Seeking Accessible Physiological Metrics to Detect Cybersickness in VR

2020 ◽  
Vol 4 (1) ◽  
pp. 1-18
Author(s):  
Takurou Magaki ◽  
Michael Vallance
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Virtual Reality ◽  
Measuring Equipment ◽  
Electrodermal Activity ◽  
Empirical Studies ◽  
Wearable Device ◽  
Data Complexity ◽  
Computer Based ◽  
Learning Data

Virtual reality is predicted to change the way we use technologies in education. However, currently restricting the lack of integration is high cost, unconvincing learning data, complexity of the technologies, and persistently, cybersickness. There are a number of theories as to the causes of cybersickness, but none are infallible. Moreover, many of the evaluation methods and empirical studies are highly specialized physiological analyses requiring sophisticated measuring equipment. Such studies can be difficult to prepare and present unnatural conditions for users engaged in a VR experience. In this research the Empatica E4 wearable device and its ecosystem were utilized to record physiological metrics of heart rate variability and electrodermal activity during customized computer-based and VR tasks to detect the onset of cybersickness. Although inconclusive, the metrics of NNMean, SDNN, RMSSD in HRV data, and SCR width and Peak EDA in EDA data are proposed for further analysis as potential indicators of cybersickness.

scholarly journals Smart Devices and Wearable Technologies to Detect and Monitor Mental Health Conditions and Stress: A Systematic Review

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3461
Author(s):  
Blake Anthony Hickey ◽  
Taryn Chalmers ◽  
Phillip Newton ◽  
Chin-Teng Lin ◽  
David Sibbritt ◽  
...  
Keyword(s):  
Mental Health ◽  
Systematic Review ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Electrodermal Activity ◽  
Smart Devices ◽  
Stress Detection ◽  
Wearable Technologies ◽  
Detection Of Stress ◽  
Anxiety Depression

Recently, there has been an increase in the production of devices to monitor mental health and stress as means for expediting detection, and subsequent management of these conditions. The objective of this review is to identify and critically appraise the most recent smart devices and wearable technologies used to identify depression, anxiety, and stress, and the physiological process(es) linked to their detection. The MEDLINE, CINAHL, Cochrane Central, and PsycINFO databases were used to identify studies which utilised smart devices and wearable technologies to detect or monitor anxiety, depression, or stress. The included articles that assessed stress and anxiety unanimously used heart rate variability (HRV) parameters for detection of anxiety and stress, with the latter better detected by HRV and electroencephalogram (EGG) together. Electrodermal activity was used in recent studies, with high accuracy for stress detection; however, with questionable reliability. Depression was found to be largely detected using specific EEG signatures; however, devices detecting depression using EEG are not currently available on the market. This systematic review highlights that average heart rate used by many commercially available smart devices is not as accurate in the detection of stress and anxiety compared with heart rate variability, electrodermal activity, and possibly respiratory rate.

670. Heart Rate Variability Responses to a Standardized Virtual Reality Exposure in Veterans with PTSD

2017 ◽  
Vol 81 (10) ◽  
pp. S271 ◽  
Author(s):  
Samuel Ridout ◽  
Christopher Spofford ◽  
Mascha van ׳t Wout ◽  
William Unger ◽  
Noah Philip ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Virtual Reality ◽  
Virtual Reality Exposure

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.

scholarly journals Quantitative assessment of the relationship between behavioral and autonomic dynamics during propofol-induced unconsciousness

2020 ◽  
Author(s):  
Sandya Subramanian ◽  
Patrick L. Purdon ◽  
Riccardo Barbieri ◽  
Emery N. Brown
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Prediction Models ◽  
Electrodermal Activity ◽  
Strong Relationship ◽  
Pulse Amplitude ◽  
Physiological Data ◽  
Statistical Tool ◽  
Autonomic Changes ◽  
Before And After

