Integrated Haptic Feedback with Augmented Reality to Improve Pinching and Fine Moving of Objects
Source abstract
Abstract
Hand gestures are essential for interaction in augmented and virtual reality (AR/VR), allowing users to intuitively manipulate virtual objects and engage with human–machine interfaces (HMIs). Accurate gesture recognition is critical for effective task execution. However, users often encounter difficulties due to the lack of immediate and clear feedback from head-mounted displays (HMDs). Current tracking technologies cannot always guarantee reliable recognition, leaving users uncertain about whether their gestures have been successfully detected. To address this limitation, haptic feedback can play a key role by confirming gesture recognition and compensating for discrepancies between the visual perception of fingertip contact with virtual objects and the actual system recognition. The goal of this paper is to compare a simple vibrotactile ring with a full glove device and identify their possible improvements for a fundamental gesture like pinching and fine moving of objects using Microsoft HoloLens 2. Where the pinch action is considered an essential fine motor skill, augmented reality integrated with haptic feedback can be useful to notify the user of the recognition of the gestures and compensate for misaligned visual perception between the tracked fingertip with respect to virtual objects to determine better performance in terms of spatial precision. In our experiments, the participants’ median distance error using bare hands over all axes was 10.3 mm (interquartile range [IQR] = 13.1 mm) in a median time of 10.0 s (IQR = 4.0 s). While both haptic devices demonstrated improvement in participants precision with respect to the bare-hands case, participants achieved with the full glove median errors of 2.4 mm (IQR = 5.2) in a median time of 8.0 s (IQR = 6.0 s), and with the haptic rings they achieved even better performance with median errors of 2.0 mm (IQR = 2.0 mm) in an even better median time of only 6.0 s (IQR= 5.0 s). Our outcomes suggest that simple devices like the described haptic rings can be better than glove-like devices, offering better performance in terms of accuracy, execution time, and wearability. The haptic glove probably compromises hand and finger tracking with the Microsoft HoloLens 2.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Teleoperation and Haptic Systems · Augmented Reality Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Dipartimento di Ingegneria dell’Informazione, University of Pisa, Via Caruso 16, 56122 Pisa, Italy
Endocas Center, Cisanello Hospital, University of Pisa, Via Paradisa 2, 56124 Pisa, Italy
Centro Piaggio, University of Pisa, Largo Lucio Lazzarino 1, 56122 Pisa, Italy
Funding
Italian Ministry of Education and Research (MUR) · European Union · Italian Ministry of University and Research (MUR)—Fondo Italiano per la Scienza (FIS) · EXPERIENCE
References (22)
Show source-provided references
1. Sferrazza, C., Seo, Y., Liu, H., Lee, Y., and Abbeel, P. (2023). The Power of the Senses: Generalizable Manipulation from Vision and Touch through Masked Multimodal Learning. arXiv.10.1109/IROS58592.2024.10802719 ↗
2. Haptic Feedback, Force Feedback, and Force-Sensing in Simulation Training for Laparoscopy: A Systematic Overview10.1016/j.jsurg.2018.06.008 ↗
3. Pseudo-haptics and self-haptics for freehand mid-air text entry in VR10.1016/j.apergo.2022.103819 ↗
4. Norkhairani, A.R., Zaman, H.B., and Ahmad, A. (2011). Simulation for Laparoscopy Surgery with Haptic Element for Medical Students in HUKM: A Preliminary Analysis. Visual Informatics: Sustaining Research and Innovations, Springer.10.1007/978-3-642-25191-7_13 ↗
5. Recent Progress in Flexible Tactile Sensors for Human-Interactive Systems: From Sensors to Advanced Applications10.1002/adma.202005902 ↗
6. Lippi, M., Welle, M.C., Wozniak, M.K., Gasparri, A., and Kragic, D. (2024). Low-Cost Teleoperation with Haptic Feedback through Vision-based Tactile Sensors for Rigid and Soft Object Manipulation. arXiv.10.1109/RO-MAN60168.2024.10731383 ↗
7. Vermeulen, Y., Van Damme, S., Van Wallendael, G., De Turck, F., and Torres Vega, M. (2023, January 6–8). Haptic Interactions for Extended Reality. Proceedings of the 2023 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA.10.1109/ICCE56470.2023.10043499 ↗
8. Pezent, E., Gupta, A., Duhaime, H., O’Malley, M., Israr, A., Samad, M., Robinson, S., Agarwal, P., Benko, H., and Colonnese, N. (2020, January 25–30). Explorations of Wrist Haptic Feedback for AR/VR Interactions with Tasbi. Proceedings of the Adjunct 35th Annual ACM Symposium on User Interface Software and Technology, Honolulu, HI, USA. UIST’22.
