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.

ECG is an attractive option to assess stress in serious Virtual Reality (VR) applications due to its non-invasive nature. However, the existing Machine Learning (ML) models perform poorly. Moreover, existing studies only perform a binary stress assessment, while to develop a more engaging biofeedback-based application, multi-level assessment is necessary. Existing studies annotate and classify a single experience (e.g. watching a VR video) to a single stress level, which again prevents design of dynamic experiences where real-time in-game stress assessment can be utilized. In this paper, we report our findings on a new study on VR stress assessment, where three stress levels are assessed. ECG data was collected from 9 users experiencing a VR roller coaster. The VR experience was then manually labeled in 10-seconds segments to three stress levels by three raters. We then propose a novel multimodal deep fusion model utilizing spectrogram and 1D ECG that can provide a stress prediction from just a 1-second window. Experimental results demonstrate that the proposed model outperforms the classical HRV-based ML models (9% increase in accuracy) and baseline deep learning models (2.5% increase in accuracy). We also report results on the benchmark WESAD dataset to show the supremacy of the model.

ECG is an attractive option to assess stress in serious Virtual Reality (VR) applications due to its non-invasive nature. However, the existing Machine Learning (ML) models perform poorly. Moreover, existing studies only perform a binary stress assessment, while to develop a more engaging biofeedback-based application, multi-level assessment is necessary. Existing studies annotate and classify a single experience (e.g. watching a VR video) to a single stress level, which again prevents design of dynamic experiences where real-time in-game stress assessment can be utilized. In this paper, we report our findings on a new study on VR stress assessment, where three stress levels are assessed. ECG data was collected from 9 users experiencing a VR roller coaster. The VR experience was then manually labeled in 10-seconds segments to three stress levels by three raters. We then propose a novel multimodal deep fusion model utilizing spectrogram and 1D ECG that can provide a stress prediction from just a 1-second window. Experimental results demonstrate that the proposed model outperforms the classical HRV-based ML models (9% increase in accuracy) and baseline deep learning models (2.5% increase in accuracy). We also report results on the benchmark WESAD dataset to show the supremacy of the model.

Stress can affect a person's performance and health positively and negatively. A lot of the relaxation methods have been suggested to reduce the amount of stress. This study used virtual reality (VR) video games to alleviate stress. Physiological signals captured from Electrocardiogram (ECG), galvanic skin response (GSR), and respiration (RESP) were used to determine if the subject was stressed or relaxed. Time and frequency domain features were then extracted to evaluate stress levels. Frequency domain methods such as low-frequency (LF), high-frequency (HF), LF-HF ratio (LF/HF) are considered the most effective for HRV analysis, Poincare plots are moré discerning visually and shares a 81% correlation with LF/HF ratio. GSR is associated with EDA activity, which only increases due to stress. Stress and relax were classified using Linear Discriminant Analysis (LDA), Decision Tree, Support Vector machine (SVM), Gradient Boost (GB), and Naive Bayes. GB performed the best with an accuracy of 85% after 5 fold cross validation with 100 iterations, which is admirable from a small dataset with 50 samples.

Stress can affect a person's performance and health positively and negatively. A lot of the relaxation methods have been suggested to reduce the amount of stress. This study used virtual reality (VR) video games to alleviate stress. Physiological signals captured from Electrocardiogram (ECG), galvanic skin response (GSR), and respiration (RESP) were used to determine if the subject was stressed or relaxed. Time and frequency domain features were then extracted to evaluate stress levels. Frequency domain methods such as low-frequency (LF), high-frequency (HF), LF-HF ratio (LF/HF) are considered the most effective for HRV analysis, Poincare plots are moré discerning visually and shares a 81% correlation with LF/HF ratio. GSR is associated with EDA activity, which only increases due to stress. Stress and relax were classified using Linear Discriminant Analysis (LDA), Decision Tree, Support Vector machine (SVM), Gradient Boost (GB), and Naive Bayes. GB performed the best with an accuracy of 85% after 5 fold cross validation with 100 iterations, which is admirable from a small dataset with 50 samples.