Multilevel Longitudinal Analysis of Shooting Performance as a Function of Stress and Cardiovascular Responses

Spangler, Derek P.; Alam, Sazedul; Rahman, Sohanur; Crone, Joshua C.; Robucci, Ryan; Banerjee, Nilanjan; Kerick, Scott E.; Brooks, Justin

doi:10.1109/taffc.2020.2995769

Cited by 9 publications

(15 citation statements)

References 145 publications

(179 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In simpler cases, stress and workload indicators are used to substantiate the basic effectiveness of virtual stimuli in training. Physiological indicators can be used to show that implemented scenarios are really able to elicit desired stress responses (Loreto et al, 2018;Prachyabrued et al, 2019;Spangler et al, 2020).…”

Section: Simulator Trainingmentioning

confidence: 99%

A Systematic Review of Physiological Measurements, Factors, Methods, and Applications in Virtual Reality

Halbig

Latoschik

2021

Front. Virtual Real.

View full text Add to dashboard Cite

Measurements of physiological parameters provide an objective, often non-intrusive, and (at least semi-)automatic evaluation and utilization of user behavior. In addition, specific hardware devices of Virtual Reality (VR) often ship with built-in sensors, i.e. eye-tracking and movements sensors. Hence, the combination of physiological measurements and VR applications seems promising. Several approaches have investigated the applicability and benefits of this combination for various fields of applications. However, the range of possible application fields, coupled with potentially useful and beneficial physiological parameters, types of sensor, target variables and factors, and analysis approaches and techniques is manifold. This article provides a systematic overview and an extensive state-of-the-art review of the usage of physiological measurements in VR. We identified 1,119 works that make use of physiological measurements in VR. Within these, we identified 32 approaches that focus on the classification of characteristics of experience, common in VR applications. The first part of this review categorizes the 1,119 works by field of application, i.e. therapy, training, entertainment, and communication and interaction, as well as by the specific target factors and variables measured by the physiological parameters. An additional category summarizes general VR approaches applicable to all specific fields of application since they target typical VR qualities. In the second part of this review, we analyze the target factors and variables regarding the respective methods used for an automatic analysis and, potentially, classification. For example, we highlight which measurement setups have been proven to be sensitive enough to distinguish different levels of arousal, valence, anxiety, stress, or cognitive workload in the virtual realm. This work may prove useful for all researchers wanting to use physiological data in VR and who want to have a good overview of prior approaches taken, their benefits and potential drawbacks.

show abstract

Section: Simulator Trainingmentioning

confidence: 99%

A Systematic Review of Physiological Measurements, Factors, Methods, and Applications in Virtual Reality

Halbig

Latoschik

2021

Front. Virtual Real.

View full text Add to dashboard Cite

show abstract

“…A total of 521, 672, and 671 records for the Bl, LD, and HD classes, respectively, were used to validate the proposed method. A detailed description of the VR study and data collection protocol has been published in [68].…”

Section: B Data Acquisition Protocolmentioning

confidence: 99%

Classification of VR-Gaming Difficulty Induced Stress Levels using Physiological (EEG & ECG) Signals and Machine Learning

Pratiher¹,

Radhakrishnan²,

Sahoo³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

"This work has been submitted to the IEEE Transactions on Affective Computing for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible." Physiological sensing has long been an indispensable fixture for virtual reality (VR) gaming studies. Moreover, VR induced stressors are increasingly being used to assess the impact of stress on an individual’s health and well-being. This study discusses the results of experimental research comprising multimodal physiological signal acquisition from 31 participants during a Go/No-Go VR-based shooting exercise where participants had to shoot the enemy and spare the friendly targets. The study encompasses multiple sessions, including orientation, thresholding, and shooting. The shooting sessions consist of tasks under low & high difficulty induced stress conditions with in-between baseline segments. Machine learning (ML) performance with heart rate variability (HRV) from electrocardiogram (ECG) and electroencephalogram (EEG) features outperform the prevalent methods for four different VR gaming difficulty-induced stress (GDIS) classification problems (CPs). Further, the significance of the HRV predictors and different brain region activations from EEG is deciphered using statistical hypothesis testing (SHT). The ablation study shows the efficacy of multimodal physiological sensing for different gaming difficulty-induced stress classification problems (GDISCPs) in a VR shooting task.

show abstract

“…Bodala et al [2021] used growth modelling to study the longitudinal changes in participants' ratings of human and teleoperated robot coaches delivering mindfulness training. Spangler et al [2020] used multilevel modelling to analyze changes in performance as a function of stress and cardiovascular response at multiple time scales such as task, block and session. McDuff et al [2019] used hierarchical regression to select best model from incremental linear regression models to understand multiple factors affecting longitudinal emotional progression across several weeks.…”

Section: Longitudinal Studies and Analysesmentioning

confidence: 99%

Dynamic Bayesian Network Modelling of User Affect and Perceptions of a Teleoperated Robot Coach during Longitudinal Mindfulness Training

Bodala¹,

Güneş²

2021

Preprint

View full text Add to dashboard Cite

Longitudinal interaction studies with Socially Assistive Robots (SARs) are crucial to ensure that the robot is relevant for long-term use and its perceptions are not prone to the novelty effect. In this paper, we present a dynamic Bayesian network (DBN) model to capture the longitudinal interactions participants had with a teleoperated robot coach (RC) delivering mindfulness training sessions. The DBN model is used to study complex, temporal interactions between the participants' self-reported personality traits, weekly baseline wellbeing scores, session ratings, and participants' facial AUs elicited during the sessions during a 5-week longitudinal study. DBN modelling involves learning a graphical representation that facilitates intuitive understanding of how multiple components contribute to the longitudinal changes in session ratings corresponding to the perceptions of the robot coach and the changes in the relaxation/calm levels before and after each session. The learnt model captures the following within-and between-sessions aspects of the longitudinal interaction study -(i) influences of the 5 personality dimensions on the facial AU states and the session ratings; (ii) influence of facial AU states on the session ratings; and (iii) the influences within the items of the session ratings. The structure of the DBN was learnt using the first 3 time-points and the obtained model is used to predict the session ratings of the remaining 2 time-points of the 5-week longitudinal data. The goodness-of-fit of the model is quantified in terms of subject-wise RM SE and R 2 scores. We also demonstrate two applications of the model, namely imputation of missing values in the dataset and estimation of longitudinal session ratings of a new participant with a given personality profile. The obtained DBN model thus facilitates learning of conditional dependency structure between variables in the longitudinal data and offers inferences and conceptual understanding which is not possible through other regression methodologies.

show abstract

Multilevel Longitudinal Analysis of Shooting Performance as a Function of Stress and Cardiovascular Responses

Cited by 9 publications

References 145 publications

A Systematic Review of Physiological Measurements, Factors, Methods, and Applications in Virtual Reality

A Systematic Review of Physiological Measurements, Factors, Methods, and Applications in Virtual Reality

Classification of VR-Gaming Difficulty Induced Stress Levels using Physiological (EEG & ECG) Signals and Machine Learning

Dynamic Bayesian Network Modelling of User Affect and Perceptions of a Teleoperated Robot Coach during Longitudinal Mindfulness Training

Contact Info

Product

Resources

About