Multimodal Approach to Assess a Virtual Reality-Based Surgical Training Platform


Demirel D., KELEŞ H. O., Modak C., Basturk K. K., Barker J. R., Halic T.

15th International Conference on Virtual, Augmented and Mixed Reality, VAMR 2023, held as part of the 25th International Conference on Human-Computer Interaction, HCII 2023, Copenhagen, Danimarka, 23 - 28 Temmuz 2023, cilt.14027 LNCS, ss.430-440 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 14027 LNCS
  • Doi Numarası: 10.1007/978-3-031-35634-6_30
  • Basıldığı Şehir: Copenhagen
  • Basıldığı Ülke: Danimarka
  • Sayfa Sayıları: ss.430-440
  • Anahtar Kelimeler: ECG, Mental workload, Skill assessment, Virtual Reality
  • Ankara Üniversitesi Adresli: Evet

Özet

Virtual reality (VR) can bring numerous benefits to the learning process. Combining a VR environment with physiological sensors can be beneficial in skill assessment. We aim to investigate trainees’ physiological (ECG) and behavioral differences during the virtual reality-based surgical training environment. Our finding showed a significant association between the VR-Score and all participants’ total NASA-TLX workload score. The extent of the NASA-TLX workload score was negatively correlated with VR-Score (R2 = 0.15, P < 0.03). In time-domain ECG analysis, we found that RMSSD (R2 = 0.16, P < 0.05) and pNN50 (R2 = 0.15, P < 0.05) scores correlated with significantly higher VR-score of all participants. In this study, we used SVM (linear kernel) and Logistic Regression classification techniques to classify the participants as gamers and non-gamers using data from VR headsets. Both SVM and Logistic Regression accurately classified the participants as gamers and non-gamers with 83% accuracy. For both SVM and Linear Regression, precision was noted as 88%, recall as 83%, and f1-score as 83%. There is increasing interest in characterizing trainees' physiological and behavioral activity profiles in a VR environment, aiming to develop better training and assessment methodologies.