9. Augmented tactile-perception and haptic-feedback rings as human-machine interfaces aiming for immersive interactions10.1038/s41467-022-32745-8 ↗
10. ErgoTac: A Tactile Feedback Interface for Improving Human Ergonomics in Workplaces10.1109/LRA.2018.2864356 ↗
11. Haptic augmented reality (HapticAR) for assembly guidance10.1007/s12008-019-00532-3 ↗
12. Chiu, P.H., Lee, S.H., and Yeh, S.C. (2017, January 2–3). Pinch simulation with haptic feedback for stroke rehabilitation: A pilot study. Proceedings of the 2017 2nd International Conference on Information Technology (INCIT), Nakhonpathom, Thailand.10.1109/INCIT.2017.8257885 ↗
13. A hand gesture recognition technique for human–computer interaction10.1016/j.jvcir.2015.01.015 ↗
14. Li, T., Liu, Y., Ma, S., Hu, M., Liu, T., and Song, W. (2022, January 17–21). NailRing: An Intelligent Ring for Recognizing Micro-gestures in Mixed Reality. Proceedings of the 2022 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), Singapore.10.1109/ISMAR55827.2022.00032 ↗
15. (2025, April 29). Hand Tracking-MRTK 2|Microsoft Learn. Available online: https://learn.microsoft.com/it-it/windows/mixed-reality/mrtk-unity/mrtk2/features/input/hand-tracking?view=mrtkunity-2022-05.
16. Schäfer, A., Reis, G., and Stricker, D. (2022, January 12–16). Comparing Controller with the Hand Gestures Pinch and Grab for Picking Up and Placing Virtual Objects. Proceedings of the 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), Christchurch, New Zealand.10.1109/VRW55335.2022.00220 ↗
17. Wilson, A.D. (2006, January 15–18). Robust computer vision-based detection of pinching for one and two-handed gesture input. Proceedings of the 19th Annual ACM Symposium on User Interface Software and Technology, Montreux, Switzerland. UIST06.10.1145/1166253.1166292 ↗
18. van Schaik (’LocalJoost’), J. (2025, April 29). Basic Hand Gesture Recognition with MRTK on HoloLens 2—DotNetByExample-The Next Generation. Available online: https://localjoost.github.io/Basic-hand-gesture-recognition-with-MRTK-on-HoloLens-2/.
19. (2025, June 30). Pinch And Grab Detection-Ultraleap Documentation. Available online: https://docs.ultraleap.com/xr-and-tabletop/xr/unity/plugin/features/pinch-and-grab-detection.html?utm_source=chatgpt.com.
20. (2025, May 16). [Draft Version] bHaptics Hololens Integration-Unity. Available online: https://bhaptics.notion.site/Draft-Version-bHaptics-Hololens-Integration-Unity-f4cd992810d0466b926977f24f303691.
21. Prümper, J. (2025, May 22). ISONORM 9241/110-S; Beurteilung von Software auf Grundlage der Internationalen Ergonomie-Norm. Available online: https://silo.tips/download/fragebogen-isonorm-9241-110-s.
22. Nonparametric statistical tests for the continuous data: The basic concept and the practical use10.4097/kjae.2016.69.1.8 ↗