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可穿戴超声接口用于假肢手控制。

A Wearable Ultrasound Interface for Prosthetic Hand Control.

出版信息

IEEE J Biomed Health Inform. 2022 Nov;26(11):5384-5393. doi: 10.1109/JBHI.2022.3203084. Epub 2022 Nov 10.

DOI:10.1109/JBHI.2022.3203084
PMID:36044504
Abstract

Ultrasound can non-invasively detect muscle deformations and has great potential applications in prosthetic hand control. Traditional ultrasound equipment was usually too bulky to be applied in wearable scenarios. This research presented a compact ultrasound device that could be integrated into a prosthetic hand socket. The miniaturized ultrasound system included four A-mode ultrasound transducers for sensing musculature deformations, a signal excitation/acquisition module, and a prosthetic hand control module. The size of the ultrasound system was 657525 mm, weighing only 85 g. For the first time, we integrated the ultrasound system into a prosthetic hand socket to evaluate its performance in practical prosthetic hand control. We designed an experiment requiring twenty subjects to perform six commonly used gestures. The performance of decoding ultrasound signals was analyzed offline using four classification algorithms and then was assessed in online control. The average values of online classification accuracy with and without wearing the physical prosthetic were 91.5 [Formula: see text] and 96.5 [Formula: see text], respectively. We found that wearing the prosthetic hand influenced the ultrasound gestures classification accuracy, but remarkable online classification performance could still be maintained. These experimental results demonstrated the efficacy of the designed integrated ultrasound system for practical use, paving the way for an effective HMI system that could be widely used in prosthetic hand control.

摘要

超声可以非侵入式地检测肌肉变形,在假肢手控制方面具有很大的潜在应用。传统的超声设备通常过于庞大,无法应用于可穿戴场景。本研究提出了一种可集成到假肢手插座中的紧凑型超声设备。该微型超声系统包括四个用于感知肌肉变形的 A 模式超声换能器、一个信号激励/采集模块和一个假肢手控制模块。超声系统的尺寸为 657525 毫米,重量仅为 85 克。我们首次将超声系统集成到假肢手插座中,以评估其在实际假肢手控制中的性能。我们设计了一个需要二十名受试者执行六种常用手势的实验。使用四种分类算法离线分析解码超声信号的性能,然后在线控制中进行评估。佩戴和不佩戴物理假肢的在线分类准确率的平均值分别为 91.5 [Formula: see text]和 96.5 [Formula: see text]。我们发现佩戴假肢对手势分类准确率有影响,但仍能保持显著的在线分类性能。这些实验结果表明,所设计的集成超声系统对于实际应用是有效的,为广泛应用于假肢手控制的有效人机界面系统铺平了道路。

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