User Experience Evaluation in Virtual Reality based on Subjective Feelings and Physiological Signals

2020 ◽  
Vol 2020 (13) ◽  
pp. 60413-1-60413-11
Author(s):  
Yunfang Niu ◽  
Danli Wang ◽  
Ziwei Wang ◽  
Fan Sun ◽  
Kang Yue ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Positive Emotions ◽  
Electrodermal Activity ◽  
Self Report ◽  
Physiological Signals ◽  
Physiological Data ◽  
Self Assessment ◽  
Emotion Experience ◽  
Eye Fatigue ◽  
Electrocardiogram Ecg

At present, the research on emotion in the virtual environment is limited to the subjective materials, and there are very few studies based on objective physiological signals. In this article, the authors conducted a user experiment to study the user emotion experience of virtual reality (VR) by comparing subjective feelings and physiological data in VR and two-dimensional display (2D) environments. First, they analyzed the data of self-report questionnaires, including Self-assessment Manikin (SAM), Positive And Negative Affect Schedule (PANAS) and Simulator Sickness Questionnaire (SSQ). The result indicated that VR causes a higher level of arousal than 2D, and easily evokes positive emotions. Both 2D and VR environments are prone to eye fatigue, but VR is more likely to cause symptoms of dizziness and vertigo. Second, they compared the differences of electrocardiogram (ECG), skin temperature (SKT) and electrodermal activity (EDA) signals in two circumstances. Through mathematical analysis, all three signals had significant differences. Participants in the VR environment had a higher degree of excitement, and the mood fluctuations are more frequent and more intense. In addition, the authors used different machine learning models for emotion detection, and compared the accuracies on VR and 2D datasets. The accuracies of all algorithms in the VR environment are higher than that of 2D, which corroborated that the volunteers in the VR environment have more obvious skin electrical signals, and had a stronger sense of immersion. This article effectively compensated for the inadequacies of existing work. The authors first used objective physiological signals for experience evaluation and used different types of subjective materials to make contrast. They hope their study can provide helpful guidance for the engineering reality of virtual reality.

User Experience Evaluation in Virtual Reality based on Subjective Feelings and Physiological Signals

2019 ◽  
Vol 63 (6) ◽  
pp. 60413-1-60413-11
Author(s):  
Yunfang Niu ◽  
Danli Wang ◽  
Ziwei Wang ◽  
Fan Sun ◽  
Kang Yue ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Positive Emotions ◽  
Electrodermal Activity ◽  
Self Report ◽  
Physiological Signals ◽  
Physiological Data ◽  
Self Assessment ◽  
Emotion Experience ◽  
Eye Fatigue ◽  
Electrocardiogram Ecg

Abstract At present, the research on emotion in the virtual environment is limited to the subjective materials, and there are very few studies based on objective physiological signals. In this article, the authors conducted a user experiment to study the user emotion experience of virtual reality (VR) by comparing subjective feelings and physiological data in VR and two-dimensional display (2D) environments. First, they analyzed the data of self-report questionnaires, including Self-assessment Manikin (SAM), Positive And Negative Affect Schedule (PANAS) and Simulator Sickness Questionnaire (SSQ). The result indicated that VR causes a higher level of arousal than 2D, and easily evokes positive emotions. Both 2D and VR environments are prone to eye fatigue, but VR is more likely to cause symptoms of dizziness and vertigo. Second, they compared the differences of electrocardiogram (ECG), skin temperature (SKT) and electrodermal activity (EDA) signals in two circumstances. Through mathematical analysis, all three signals had significant differences. Participants in the VR environment had a higher degree of excitement, and the mood fluctuations are more frequent and more intense. In addition, the authors used different machine learning models for emotion detection, and compared the accuracies on VR and 2D datasets. The accuracies of all algorithms in the VR environment are higher than that of 2D, which corroborated that the volunteers in the VR environment have more obvious skin electrical signals, and had a stronger sense of immersion. This article effectively compensated for the inadequacies of existing work. The authors first used objective physiological signals for experience evaluation and used different types of subjective materials to make contrast. They hope their study can provide helpful guidance for the engineering reality of virtual reality.

scholarly journals Design of User-Customized Negative Emotion Classifier Based on Feature Selection Using Physiological Signal Sensors

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4253 ◽  
Author(s):  
JeeEun Lee ◽  
Sun K. Yoo
Keyword(s):  
Feature Selection ◽  
Negative Emotion ◽  
Electrodermal Activity ◽  
Cognitive Status ◽  
Physiological Signals ◽  
Self Assessment ◽  
Physiological Signal ◽  
Distribution Shape ◽  
The Difference ◽  
The Individual

