Toward Intuitive Prosthetic Control Solving Common Issues Using Force Myography, Surface Electromyography, and Pattern Recognition in a Pilot Case Study
Toward Intuitive Prosthetic Control Solving Common Issues Using Force Myography, Surface Electromyography, and Pattern Recognition in a Pilot Case Study
复制标题
DOI：
10.1109/mra.2017.2747899
复制
发表时间：
2017-12-01
影响因子：
5.7
通讯作者：
Menon, Carlo
中科院分区：
文献类型：
--
作者：
Ahmadizadeh, Chakaveh;Merhi, Lukas-Karim;Menon, Carlo
尽管在过去十年中出现了先进的多自由度（DoF）机器人手，但假肢控制缺乏强大的界面，无法以大多数用户可接受的方式促进其所有功能[1]。在这篇文章中，我们探讨了使用传感技术称为力肌描记术（FMG）作为替代或替代传统的表面肌电图（sEMG）技术作为人机界面（HMI）的多自由度假手，bebionic 3由奥托博克，奥斯汀，得克萨斯州的控制的可行性。在这篇文章中，我们提出了一个假肢原型开发的Cybathlon 2016年，锦标赛的赛车飞行员残疾人使用辅助机器人设备。讨论了样机的设计，并分析了两个因素对其控制的影响。这些因素是1）多感觉方法的影响和2）FMG传感器条在假肢内接受腔中的放置。通过比较所得到的模式识别精度来进行分析。结果表明，使用这两种传感方式（FMG和EMG）一起产生了最高的模式识别准确率（81.1%）的10类运动（四个手腕运动和六个抓地力模式）。我们证明，FMG有潜力成为一个人机界面控制上肢动力假肢。FMG还说明了通过使用模式识别进行直观控制的潜力。多传感器方法可以帮助增加用于假肢控制的HMI的鲁棒性。
Despite the appearance of advanced multi-degrees of freedom (DoF) robotic hands during the past decade, prosthetic control lacks a powerful interface to facilitate all its functionalities in a manner that is acceptable for a majority of users [1]. In this article, we explore the feasibility of using a sensing technique called force myography (FMG) as an alternative or synergist to the traditional surface electromyography (sEMG) technique as a human-machine interface (HMI) for the control of a multi-DoF prosthetic hand, bebionic 3 by Ottobock, Austin, Texas. In this article, we present a prosthetic prototype developed for the Cybathlon 2016, a championship for racing pilots with disabilities using assistive robotic devices. The design of the prototype is discussed and the effect of two factors on its control is analyzed. These factors are 1) the impact of a multisensory approach and 2) the placement of FMG sensor strips within the prosthetic inner socket. Analysis is performed by comparing resulting pattern recognition accuracies. Results show that the use of both sensing modalities (FMG and EMG) together produced the highest pattern recognition accuracy (81.1%) for ten classes of motion (four wrist movements and six grip patterns). We demonstrated that FMG has the potential to be an HMI for control of upper-limb-powered prostheses. FMG also illustrates the potential for intuitive control through the use of pattern recognition. A multisensory approach could assist in increasing robustness of the HMI for prosthetic control.