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触觉化身:模拟人类触觉认知的触觉感知系统。

Tactile Avatar: Tactile Sensing System Mimicking Human Tactile Cognition.

机构信息

Department of Information and Communication Engineering Daegu Gyeongbuk Institute of Science & Technology (DGIST) Daegu 711-873 Korea.

Department of Neurology University of California San Francisco (UCSF) San Francisco CA 94158 USA.

出版信息

Adv Sci (Weinh). 2021 Feb 8;8(7):2002362. doi: 10.1002/advs.202002362. eCollection 2021 Apr.

DOI:10.1002/advs.202002362
PMID:33854875
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8024994/
Abstract

As a surrogate for human tactile cognition, an artificial tactile perception and cognition system are proposed to produce smooth/soft and rough tactile sensations by its user's tactile feeling; and named this system as "tactile avatar". A piezoelectric tactile sensor is developed to record dynamically various physical information such as pressure, temperature, hardness, sliding velocity, and surface topography. For artificial tactile cognition, the tactile feeling of humans to various tactile materials ranging from smooth/soft to rough are assessed and found variation among participants. Because tactile responses vary among humans, a deep learning structure is designed to allow personalization through training based on individualized histograms of human tactile cognition and recording physical tactile information. The decision error in each avatar system is less than 2% when 42 materials are used to measure the tactile data with 100 trials for each material under 1.2N of contact force with 4cm s of sliding velocity. As a tactile avatar, the machine categorizes newly experienced materials based on the tactile knowledge obtained from training data. The tactile sensation showed a high correlation with the specific user's tendency. This approach can be applied to electronic devices with tactile emotional exchange capabilities, as well as advanced digital experiences.

摘要

作为人类触觉认知的替代品,提出了一种人工触觉感知和认知系统,通过用户的触觉感受产生平滑/柔软和粗糙的触觉;并将该系统命名为“触觉替身”。开发了一种压电触觉传感器,用于动态记录压力、温度、硬度、滑动速度和表面形貌等各种物理信息。对于人工触觉认知,评估了人类对从平滑/柔软到粗糙的各种触觉材料的触觉感受,并发现参与者之间存在差异。由于触觉反应因人而异,因此设计了深度学习结构,通过基于个体人类触觉认知的直方图和记录物理触觉信息的训练来实现个性化。当使用 42 种材料在 1.2N 的接触力下以 4cm/s 的滑动速度对每种材料进行 100 次试验测量触觉数据时,每个替身系统的决策误差小于 2%。作为一个触觉替身,机器根据从训练数据中获得的触觉知识对新体验的材料进行分类。触觉感觉与特定用户的倾向高度相关。这种方法可以应用于具有触觉情感交流能力的电子设备以及先进的数字体验。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae92/8024994/12a516299d30/ADVS-8-2002362-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae92/8024994/dd5fa1a86c00/ADVS-8-2002362-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae92/8024994/1433ce09aa72/ADVS-8-2002362-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae92/8024994/ac083c25a6bd/ADVS-8-2002362-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae92/8024994/49a3780a4786/ADVS-8-2002362-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae92/8024994/18c2d5a30bd8/ADVS-8-2002362-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae92/8024994/12a516299d30/ADVS-8-2002362-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae92/8024994/dd5fa1a86c00/ADVS-8-2002362-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae92/8024994/1433ce09aa72/ADVS-8-2002362-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae92/8024994/ac083c25a6bd/ADVS-8-2002362-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae92/8024994/49a3780a4786/ADVS-8-2002362-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae92/8024994/18c2d5a30bd8/ADVS-8-2002362-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae92/8024994/12a516299d30/ADVS-8-2002362-g001.jpg

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