A Wearable Bidirectional Human–Machine Interface: Merging Motion Capture and Vibrotactile Feedback in a Wireless Bracelet

Author:

Kindel Julian1ORCID,Andreas Daniel1ORCID,Hou Zhongshi1ORCID,Dwivedi Anany2ORCID,Beckerle Philipp13ORCID

Affiliation:

1. Chair of Autonomous Systems and Mechatronics, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany

2. Artificial Intelligence (AI) Institute, Division of Health, Engineering, Computing and Science, University of Waikato, Hamilton 3216, New Zealand

3. Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany

Abstract

Humans interact with the environment through a variety of senses. Touch in particular contributes to a sense of presence, enhancing perceptual experiences, and establishing causal relations between events. Many human–machine interfaces only allow for one-way communication, which does not do justice to the complexity of the interaction. To address this, we developed a bidirectional human–machine interface featuring a bracelet equipped with linear resonant actuators, controlled via a Robot Operating System (ROS) program, to simulate haptic feedback. Further, the wireless interface includes a motion sensor and a sensor to quantify the tightness of the bracelet. Our functional experiments, which compared stimulation with three and five intensity levels, respectively, were performed by four healthy participants in their twenties and thirties. The participants achieved an average accuracy of 88% estimating three vibration intensity levels. While the estimation accuracy for five intensity levels was only 67%, the results indicated a good performance in perceiving relative vibration changes with an accuracy of 82%. The proposed haptic feedback bracelet will facilitate research investigating the benefits of bidirectional human–machine interfaces and the perception of vibrotactile feedback in general by closing the gap for a versatile device that can provide high-density user feedback in combination with sensors for intent detection.

Funder

German Research Foundation

Publisher

MDPI AG

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3