Open-Source Physiological Computing Framework using Heart Rate Variability in Mobile Virtual Reality Applications

The heart rate variability (HRV) signal derived from the ECG is a beat-to-beat record of RR intervals and is, as a time series, irregularly sampled. It is common engineering practice to resample this record, typically at 4 Hz, onto a regular time axis for analysis in advance of time domain filtering and spectral analysis based on the DFT. However, it is recognised that resampling introduces noise and frequency bias. The present work describes the implementation of a time-varying filter using a smoothing priors approach based on a Gaussian process model, which does not require data to be regular in time. Its output is directly compatible with the Lomb-Scargle algorithm for power density estimation. A web-based demonstration is available over the Internet for exemplar data. The MATLAB (MathWorks Inc.) code can be downloaded as open source.

Download Full-text

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

Current Treatment Options in Psychiatry ◽

10.1007/s40501-017-0118-9 ◽

2017 ◽

Vol 4 (3) ◽

pp. 271-280 ◽

Cited By ~ 2

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

Download Full-text

Heart rate variability analysis for the assessment of immersive emotional arousal using virtual reality: Comparing real and virtual scenarios

PLoS ONE ◽

10.1371/journal.pone.0254098 ◽

2021 ◽

Vol 16 (7) ◽

pp. e0254098

Author(s):

Javier Marín-Morales ◽

Juan Luis Higuera-Trujillo ◽

Jaime Guixeres ◽

Carmen Llinares ◽

Mariano Alcañiz ◽

...

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Virtual Reality ◽

Emotion Recognition ◽

Cardiovascular Responses ◽

Statistical Hypothesis ◽

Support Vector ◽

Heart Rate Variability Analysis ◽

The Real ◽

Head Mounted Display

Many affective computing studies have developed automatic emotion recognition models, mostly using emotional images, audio and videos. In recent years, virtual reality (VR) has been also used as a method to elicit emotions in laboratory environments. However, there is still a need to analyse the validity of VR in order to extrapolate the results it produces and to assess the similarities and differences in physiological responses provoked by real and virtual environments. We investigated the cardiovascular oscillations of 60 participants during a free exploration of a real museum and its virtualisation viewed through a head-mounted display. The differences between the heart rate variability features in the high and low arousal stimuli conditions were analysed through statistical hypothesis testing; and automatic arousal recognition models were developed across the real and the virtual conditions using a support vector machine algorithm with recursive feature selection. The subjects’ self-assessments suggested that both museums elicited low and high arousal levels. In addition, the real museum showed differences in terms of cardiovascular responses, differences in vagal activity, while arousal recognition reached 72.92% accuracy. However, we did not find the same arousal-based autonomic nervous system change pattern during the virtual museum exploration. The results showed that, while the direct virtualisation of a real environment might be self-reported as evoking psychological arousal, it does not necessarily evoke the same cardiovascular changes as a real arousing elicitation. These contribute to the understanding of the use of VR in emotion recognition research; future research is needed to study arousal and emotion elicitation in immersive VR.

Download Full-text

Effect of Virtual Reality on Stress Reduction and Change of Physiological Parameters Including Heart Rate Variability in People With High Stress: An Open Randomized Crossover Trial

Frontiers in Psychiatry ◽

10.3389/fpsyt.2021.614539 ◽

2021 ◽

Vol 12 ◽

Author(s):

Hyewon Kim ◽

Dong Jun Kim ◽

Seonwoo Kim ◽

Won Ho Chung ◽

Kyung-Ah Park ◽

...

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Virtual Reality ◽

Rating Scale ◽

High Stress ◽

Mental Healthcare ◽

Numeric Rating Scale ◽

Physiological Parameters ◽

Relaxation Phase ◽

Significant Difference

Introduction: Although, attempts to apply virtual reality (VR) in mental healthcare are rapidly increasing, it is still unclear whether VR relaxation can reduce stress more than conventional biofeedback.Methods: Participants consisted of 83 healthy adult volunteers with high stress, which was defined as a score of 20 or more on the Perceived Stress Scale-10 (PSS-10). This study used an open, randomized, crossover design with baseline, stress, and relaxation phases. During the stress phase, participants experienced an intentionally generated shaking VR and serial-7 subtraction. For the relaxation phase, participants underwent a randomly assigned relaxation session on day 1 among VR relaxation and biofeedack, and the other type of relaxation session was applied on day 2. We compared the State-Trait Anxiety Inventory-X1 (STAI-X1), STAI-X2, the Numeric Rating Scale (NRS), and physiological parameters including heart rate variability (HRV) indexes in the stress and relaxation phases.Results: A total of 74 participants were included in the analyses. The median age of participants was 39 years, STAI-X1 was 47.27 (SD = 9.92), and NRS was 55.51 (SD = 24.48) at baseline. VR and biofeedback significantly decreased STAI-X1 and NRS from the stress phase to the relaxation phase, while the difference of effect between VR and biofeedback was not significant. However, there was a significant difference in electromyography, LF/HF ratio, LF total, and NN50 between VR relaxation and biofeedback.Conclusion: VR relaxation was effective in reducing subjectively reported stress in individuals with high stress.

Download Full-text