Encoding of tactile information in hand via skin-integrated wireless haptic interface
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
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Detail(s)
Original language | English |
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Pages (from-to) | 893-903 |
Journal / Publication | Nature Machine Intelligence |
Volume | 4 |
Issue number | 10 |
Online published | 17 Oct 2022 |
Publication status | Published - Oct 2022 |
Link(s)
Abstract
Vivid haptic feedback remains a challenge in truly immersive virtual reality and augmented reality. As the tactile sensitivity among different individuals and different parts of the hand within a person varies widely, a universal method to encode tactile information into faithful feedback in hands according to sensitivity features is urgently needed. In addition, existing haptic interfaces worn on the hand are usually bulky, rigid and tethered by cables, which is a hurdle for accurately and naturally providing haptic feedbacks. Here we report a soft, ultrathin, miniaturized and wireless electrotactile system (WeTac) that delivers current through the hand to induce tactile sensations as the skin-integrated haptic interface. With a relatively high pixel density over the whole hand area, the WeTac can provide tactile stimulation and measure the sensation thresholds of users in a flexible way. By mapping the thresholds for different electrical parameters, personalized threshold data can be acquired to reproduce virtual touching sensations on the hand with optimized stimulation intensity and avoid causing pain. With an accurate control of sensation level, temporal and spatial perception, it allows providing personalized feedback when users interact with virtual objects. This technique is promising for a more vivid touching experience in the virtual world and in human–machine interactions. © The Author(s), under exclusive licence to Springer Nature Limited 2022.
Citation Format(s)
Encoding of tactile information in hand via skin-integrated wireless haptic interface. / Yao, Kuanming; Zhou, Jingkun; Huang, Qingyun et al.
In: Nature Machine Intelligence, Vol. 4, No. 10, 10.2022, p. 893-903.
In: Nature Machine Intelligence, Vol. 4, No. 10, 10.2022, p. 893-903.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review