Preliminary Design of an Environment Recognition System for Controlling Robotic Lower-Limb Prostheses and Exoskeletons

Brock Laschowski, William J. McNally, A. Wong, J. McPhee
{"title":"Preliminary Design of an Environment Recognition System for Controlling Robotic Lower-Limb Prostheses and Exoskeletons","authors":"Brock Laschowski, William J. McNally, A. Wong, J. McPhee","doi":"10.1109/ICORR.2019.8779540","DOIUrl":null,"url":null,"abstract":"Drawing inspiration from autonomous vehicles, using future environment information could improve the control of wearable biomechatronic devices for assisting human locomotion. To the authors knowledge, this research represents the first documented investigation using machine vision and deep convolutional neural networks for environment recognition to support the predictive control of robotic lower-limb prostheses and exoskeletons. One participant was instrumented with a battery-powered, chest-mounted RGB camera system. Approximately 10 hours of video footage were experimentally collected while ambulating throughout unknown outdoor and indoor environments. The sampled images were preprocessed and individually labelled. A deep convolutional neural network was developed and trained to automatically recognize three walking environments: level-ground, incline staircases, and decline staircases. The environment recognition system achieved 94.85% overall image classification accuracy. Extending these preliminary findings, future research should incorporate other environment classes (e.g., incline ramps) and integrate the environment recognition system with electromechanical sensors and/or surface electromyography for automated locomotion mode recognition. The challenges associated with implementing deep learning on wearable biomechatronic devices are discussed.","PeriodicalId":130415,"journal":{"name":"2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"40","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICORR.2019.8779540","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 40

Abstract

Drawing inspiration from autonomous vehicles, using future environment information could improve the control of wearable biomechatronic devices for assisting human locomotion. To the authors knowledge, this research represents the first documented investigation using machine vision and deep convolutional neural networks for environment recognition to support the predictive control of robotic lower-limb prostheses and exoskeletons. One participant was instrumented with a battery-powered, chest-mounted RGB camera system. Approximately 10 hours of video footage were experimentally collected while ambulating throughout unknown outdoor and indoor environments. The sampled images were preprocessed and individually labelled. A deep convolutional neural network was developed and trained to automatically recognize three walking environments: level-ground, incline staircases, and decline staircases. The environment recognition system achieved 94.85% overall image classification accuracy. Extending these preliminary findings, future research should incorporate other environment classes (e.g., incline ramps) and integrate the environment recognition system with electromechanical sensors and/or surface electromyography for automated locomotion mode recognition. The challenges associated with implementing deep learning on wearable biomechatronic devices are discussed.
机器人下肢假体与外骨骼控制环境识别系统的初步设计
从自动驾驶汽车中获得灵感,利用未来的环境信息可以改善可穿戴生物机电设备的控制,以帮助人类运动。据作者所知,这项研究代表了首次使用机器视觉和深度卷积神经网络进行环境识别的文献调查,以支持机器人下肢假体和外骨骼的预测控制。其中一名参与者配备了一个电池供电的、安装在胸前的RGB相机系统。在未知的室外和室内环境中走动时,实验收集了大约10小时的视频片段。对采样图像进行预处理并单独标记。开发并训练了一个深度卷积神经网络来自动识别三种步行环境:平地、倾斜楼梯和下降楼梯。环境识别系统整体图像分类准确率达到94.85%。扩展这些初步发现,未来的研究应该纳入其他环境类别(例如,倾斜斜坡),并将环境识别系统与机电传感器和/或表面肌电图集成在一起,以实现自动运动模式识别。讨论了在可穿戴生物机电设备上实施深度学习所面临的挑战。
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
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