ABSTRACTDuring general anesthesia, both behavioral and autonomic changes are caused by the administration of anesthetics such as propofol. Propofol produces unconsciousness by creating highly structured oscillations in brain circuits. The anesthetic also has autonomic effects due to its actions as a vasodilator and myocardial depressant. Understanding how autonomic dynamics change in relation to propofol-induced unconsciousness is an important scientific and clinical question since anesthesiologists often infer changes in level of unconsciousness from changes in autonomic dynamics. Therefore, we present a framework combining physiology-based statistical models that have been developed specifically for heart rate variability and electrodermal activity with a robust statistical tool to compare behavioral and multimodal autonomic changes before, during, and after propofol-induced unconsciousness. We tested this framework on physiological data recorded from nine healthy volunteers during computer-controlled administration of propofol. We studied how autonomic dynamics related to behavioral markers of unconsciousness: 1) overall, 2) during the transitions of loss and recovery of consciousness, and 3) before and after anesthesia as a whole. Our results show a strong relationship between behavioral state of consciousness and autonomic dynamics. All of our prediction models showed areas under the curve greater than 0.75 despite the presence of non-monotonic relationships among the variables during the transition periods. Our analysis highlighted the specific roles played by fast versus slow changes, parasympathetic vs sympathetic activity, heart rate variability vs electrodermal activity, and even pulse rate vs pulse amplitude information within electrodermal activity. Further advancement upon this work can quantify the complex and subject-specific relationship between behavioral changes and autonomic dynamics before, during, and after anesthesia. However, this work demonstrates the potential of a multimodal, physiologically-informed, statistical approach to characterize autonomic dynamics.

Heart Rate Variability, QT Variability, and Electrodermal Activity during Exercise

2010 ◽  
Vol 42 (3) ◽  
pp. 443-448 ◽  
Author(s):  
SILKE BOETTGER ◽  
CHRISTIAN PUTA ◽  
VIKRAM K. YERAGANI ◽  
LARS DONATH ◽  
HANS-JOSEF MÜLLER ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Electrodermal Activity ◽  
Qt Variability

scholarly journals Early Detection of Hypoglycemia in Type 1 Diabetes Using Heart Rate Variability Measured by a Wearable Device

Diabetes Care ◽  
2019 ◽  
Vol 42 (4) ◽  
pp. 689-692 ◽  
Author(s):  
Marleen Olde Bekkink ◽  
Mats Koeneman ◽  
Bastiaan E. de Galan ◽  
Sebastian J. Bredie
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Type 1 Diabetes ◽  
Early Detection ◽  
Wearable Device

Heart Rate Variability Responses to a Standardized Virtual Reality Exposure in Veterans with PTSD

2017 ◽  
Vol 4 (3) ◽  
pp. 271-280 ◽  
Author(s):  
Samuel J. Ridout ◽  
Christopher M. Spofford ◽  
Mascha van’t Wout-Frank ◽  
Noah S. Philip ◽  
William S. Unger ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Virtual Reality ◽  
Virtual Reality Exposure

scholarly journals Sex Differences in Stress Reactivity to the Trier Social Stress Test in Virtual Reality

2020 ◽  
Author(s):  
Qing Liu ◽  
Wenjuan Zhang
Keyword(s):  
Heart Rate ◽  
Virtual Reality ◽  
Sex Differences ◽  
Social Stress ◽  
Stress Reactivity ◽  
Electrodermal Activity ◽  
Stress Test ◽  
Young Male ◽  
Trier Social Stress Test ◽  
Stress Group

Abstract Background: The aim of the present study is to investigate the sex differences in stress reactivity to the Trier Social Stress Test (TSST) in a virtual reality(TSST-VR). Methods: Healthy young male (n = 30) and female (n = 30) undergraduates were randomly assigned to a psychosocial stress protocol (TSST) condition or to a non-stressful control condition (Placebo-TSST) under VR. Electrodermal activity (EDA), heart rate (HR) and heart rate variability (HRV) were measured throughout the study. The subjective scales of stress and emotion were also conducted. Results: The results showed that after VR, the stress group reported higher stress perceptions than the non-stress group. Compared with females, the males stronger EDA and higher HRV before the VR, and lower HR during VR as well as higher HRV after VR. The correlation between subjective and objective reactivity demonstrated that HRV during VR was negatively correlated to depression and negative affect. The HRV after VR was negatively correlated to the positive coping but was positively correlated to the depression. Conclusions: These findings suggest that the TSST-VR could be used as an available tool for testing gender differences to social stress induction in experimental settings. Compared with females, males were more sensitive to stress.

Sign in / Sign up

Export Citation Format

Share Document