ArticleJMIR serious games2020
Biosensor Real-Time Affective Analytics in Virtual and Mixed Reality Medical Education Serious Games: Cohort Study.
Article in JMIR serious games, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
What it found
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Detecting Emotions through Electrodermal Activity in Learning Contexts: A Systematic Review.Sensors (Basel, Switzerland) · 2021Pooled it
- Conceptual Ambiguity Surrounding Gamification and Serious Games in Health Care: Literature Review and Development of Game-Based Intervention Reporting Guidelines (GAMING).Journal of medical Internet research · 2021Pooled it
- Mixed Methods Examination of Challenging and Bothersome Events in Nursing Virtual Simulations: Comparing Screen-Based and Headset VR Modalities.Simulation & gaming · 2026Article
- Electroencephalogram-Based Analysis of Monomodal and Multimodal Interaction in Mixed Reality Games.Sensors (Basel, Switzerland) · 2026Article
- An Experimental Study on the Effectiveness and Usefulness of 360° Virtual Reality Simulation in Korean Medical Education: A Pilot Study.Healthcare (Basel, Switzerland) · 2026Article
- Virtual Reality for Workplace Violence Training of Health Care Workers: Pilot Mixed Methods Usability Study.JMIR serious games · 2025Article
- The stressed heart: Validity evidence supporting mobile heart rate variability applications to detect psychological stress in healthcare learners.Medical education · 2025Article
- Electrodermal Activity (EDA) Morphologies and Prediction of Engagement with Simple Moving Average Crossover: A Mixed-Method Study.Sensors (Basel, Switzerland) · 2024Article
- Feasibility evaluation of radiotherapy positioning system guided by augmented reality and point cloud registration.Journal of applied clinical medical physics · 2024Article
- A virtual reality experiment to study pedestrian perception of future street scenarios.Scientific reports · 2024Article
- Editorial: Immersive media in connected health-volume II.Frontiers in digital health · 2024Article
- Thematic analysis of stakeholder perceptions for co-creative healthcare XR resource design and development; traversing a minefield of opportunities.Frontiers in digital health · 2024Article
- Empowering Health Care Education Through Learning Analytics: In-depth Scoping Review.Journal of medical Internet research · 2023Article
- NeuroSuitUp: System Architecture and Validation of a Motor Rehabilitation Wearable Robotics and Serious Game Platform.Sensors (Basel, Switzerland) · 2023Article
- Developing a Virtual Reality Educational Tool to Stimulate Emotions for Learning: Focus Group Study.JMIR formative research · 2023Article
- Exploring the use of virtual reality in surgical education.World journal of transplantation · 2023Review
- Neurorehabilitation Through Synergistic Man-Machine Interfaces Promoting Dormant Neuroplasticity in Spinal Cord Injury: Protocol for a Nonrandomized Controlled Trial.JMIR research protocols · 2022Article
- Learning from feedback: Evaluation of dynamic decision-making in virtual reality under various repetitive training frameworks.Frontiers in psychology · 2022Article
- The Study of Walking, Walkability and Wellbeing in Immersive Virtual Environments.International journal of environmental research and public health · 2021Article
- Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe role of emotion is crucial to the learning process, as it is linked to motivation, interest, and attention. Affective states are expressed in the brain and in overall biological activity. Biosignals, like heart rate (HR), electrodermal activity (EDA), and electroencephalography (EEG) are physiological expressions affected by emotional state. Analyzing these biosignal recordings can point to a person's emotional state. Contemporary medical education has progressed extensively towards diverse learning resources using virtual reality (VR) and mixed reality (MR) applications.
objectiveThis paper aims to study the efficacy of wearable biosensors for affect detection in a learning process involving a serious game in the Microsoft HoloLens VR/MR platform.
methodsA wearable array of sensors recording HR, EDA, and EEG signals was deployed during 2 educational activities conducted by 11 participants of diverse educational level (undergraduate, postgraduate, and specialist neurosurgeon doctors). The first scenario was a conventional virtual patient case used for establishing the personal biosignal baselines for the participant. The second was a case in a VR/MR environment regarding neuroanatomy. The affective measures that we recorded were EEG (theta/beta ratio and alpha rhythm), HR, and EDA.
resultsResults were recorded and aggregated across all 3 groups. Average EEG ratios of the virtual patient (VP) versus the MR serious game cases were recorded at 3.49 (SD 0.82) versus 3.23 (SD 0.94) for students, 2.59 (SD 0.96) versus 2.90 (SD 1.78) for neurosurgeons, and 2.33 (SD 0.26) versus 2.56 (SD 0.62) for postgraduate medical students. Average alpha rhythm of the VP versus the MR serious game cases were recorded at 7.77 (SD 1.62) μV versus 8.42 (SD 2.56) μV for students, 7.03 (SD 2.19) μV versus 7.15 (SD 1.86) μV for neurosurgeons, and 11.84 (SD 6.15) μV versus 9.55 (SD 3.12) μV for postgraduate medical students. Average HR of the VP versus the MR serious game cases were recorded at 87 (SD 13) versus 86 (SD 12) bpm for students, 81 (SD 7) versus 83 (SD 7) bpm for neurosurgeons, and 81 (SD 7) versus 77 (SD 6) bpm for postgraduate medical students. Average EDA of the VP versus the MR serious game cases were recorded at 1.198 (SD 1.467) μS versus 4.097 (SD 2.79) μS for students, 1.890 (SD 2.269) μS versus 5.407 (SD 5.391) μS for neurosurgeons, and 0.739 (SD 0.509) μS versus 2.498 (SD 1.72) μS for postgraduate medical students. The variations of these metrics have been correlated with existing theoretical interpretations regarding educationally relevant affective analytics, such as engagement and educational focus.
conclusionsThese results demonstrate that this novel sensor configuration can lead to credible affective state detection and can be used in platforms like intelligent tutoring systems for providing real-time, evidence-based, affective learning analytics using VR/MR-deployed medical education resources.
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