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Multimodal Approach to Assess a Virtual Reality-based Surgical Training Platform
Doga Demirel1, Hasan Onur Keles2, Chinmoy Modak1
1Florida Polytechnic University, Lakeland, Florida, USA.
Virtual, Augmented and Mixed Reality : 15Th International Conference, VAMR 2023, Held As Part of the 25Th HCI International Conference, HCII 2023, Copenhagen, Denmark, July 23-28, 2023, Proceedings. VAMR (Conference) (15Th : 2023 : Cope
|November 14, 2023
Summary
Virtual reality (VR) surgical training shows that higher performance (VR-Score) correlates with lower workload and improved physiological markers. This study also accurately distinguished gamers from non-gamers using VR data.
Area of Science:
- Medical Education
- Human-Computer Interaction
- Physiological Monitoring
Background:
- Virtual reality (VR) offers significant advantages for learning.
- Integrating physiological sensors with VR enhances skill assessment capabilities.
Purpose of the Study:
- To investigate physiological (ECG) and behavioral differences in trainees during VR surgical training.
- To correlate performance metrics with workload and physiological responses.
- To classify participants as gamers or non-gamers based on VR interaction data.
Main Methods:
- Trainees underwent virtual reality-based surgical training.
- Physiological data (ECG) and performance scores (VR-Score) were collected.
- NASA-TLX was used for workload assessment.
- Support Vector Machine (SVM) and Logistic Regression were employed for gamer classification.
Main Results:
- A significant negative correlation was found between VR-Score and NASA-TLX workload (R²=0.15, P<0.03).
- Time-domain ECG metrics, RMSSD (R²=0.16, P<0.05) and pNN50 (R²=0.15, P<0.05), positively correlated with higher VR-Scores.
- Both SVM and Logistic Regression achieved 83% accuracy in classifying gamers and non-gamers, with 88% precision and 83% recall/F1-score.
Conclusions:
- VR surgical training performance is linked to reduced perceived workload and specific physiological responses.
- Objective classification of user types (gamers vs. non-gamers) is feasible using VR interaction data.
- Characterizing physiological and behavioral profiles in VR is crucial for developing advanced training and assessment tools.
