effective analysis of HRV during ambulatory and motion situations such as exercise, video gaming, and driving could have a significant impact toward improving social well-being. Detection of HRV in motion is far from perfect, situations involving exercise or driving reported accuracy as high as 85% and as low as 59%. HRV detection in motion can be improved further by harnessing the advancements in machine learning techniques.

Heat Stress Training Camps for Endurance Sport: A Descriptive Case Study of Successful Monitoring in 2 Ironman Triathletes

2020 ◽  
Vol 15 (1) ◽  
pp. 146-150
Author(s):  
Ed Maunder ◽  
Andrew E. Kilding ◽  
Christopher J. Stevens ◽  
Daniel J. Plews
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Heat Stress ◽  
Well Being ◽  
Self Report ◽  
Endurance Athletes ◽  
Resting Heart Rate ◽  
Resting Heart Rate Variability ◽  
Stress Training

A common practice among endurance athletes is to purposefully train in hot environments during a “heat stress camp.” However, combined exercise-heat stress poses threats to athlete well-being, and therefore, heat stress training has the potential to induce maladaptation. This case study describes the monitoring strategies used in a successful 3-week heat stress camp undertaken by 2 elite Ironman triathletes, namely resting heart rate variability, self-report well-being, and careful prescription of training based on previously collected physiological data. Despite the added heat stress, training volume very likely increased in both athletes, and training load very likely increased in one of the athletes, while resting heart rate variability and self-report well-being were maintained. There was also some evidence of favorable metabolic changes during routine laboratory testing following the camp. The authors therefore recommend that practitioners working with endurance athletes embarking on a heat stress training camp consider using the simple strategies employed in the present case study to reduce the risk of maladaptation and nonfunctional overreaching.

scholarly journals Mental Stress Detection via Heart Rate Variability using Machine Learning

2020 ◽  
Vol 12 (3) ◽  
pp. 431-444
Author(s):  
Bimo Sunarfri Hantono ◽  
◽  
Lukito Edi Nugroho ◽  
Paulus Insap Santosa ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Working Memory ◽  
Heart Rate Variability ◽  
Mental Stress ◽  
Nearest Neighbor ◽  
Frequency Domain Analysis ◽  
K Nearest Neighbor ◽  
Stress Levels ◽  
Back Task

Mental stress is an undesirable condition for everyone. Increased stress can cause many problems, such as depression, heart attacks, and strokes. Psychophysiological conditions possible use as a reference to a person’s mental state of stress. The development of mobile device technology, along with the accompanying sensors, can be used to measure the psychophysiological condition of its users. Heart rate allows measured from the photoplethysmography signal utilizing a smartphone or smartwatch. The heart rate variability is currently one of the most studied methods for assessing mental stress. Our objective is to analyze stress levels on the subjects when performing tasks on the smartphone. This study involved 41 students as respondents. Their heart rate was recorded using a smartphone while they were doing the n-back tasks. The n-back task is one of the performance tasks used to measure working memory and working memory capacity. In this study, the n-back task was also used as a stressor. The heart rate dataset and n-back task results are then processed and analyzed using machine learning to determine stress levels. Compared with three other algorithms (neural network, discriminant analysis, and naïve Bayes), the k-nearest neighbor algorithm is most appropriate to use in the classification of time and frequency domain analysis.

scholarly journals WiStress

2021 ◽  
Vol 5 (3) ◽  
pp. 1-37
Author(s):  
Unsoo Ha ◽  
Sohrab Madani ◽  
Fadel Adib
Keyword(s):  
Stress Level ◽  
Critical Role ◽  
Well Being ◽  
User Participation ◽  
Daily Activities ◽  
Stress Monitoring ◽  
Ultra Low Power ◽  
Ubiquitous Sensing ◽  
Active User ◽  
Stress Levels

Stress plays a critical role in our lives, impacting our productivity and our long-term physiological and psychological well-being. This has motivated the development of stress monitoring solutions to better understand stress, its impact on productivity and teamwork, and help users adapt their habits toward more sustainable stress levels. However, today's stress monitoring solutions remain obtrusive, requiring active user participation (e.g., self-reporting), interfering with people's daily activities, and often adding more burden to users looking to reduce their stress. In this paper, we introduce WiStress, the first system that can passively monitor a user's stress levels by relying on wireless signals. WiStress does not require users to actively provide input or to wear any devices on their bodies. It operates by transmitting ultra-low-power wireless signals and measuring their reflections off the user's body. WiStress introduces two key innovations. First, it presents the first machine learning network that can accurately and robustly extract heartbeat intervals (IBI's) from wireless reflections without constraints on a user's daily activities. Second, it introduces a stress classification framework that combines the extracted heartbeats with other wirelessly captured stress-related features in order to infer a subject's stress level. We built a prototype of WiStress and tested it on 22 different subjects across different environments in both stress-induced and free-living conditions. Our results demonstrate that WiStress has high accuracy (84%-95%) in inferring a person's stress level in a fully-automated way, paving the way for ubiquitous sensing systems that can monitor stress and provide feedback to improve productivity, health, and well-being.

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