cs.ROApr 1, 2026

A wearable haptic device for edge and surface simulation

Authors: Rui Chen, Xianlong Mai, Alireza Sanaei, Domenico Chiaradia, Antonio Frisoli, Daniele Leonardis

Organizations: Institute of Mechanical Intelligence, School of Advanced Studies Sant’Anna (SSSA), 56127 Pisa, Italy. · CAS Key Laboratory of Mechanical Behavior and Design of Materials, Institute of Humanoid Robots, School of Engineering Sciences, University of Science and Technology of China, Hefei, China.

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Abstract

Object manipulation is fundamental to virtual reality (VR) applications, yet conventional fingertip haptic devices fail to render certain tactile features relevant for immersive and precise interactions, as i.e. detection of edges. This paper presents a compact, lightweight fingertip haptic device (24.3 g) that delivers distinguishable surface and edge contact feedback through a novel dual-motor mechanism. Pressure distribution characterization using a 6 x 6 flexible sensor array demonstrates distinct contact patterns between the two stimulation modes. A preliminary user study with five participants achieved 93% average classification accuracy across four conditions (edge/surface contact with light/heavy pressure), with mean response times of 2.79 seconds. The results indicate that the proposed device can effectively convey edge and surface tactile cues, potentially enhancing object manipulation fidelity in VR environments.

Figures & tables

Figure 1: Conceptual design of the haptic device showing the dual-motor mechanism for surface and edge contact stimulation.
Figure 2: (a) The wrist-mounted control box. (b) System architecture of the control box.
Figure 3: Experimental setup for pressure distribution measurement using a 6×6 FSR array.
Figure 4: Measured pressure distributions: (a) Edge contact showing concentrated linear pressure. (b) Surface contact showing distributed pressure pattern.
Figure 5: User study protocol: (a) Experimental setup with participant wearing the device. (b) Stimulus presentations.
Figure 6: User study results: (a) Classification accuracy across four conditions showing consistently high performance. (b) Response time distribution indicating rapid stimulus discrimination.
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