Sikdar Siddhartha, Rangwala Huzefa, Eastlake Emily B, Hunt Ira A, Nelson Andrew J, Devanathan Jayanth, Shin Andrew, Pancrazio Joseph J
IEEE Trans Neural Syst Rehabil Eng. 2014 Jan;22(1):69-76. doi: 10.1109/TNSRE.2013.2274657. Epub 2013 Aug 28.
Recently there have been major advances in the electro-mechanical design of upper extremity prosthetics. However, the development of control strategies for such prosthetics has lagged significantly behind. Conventional noninvasive myoelectric control strategies rely on the amplitude of electromyography (EMG) signals from flexor and extensor muscles in the forearm. Surface EMG has limited specificity for deep contiguous muscles because of cross talk and cannot reliably differentiate between individual digit and joint motions. We present a novel ultrasound imaging based control strategy for upper arm prosthetics that can overcome many of the limitations of myoelectric control. Real time ultrasound images of the forearm muscles were obtained using a wearable mechanically scanned single element ultrasound system, and analyzed to create maps of muscle activity based on changes in the ultrasound echogenicity of the muscle during contraction. Individual digit movements were associated with unique maps of activity. These maps were correlated with previously acquired training data to classify individual digit movements. Preliminary results using ten healthy volunteers demonstrated this approach could provide robust classification of individual finger movements with 98% accuracy (precision 96%-100% and recall 97%-100% for individual finger flexions). The change in ultrasound echogenicity was found to be proportional to the digit flexion speed (R(2)=0.9), and thus our proposed strategy provided a proportional signal that can be used for fine control. We anticipate that ultrasound imaging based control strategies could be a significant improvement over conventional myoelectric control of prosthetics.
近年来，上肢假肢的机电设计取得了重大进展。然而，此类假肢控制策略的发展却明显滞后。传统的非侵入性肌电控制策略依赖于来自前臂屈肌和伸肌的肌电图（EMG）信号的幅度。由于串扰，表面肌电对深层相邻肌肉的特异性有限，并且无法可靠地区分单个手指和关节的运动。我们提出了一种基于超声成像的上臂假肢控制策略，该策略可以克服肌电控制的许多局限性。使用可穿戴的机械扫描单元素超声系统获取前臂肌肉的实时超声图像，并进行分析以根据肌肉收缩期间超声回声性的变化创建肌肉活动图。单个手指的运动与独特的活动图相关联。这些图与先前获取的训练数据相关联，以对单个手指的运动进行分类。使用十名健康志愿者的初步结果表明，这种方法可以以98%的准确率对单个手指运动进行可靠分类（单个手指弯曲的精度为96%-100%，召回率为97%-100%）。发现超声回声性的变化与手指弯曲速度成正比（R(2)=0.9），因此我们提出的策略提供了一个可用于精细控制的比例信号。我们预计，基于超声成像的控制策略可能会比传统的假肢肌电控制有显著改进。
