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IET Cyber-Systems and Robotics
IET Cyber-Systems and Robotics
    
上臂假肢控制中基于EMG和NIR的姿态识别
Ejay Nsugbe1, Carol Phillips2, Mike Fraser1, Jess McIntosh1
1University of Bristol, Queen's Building, University Walk, Bristol BS8 1TR, UK 2Department of Radiology, University Hospitals Bristol, NHS Foundation Trust, Bristol, UK
Gesture recognition for transhumeral prosthesis control using EMG and NIR
Ejay Nsugbe1, Carol Phillips2, Mike Fraser1, Jess McIntosh1
1University of Bristol, Queen's Building, University Walk, Bristol BS8 1TR, UK 2Department of Radiology, University Hospitals Bristol, NHS Foundation Trust, Bristol, UK
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摘要： 与肌电假肢相关的一个关键挑战是从截肢者的残余解剖结构中获取高质量的手势意图信号。在这项研究中，作者旨在融合穿戴式肌电图（EMG）和近红外（NIR）信号对12名健全人的8个手势动作进行分类，并通过观察该方法的分类精度来克服这一挑战。作为研究的一部分，研究人员研究了不同感知配置情况下多种分类方法的分类精度，这些感知配置包括仅肌电图信号、仅近红外信号和肌电图-近红外融合信号，分类方法包括多层感知器神经网络，线性判别分析和二次判别分析。作为研究的一部分，他们进行了单独的离线超声波扫描，作为真实数据与可穿戴传感器采集结果进行对比，同时这些数据能够使研究人员在手势运动期间更仔细地研究沿肱骨的解剖结构。这项工作的结果和发现表明，在不需要使用侵入性神经肌肉传感器或更复杂硬件的条件下，使用价格合理、符合人体工程学、可穿戴的肌电图和近红外传感技术，进一步发展上臂假肢是可行的。
Abstract: A key challenge associated with myoelectric prosthesis limbs is the acquisition of a good quality gesture intent signal from the residual anatomy of an amputee. In this study, the authors aim to overcome this limitation by observing the classification accuracy of the fusion of wearable electromyography (EMG) and near-infrared (NIR) to classify eight hand gesture motions across 12 able-bodied participants. As part of the study, they investigate the classification accuracy across a multi-layer perceptron neural network, linear discriminant analysis and quadratic discriminant analysis for different sensing configurations, i.e. EMG-only, NIR-only and EMG-NIR. A separate offline ultrasound scan was conducted as part of the study and served as a ground truth and contrastive basis for the results picked up from the wearable sensors, and allowed for a closer study of the anatomy along the humerus during gesture motion. Results and findings from the work suggest that it could be possible to further develop transhumeral prosthesis using affordable, ergonomic and wearable EMG and NIR sensing, without the need for invasive neuromuscular sensors or further hardware complexity.
收稿日期: 2020-03-27
通讯作者: Ejay Nsugbe     E-mail: ennsugbe@yahoo.com
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引用本文:

Ejay Nsugbe, Carol Phillips​, Mike Fraser, Jess McIntosh. Gesture recognition for transhumeral prosthesis control using EMG and NIR. IET Cyber-Systems and Robotics, 10.1049/iet-csr.2020.0008.

链接本文:

http://www.zjujournals.com/iet-csr/CN/10.1049/iet-csr.2020.0008    或    http://www.zjujournals.com/iet-csr/CN/Y2020/V2/I3/1

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