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Huacen Wang

dblp:336/5679 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2022
0000-0002-0195-9243ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Haptics and multimodal interaction · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Haptics and multimodal interaction
haptic rendering
0.612022
So-EAGlove: VR Haptic Glove Rendering Softness Sensation With Force-Tunable Electrostatic Adhesive Brakes · IEEE Trans. Robotics 2022
Haptics and multimodal interaction › wearable haptic device
haptic glove
0.212022
So-EAGlove: VR Haptic Glove Rendering Softness Sensation With Force-Tunable Electrostatic Adhesive Brakes · IEEE Trans. Robotics 2022

Methods — techniques the papers use, named apart from their topics

feedforward control · 0.6
YearPublicationVenuePosition
2022 So-EAGlove: VR Haptic Glove Rendering Softness Sensation With Force-Tunable Electrostatic Adhesive Brakes
abstract
Haptic gloves allow the players to feel the virtual world more realistically by providing force feedback. At present, haptic gloves are mainly driven by conventional motors, and for them, being lightweight, low cost, low in power consumption, and intrinsically safe to operators are challenging. Here, we designed a haptic glove (So-EAGlove) integrating a flexible electrostatic adhesive brake to resist human fingers with a tunable braking force and render a softness sensation. This glove weighs only 51 g, but is capable of simulating objects in a large range of Young's modulus from 540 Pa to 5.4 MPa. The electrostatic adhesive brake costs only approximately 2.43 mW during operation, i.e., more than three days powered by a small button battery. We built a feedforward control model and evaluated its performance. Experimental results show that this glove can generate an accurate force to follow the force-displacement profile of the corresponding real objects. The error is less than 7%, barely noticeable by the subjects. The subjective tests also demonstrate that little statistical difference exists between the real objects and the virtual objects for the subjects.
Quan Xiong, Xuanquan Liang, Daiyue Wei, Huacen Wang, Renjie Zhu, Jianjun Mao, Hongqiang Wang 0003
IEEE Trans. Robotics4