手势与肌电控制信号的生物医学应用

Vaishali M. Gulhane, Dr. Amol Kumbhare
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引用次数: 0

摘要

近十年来,用于人体信号检测的可穿戴设备越来越受到关注。与植入式传感器相比,可穿戴设备更侧重于身体运动检测,可以支持人机交互(HMI)和生物医学应用。在可穿戴设备中,基于肌电图(EMG)、肌力图(FMG)和电阻抗断层扫描(EIT)的身体信息监测技术得到了广泛的应用。在文献中,所有这些都被用于许多类似的应用场景,这很容易使研究人员在开始探索该领域时感到困惑。因此,在本文中,我们将详细回顾这三种技术,从基本原理,器件架构,解释算法,应用实例,优缺点,到最新工作,有待解决的挑战以及该领域的前景。我们相信本文的内容可以帮助读者对这三种技术在相关场景中的设计和应用有一个整体的印象。索引术语:FMG;肌电图;EIT;生物信号;人类系统互动等。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Biomedical Applications using Hand Gesture with Electromyography Control Signal
Wearables developed for human body signal detection receive increasing attention in the current decade. Compared to implantable sensors, wearables are more focused on body motion detection, which can support human–machine interaction (HMI) and biomedical applications. In wearables, electromyography (EMG), force myography (FMG), and electrical impedance tomography (EIT) based body information monitoring technologies are broadly presented. In the literature, all of them have been adopted for many similar application scenarios, which easily confuses researchers when they start to explore the area. Hence, in this article, we review the three technologies in detail, from basics including working principles, device architectures, interpretation algorithms, application examples, merits and drawbacks, to state-of-the-art works, challenges remaining to be solved and the outlook of the field. We believe the content in this paper could help readers create a whole image of designing and applying the three technologies in relevant scenarios. Index Terms : FMG; EMG; EIT; biological signal; human–system interactivities.
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