First, the Likert scale and self-assessment manikin are used to provide emotion analogies, but they have limits for reflecting subjective factors. To solve this problem, we use physiological signals that show objective responses from cognitive status. The physiological signals used are electrocardiogram, skin temperature, and electrodermal activity (EDA). Second, the degree of emotion felt, and the related physiological signals, vary according to the individual. KLD calculates the difference in probability distribution shape patterns between two classes. Therefore, it is possible to analyze the relationship between physiological signals and emotion. As the result, features from EDA are important for distinguishing negative emotion in all subjects. In addition, the proposed feature selection algorithm showed an average accuracy of 92.5% and made it possible to improve the accuracy of negative emotion recognition.

scholarly journals Human Emotions and Physiological Signals: A Classroom Experiment

2016 ◽  
Vol 12 (04) ◽  
pp. 37 ◽  
Author(s):  
Bruno Patrão ◽  
Samuel Pedro ◽  
Paulo Menezes
Keyword(s):  
Virtual Reality ◽  
Virtual Environments ◽  
Physiological Signals ◽  
Physiological Data ◽  
Laboratory Experience ◽  
Classroom Experiment ◽  
Human Emotions

In this paper we present a Virtual Reality based laboratory experience that can be used to demonstrate the effect that emotions may play in our bodies. For attaining this purpose, a Virtual Reality-based system is presented where three different virtual environments aim at inducing specific sensations and emotions on the students participating in a classroom experiment. The objective is that the students be able to analyze their own physiological data and understand the correlation between data patterns and experienced situation.

scholarly journals Can passive measurement of physiological distress help better predict suicidal thinking?

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Evan M. Kleiman ◽  
Kate H. Bentley ◽  
Joseph S. Maimone ◽  
Hye-In Sarah Lee ◽  
Erin N. Kilbury ◽  
...  
Keyword(s):  
Negative Affect ◽  
Suicide Risk ◽  
Electrodermal Activity ◽  
Model Fit ◽  
Self Report ◽  
Outcome Variable ◽  
Physiological Data ◽  
Suicidal Thinking ◽  
Report Data ◽  
Self Report Data

AbstractThere has been growing interest in using wearable physiological monitors to passively detect the signals of distress (i.e., increases in autonomic arousal measured through increased electrodermal activity [EDA]) that may be imminently associated with suicidal thoughts. Before using these monitors in advanced applications such as creating suicide risk detection algorithms or just-in-time interventions, several preliminary questions must be answered. Specifically, we lack information about whether: (1) EDA concurrently and prospectively predicts suicidal thinking and (2) data on EDA adds to the ability to predict the presence and severity of suicidal thinking over and above self-reports of emotional distress. Participants were suicidal psychiatric inpatients (n = 25, 56% female, M age = 33.48 years) who completed six daily assessments of negative affect and suicidal thinking duration of their psychiatric inpatient stay and 28 days post-discharge, and wore on their wrist a physiological monitor (Empatica Embrace) that passively detects autonomic activity. We found that physiological data alone both concurrently and prospectively predicted periods of suicidal thinking, but models with physiological data alone had the poorest fit. Adding physiological data to self-report models improved fit when the outcome variable was severity of suicidal thinking, but worsened model fit when the outcome was presence of suicidal thinking. When predicting severity of suicidal thinking, physiological data improved model fit more for models with non-overlapping self-report data (i.e., low arousal negative affect) than for overlapping self-report data (i.e., high arousal negative affect). These findings suggest that physiological data, under certain contexts (e.g., when combined with self-report data), may be useful in better predicting—and ultimately, preventing—acute increases in suicide risk. However, some cautious optimism is warranted since physiological data do not always improve our ability to predict suicidal thinking.

scholarly journals Can Passive Measurement of Physiological Distress Help Better Predict Suicidal Thinking?

