Abstract
Achieving natural interaction in virtual environments is essential to create realistic simulations in various fields, including healthcare and education. The ability to interact in Virtual Reality (VR) in a natural way, through a combination of visual and physical feedback can greatly enhance the experience and effectiveness of these simulations. Recent works have shown that the lack of haptic and tactile feedback produces significant differences in grasping actions performed in immersive VR, with respect to the same actions performed in the real world. The passive haptics approach, which relies on physical proxies to introduce tactile feedback in VR, has been explored to address this issue. This work focuses on a specific interaction task that involves both hand movements and grasping: pouring coffee into a cup and mimicking the action of drinking it. We take into account three different scenarios: a traditional VR environment where virtual objects don’t have any real counterparts; an MR environment that uses an ecological object substitution technique where the user can interact with real objects that are tracked in real-time and see a virtual counterpart; and the corresponding real scenario. We compute the Minimum Jerk Cost and the Dynamic Time Warping distance between trajectories as metrics to compare movements in the different modalities in terms of their smoothness and trajectory shape, respectively. Our results show that movements in MR environments are smoother and produce more similar trajectories to real-world movements compared to classical VR environments. This indicates that MR with passive haptic feedback could produce more realistic and efficient human movements in virtual environments.
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References
Brooke, J.: Sus: a quick and dirty usability scale. Usabil. Eval. Ind. 189 (1995)
Buckingham, G.: Hand tracking for immersive virtual reality: opportunities and challenges (2021)
Candelieri, A., Fedorov, S., Messina, V.: Efficient kernel-based subsequence search for enabling health monitoring services in IoT-based home setting. Sensors 19, 5192 (2019)
Chessa, M., Maiello, G., Klein, L.K., Paulun, V.C., Solari, F.: Grasping objects in immersive virtual reality. In: IEEE VR, pp. 1749–1754 (2019)
Clarence, A., Knibbe, J., Cordeil, M., Wybrow, M.: Investigating the effect of direction on the limits of haptic retargeting. In: 2022 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pp. 612–621 (2022)
Fligge, N., McIntyre, J., van der Smagt, P.: Minimum jerk for human catching movements in 3D. In: IEEE RAS and EMBS BioRob, pp. 581–586 (2012)
Gerini, L., Solari, F., Chessa, M.: A cup of coffee in mixed reality: analysis of movements’ smoothness from real to virtual. In: 2022 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct), pp. 566–569. IEEE (2022)
Ghasemloonia, A., Maddahi, Y., Zareinia, K., Lama, S., Dort, J.C., Sutherland, G.R.: Surgical skill assessment using motion quality and smoothness. J. Surg. Educ. 74(2), 295–305 (2017)
Girau, E., et al.: A mixed reality system for the simulation of emergency and first-aid scenarios. In: IEEE EMBC, pp. 5690–5695 (2019)
Hoffman, H.: Physically touching virtual objects using tactile augmentation enhances the realism of virtual environments. In: Proceedings. IEEE 1998 Virtual Reality Annual International Symposium (Cat. No. 98CB3618, pp. 59–63 (1998). https://doi.org/10.1109/VRAIS.1998.658423
Huard, A., Chen, M., Sra, M.: CardsVR: a two-person VR experience with passive haptic feedback from a deck of playing cards. In: 2022 IEEE International Symposium on Mixed and Augmented Reality (ISMAR). IEEE, October 2022
Klein, L.K., Maiello, G., Paulun, V.C., Fleming, R.W.: Predicting precision grip grasp locations on three-dimensional objects. PLoS Comput. Biol. 16(8), e1008081 (2020)
Lee, H.S.: Normalization and possibility of classification analysis using the optimal warping paths of dynamic time warping in gait analysis. J. Exerc. Rehabil. 19(1), 85–91 (2023)
Li, W., Luo, Z., Xi, X.: Movement trajectory recognition of sign language based on optimized dynamic time warping. Electronics 9(9) (2020)
Milgram, P., Kishino, F.: A taxonomy of mixed reality visual displays. IEICE Trans. Inf. Syst. 77(12), 1321–1329 (1994)
Morasso, P.: Spatial control of arm movements. Exp. Brain Res. 42(2), 223–227 (1981)
Richardson, M.J., Flash, T.: Comparing smooth arm movements with the two-thirds power law and the related segmented-control hypothesis. J. Neurosci. 22(18), 8201–8211 (2002)
Riofrio, S., Pozo, D., Rosero, J., Vasquez, J.: Gesture recognition using dynamic time warping and kinect: a practical approach, pp. 302–308 (11 2017). https://doi.org/10.1109/INCISCOS.2017.36
Roren, A., et al.: Assessing smoothness of arm movements with jerk: a comparison of laterality, contraction mode and plane of elevation. A pilot study. Front. Bioeng. Biotechnol. 9 (2021)
Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. 26, 43–49 (1978)
Schott, D., Heinrich, F., Stallmeister, L., Hansen, C.: Exploring object and multi-target instrument tracking for AR-guided interventions. Curr. Direct. Biomed. Eng. 8(1), 74–77 (2022). https://doi.org/10.1515/cdbme-2022-0019
Sirizzotti, M., Guercio, S., Lampus, F., Marti, P., Lusuardi, L., Innocenti, A.: Tangible interactions in virtual reality environments. In: ETIS (2020)
Skarbez, R., Smith, M., Whitton, M.C.: Revisiting milgram and Kishino’s reality-virtuality continuum. Front. Virtual Real. 2, 647997 (2021)
Slater, M., Steed, A., McCarthy, J., Maringelli, F.: The influence of body movement on subjective presence in virtual environments. Hum. Factors 40(3), 469–477 (1998). https://doi.org/10.1518/001872098779591368, pMID: 9849105
Usoh, M., et al.: Walking \(>\)walking-in-place\(>\) flying, in virtual environments. In: Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques. SIGGRAPH ’99, pp. 359–364. ACM Press/Addison-Wesley Publishing Co., USA (1999)
Viglialoro, R.M., Condino, S., Turini, G., Carbone, M., Ferrari, V., Gesi, M.: Augmented reality, mixed reality, and hybrid approach in healthcare simulation: a systematic review. Appl. Sci. 11(5), 2338 (2021)
Yu, D., et al.: Haptics in VR using origami-augmented drones. In: 2022 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct), pp. 905–906 (2022)
Yu, X., Xiong, S.: A dynamic time warping based algorithm to evaluate kinect-enabled home-based physical rehabilitation exercises for older people. Sensors 19(13) (2019)
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Gerini, L., Solari, F., Chessa, M. (2023). Passive Haptic Feedback for More Realistic and Efficient Grasping Movements in Virtual Environments. In: De Paolis, L.T., Arpaia, P., Sacco, M. (eds) Extended Reality. XR Salento 2023. Lecture Notes in Computer Science, vol 14218. Springer, Cham. https://doi.org/10.1007/978-3-031-43401-3_1
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