Development of a Virtual Environment-Based Electrooculogram Control System for Safe Electric Wheelchair Mobility for Individuals with Severe Physical Disabilities

Jane Phoebe Achieng Ogenga, Paul Waweru Njeri, Joseph K. Muguro
{"title":"Development of a Virtual Environment-Based Electrooculogram Control System for Safe Electric Wheelchair Mobility for Individuals with Severe Physical Disabilities","authors":"Jane Phoebe Achieng Ogenga, Paul Waweru Njeri, Joseph K. Muguro","doi":"10.18196/jrc.v4i2.17165","DOIUrl":null,"url":null,"abstract":"Conventional wheelchairs are predominantly manual or joystick-operated electric wheelchairs. However, operating these wheelchairs can be difficult or impossible for individuals with severe physical disabilities. Due to losing control of their physical limbs, they depend on an attendant for assistance. As a remedy, bio-signals may be used as a control mechanism since they are readily available and can be acquired from any body part. This research proposes to use EOG signals to vail a control mechanism and test it in a virtual and actual electric wheelchair. The main contribution of the study is an investigation of the use of EOG to control an electric wheelchair in a virtual environment to determine safe control parameters for wheelchair use in complex environments. A customized data acquisition circuit was developed to acquire single-channel EOG signals using wet electrodes. The acquired signal was filtered and processed using feature extraction and classification techniques in MATLAB software. Two customized control environments were developed in Unity 3D, one with equally partitioned sections and the other with sections decreasing in size as the robot wheelchair approaches the target. Twenty-two test subjects (mean age 24.5, std 1.5) participated in the study, controlling the robot wheelchair in real-time with non or least instances of collision and oversteering. The system achieved an accuracy of 96.5% with a response time of 0.7s, translating to an ITR of 70.6 bits/min. Overall, the participants managed to navigate the virtual environment with a completion time of 101.94s ± 19.71 and 109.07s ± 13.25 for the male and female participants, respectively. In the scene with decreasing section sizes, 72% and 54% instances of collision and oversteering were reported, respectively, highlighting the need to consider the complexity of the control environment and the sufficiency of the participants' control skills to ensure safety in operations. The results confirm the usefulness of EOG as a control interface, with little or no need for recalibration. It provides a promising avenue for individuals with severe physical disabilities to operate wheelchairs independently in complex environments, enhancing their quality of life.","PeriodicalId":443428,"journal":{"name":"Journal of Robotics and Control (JRC)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Robotics and Control (JRC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18196/jrc.v4i2.17165","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

Conventional wheelchairs are predominantly manual or joystick-operated electric wheelchairs. However, operating these wheelchairs can be difficult or impossible for individuals with severe physical disabilities. Due to losing control of their physical limbs, they depend on an attendant for assistance. As a remedy, bio-signals may be used as a control mechanism since they are readily available and can be acquired from any body part. This research proposes to use EOG signals to vail a control mechanism and test it in a virtual and actual electric wheelchair. The main contribution of the study is an investigation of the use of EOG to control an electric wheelchair in a virtual environment to determine safe control parameters for wheelchair use in complex environments. A customized data acquisition circuit was developed to acquire single-channel EOG signals using wet electrodes. The acquired signal was filtered and processed using feature extraction and classification techniques in MATLAB software. Two customized control environments were developed in Unity 3D, one with equally partitioned sections and the other with sections decreasing in size as the robot wheelchair approaches the target. Twenty-two test subjects (mean age 24.5, std 1.5) participated in the study, controlling the robot wheelchair in real-time with non or least instances of collision and oversteering. The system achieved an accuracy of 96.5% with a response time of 0.7s, translating to an ITR of 70.6 bits/min. Overall, the participants managed to navigate the virtual environment with a completion time of 101.94s ± 19.71 and 109.07s ± 13.25 for the male and female participants, respectively. In the scene with decreasing section sizes, 72% and 54% instances of collision and oversteering were reported, respectively, highlighting the need to consider the complexity of the control environment and the sufficiency of the participants' control skills to ensure safety in operations. The results confirm the usefulness of EOG as a control interface, with little or no need for recalibration. It provides a promising avenue for individuals with severe physical disabilities to operate wheelchairs independently in complex environments, enhancing their quality of life.
基于虚拟环境的重度残疾人电动轮椅安全移动电眼图控制系统的开发
传统轮椅主要是手动或操纵杆操作的电动轮椅。然而,对于有严重身体残疾的人来说,操作这些轮椅可能很困难或不可能。由于失去对肢体的控制,他们依靠随从的帮助。作为一种补救措施,生物信号可以作为一种控制机制,因为它们很容易获得,可以从身体的任何部位获得。本研究提出利用眼电信号来设计控制机制,并在虚拟和实际电动轮椅上进行测试。本研究的主要贡献是研究了在虚拟环境中使用EOG控制电动轮椅,以确定轮椅在复杂环境中使用的安全控制参数。开发了一种定制的数据采集电路,用于使用湿电极获取单通道EOG信号。采集到的信号在MATLAB软件中使用特征提取和分类技术进行滤波和处理。在Unity 3D中开发了两个定制的控制环境,一个是等分割的区域,另一个是随着机器人轮椅接近目标而缩小的区域。22名测试对象(平均年龄24.5岁,标准年龄1.5岁)参与了这项研究,他们实时控制机器人轮椅,没有或很少发生碰撞和过度转向。该系统实现了96.5%的精度,响应时间为0.7s,转换为70.6 bits/min的ITR。总体而言,男性和女性在虚拟环境中导航的完成时间分别为101.94秒±19.71和109.07秒±13.25。在路段尺寸减小的场景中,碰撞和过度转向的案例分别为72%和54%,这突出了考虑控制环境复杂性和参与者控制技能充分性以确保操作安全的必要性。结果证实了EOG作为控制接口的有效性,几乎不需要重新校准。它为严重身体残疾的人在复杂的环境中独立操作轮椅提供了一条有希望的途径,提高了他们的生活质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
6.30
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信