2021 ◽  
Author(s):  
Evan Kleiman ◽  
Kate Bentley ◽  
Joseph Maimone ◽  
Sarah Lee ◽  
Erin Kilbury ◽  
...  
Keyword(s):  
Negative Affect ◽  
Suicide Risk ◽  
Electrodermal Activity ◽  
Model Fit ◽  
Self Report ◽  
Outcome Variable ◽  
Physiological Data ◽  
Suicidal Thinking ◽  
Report Data ◽  
Self Report Data

Abstract There has been growing interest in using wearable physiological monitors to passively detect the signals of distress (i.e., increases in autonomic arousal measured through increased electrodermal activity [EDA]) that may be imminently associated with suicidal thoughts. Before using these monitors in advanced applications such as creating suicide risk detection algorithms or just-in-time interventions, several preliminary questions must be answered. Specifically, we lack information about whether: (1) EDA concurrently and prospectively predicts suicidal thinking and (2) data on EDA adds to the ability to predict the presence and severity of suicidal thinking over and above self-reports of emotional distress. Participants (n=25, 56% female, M age= 33.48 years) completed six daily assessments of negative affect and suicidal thinking duration of their psychiatric inpatient stay and 28 days post-discharge, and wore on their wrist a physiological monitor (Empatica Embrace) that passively detects autonomic activity. We found that physiological data alone both concurrently and prospectively predicted periods of suicidal thinking, but models with physiological data alone had the poorest fit. Adding physiological data to self-report models improved fit when the outcome variable was severity of suicidal thinking, but worsened model fit when the outcome was presence of suicidal thinking. When predicting severity of suicidal thinking, physiological data improved model fit more for models with non-overlapping self-report data (i.e., low arousal negative affect) than for overlapping self-report data (i.e., high arousal negative affect).These findings suggest that physiological data, under certain contexts, may be useful in better predicting -- and ultimately, preventing -- acute increases in suicide risk. However, some cautious optimism is warranted since physiological data do not always improve our ability to predict suicidal thinking

scholarly journals A Usability Study of Physiological Measurement in School Using Wearable Sensors

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5380 ◽  
Author(s):  
Nattapong Thammasan ◽  
Ivo V. Stuldreher ◽  
Elisabeth Schreuders ◽  
Matteo Giletta ◽  
Anne-Marie Brouwer
Keyword(s):  
Emotional Disorders ◽  
Electrodermal Activity ◽  
Wearable Sensors ◽  
School Setting ◽  
Physiological Signals ◽  
Quantization Noise ◽  
Physiological Data ◽  
Artifact Detection ◽  
Physiological Measurement ◽  
Analog To Digital Conversion

Measuring psychophysiological signals of adolescents using unobtrusive wearable sensors may contribute to understanding the development of emotional disorders. This study investigated the feasibility of measuring high quality physiological data and examined the validity of signal processing in a school setting. Among 86 adolescents, a total of more than 410 h of electrodermal activity (EDA) data were recorded using a wrist-worn sensor with gelled electrodes and over 370 h of heart rate data were recorded using a chest-strap sensor. The results support the feasibility of monitoring physiological signals at school. We describe specific challenges and provide recommendations for signal analysis, including dealing with invalid signals due to loose sensors, and quantization noise that can be caused by limitations in analog-to-digital conversion in wearable devices and be mistaken as physiological responses. Importantly, our results show that using toolboxes for automatic signal preprocessing, decomposition, and artifact detection with default parameters while neglecting differences between devices and measurement contexts yield misleading results. Time courses of students’ physiological signals throughout the course of a class were found to be clearer after applying our proposed preprocessing steps.

scholarly journals Virtual reality exposure therapy for adolescents with fear of public speaking: a non-randomized feasibility and pilot study

2019 ◽  
Vol 13 (1) ◽  
Author(s):  
Smiti Kahlon ◽  
Philip Lindner ◽  
Tine Nordgreen
Keyword(s):  
Heart Rate ◽  
Virtual Reality ◽  
Public Speaking ◽  
Exposure Therapy ◽  
Low Cost ◽  
Early Adulthood ◽  
Pilot Trial ◽  
Treatment Session ◽  
Self Report ◽  
Physiological Data

Abstract Background Public Speaking Anxiety (PSA) is a common anxiety with onset in adolescence and early adulthood. With the advent of consumer virtual reality (VR) technology, VR-delivered exposure therapy is now a scalable and practical treatment option and has previously been shown to be efficacious with adults. In this non-randomized feasibility and pilot trial, we explore the effect of one-session (90 min) VR-delivered exposure therapy for adolescents (aged 13–16) with PSA. Methods A total of 27 adolescents were recruited from Norwegian high schools and completed self-report measures of PSA twice prior to treatment, 1 week after treatment, and at 1 and 3 month follow-up. Heart rate was recorded during the treatment session. A low-cost head-mounted VR display with a custom-built VR stimuli material depicting a cultural and age appropriate classroom and audience were used when a series of speech (exposure exercises) were performed. Results Linear mixed effects model revealed a significant decrease in PSA symptoms (Cohen’s d = 1.53) pre-post treatment, and improvements were maintained at follow-ups. Physiological data revealed a small increase in heart rate during exposure tasks. Based on feedback from the adolescents, the feasibility of the intervention was increased during the trial. Conclusions The results show that low-cost, consumer VR hardware can be used to deliver efficacious treatment for PSA in adolescents, in a feasible one-session format.

OpiTrack

2021 ◽  
Vol 5 (3) ◽  
pp. 1-29
Author(s):  
Bhanu Teja Gullapalli ◽  
Stephanie Carreiro ◽  
Brittany P. Chapman ◽  
Deepak Ganesan ◽  
Jan Sjoquist ◽  
...  
Keyword(s):  
Time Window ◽  
Electrodermal Activity ◽  
Clinical Care ◽  
Opioid Use Disorder ◽  
Physiological Signals ◽  
Physiological Data ◽  
Opioid Use ◽  
Painful Condition ◽  
Targeted Interventions ◽  
Opioid Administration

Opioid use disorder is a medical condition with major social and economic consequences. While ubiquitous physiological sensing technologies have been widely adopted and extensively used to monitor day-to-day activities and deliver targeted interventions to improve human health, the use of these technologies to detect drug use in natural environments has been largely underexplored. The long-term goal of our work is to develop a mobile technology system that can identify high-risk opioid-related events (i.e., development of tolerance in the setting of prescription opioid use, return-to-use events in the setting of opioid use disorder) and deploy just-in-time interventions to mitigate the risk of overdose morbidity and mortality. In the current paper, we take an initial step by asking a crucial question: Can opioid use be detected using physiological signals obtained from a wrist-mounted sensor? Thirty-six individuals who were admitted to the hospital for an acute painful condition and received opioid analgesics as part of their clinical care were enrolled. Subjects wore a noninvasive wrist sensor during this time (1-14 days) that continuously measured physiological signals (heart rate, skin temperature, accelerometry, electrodermal activity, and interbeat interval). We collected a total of 2070 hours (≈ 86 days) of physiological data and observed a total of 339 opioid administrations. Our results are encouraging and show that using a Channel-Temporal Attention TCN (CTA-TCN) model, we can detect an opioid administration in a time-window with an F1-score of 0.80, a specificity of 0.77, sensitivity of 0.80, and an AUC of 0.77. We also predict the exact moment of administration in this time-window with a normalized mean absolute error of 8.6% and R2 coefficient of 0.85.

scholarly journals Sensor-Based Prediction of Mental Effort during Learning from Physiological Data: A Longitudinal Case Study

Signals ◽  
2021 ◽  
Vol 2 (4) ◽  
pp. 886-901
Author(s):  
Ankita Agarwal ◽  
Josephine Graft ◽  
Noah Schroeder ◽  
William Romine
Keyword(s):  
Short Term Memory ◽  
Electrodermal Activity ◽  
Tracking System ◽  
Learning Algorithms ◽  
Mental Effort ◽  
Machine Learning Algorithms ◽  
Self Report ◽  
Physiological Data ◽  
Longitudinal Case Study

Trackers for activity and physical fitness have become ubiquitous. Although recent work has demonstrated significant relationships between mental effort and physiological data such as skin temperature, heart rate, and electrodermal activity, we have yet to demonstrate their efficacy for the forecasting of mental effort such that a useful mental effort tracker can be developed. Given prior difficulty in extracting relationships between mental effort and physiological responses that are repeatable across individuals, we make the case that fusing self-report measures with physiological data within an internet or smartphone application may provide an effective method for training a useful mental effort tracking system. In this case study, we utilized over 90 h of data from a single participant over the course of a college semester. By fusing the participant’s self-reported mental effort in different activities over the course of the semester with concurrent physiological data collected with the Empatica E4 wearable sensor, we explored questions around how much data were needed to train such a device, and which types of machine-learning algorithms worked best. We concluded that although baseline models such as logistic regression and Markov models provided useful explanatory information on how the student’s physiology changed with mental effort, deep-learning algorithms were able to generate accurate predictions using the first 28 h of data for training. A system that combines long short-term memory and convolutional neural networks is recommended in order to generate smooth predictions while also being able to capture transitions in mental effort when they occur in the individual using the device.

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