IEEE Trans Biomed Eng. 2020 Mar;67(3):688-696. doi: 10.1109/TBME.2019.2919488. Epub 2019 May 29.
Sonomyography has been shown to be a promising method for decoding volitional motor intent from analysis of ultrasound images of the forearm musculature. The objectives of this paper are to determine the optimal location for ultrasound transducer placement on the anterior forearm for imaging maximum muscle deformations during different hand motions, and to investigate the effect of using a sparse set of ultrasound scanlines for motion classification for ultrasound-based muscle-computer interfaces (MCIs).
The optimal placement of the ultrasound transducer along the forearm was identified using freehand three-dimensional reconstructions of the muscle thickness during rest and motion completion. Based on the ultrasound images acquired from the optimally placed transducer, classification accuracy with equally spaced scanlines across the cross-sectional field of view was determined. Furthermore, the unique contribution of each scanline to class discrimination using Fisher criterion (FC) and mutual information (MI) with respect to motion discriminability was determined.
Experiments with five able-bodied subjects show that the maximum muscle deformation occurred between 40%-50% of the forearm length for multiple degrees-of-freedom. The average classification accuracy was 94% ± 6% with the entire 128-scanline image and 94% ± 5% with four equally spaced scanlines. However, no significant improvement in classification accuracy was observed with optimal scanline selection using FC and MI.
For an optimally placed transducer, a small subset of ultrasound scanlines can be used instead of a full imaging array without sacrificing performance in terms of classification accuracy for multiple degrees-of-freedom.
The selection of a small subset of transducer elements can enable the reduction of computation, and simplification of the instrumentation and power consumption of wearable sonomyographic MCIs, particularly for rehabilitation and gesture recognition applications.
超声肌图已被证明是一种很有前途的方法，可以通过分析前臂肌肉的超声图像来解码自愿运动意图。本文的目的是确定在前臂上放置超声换能器的最佳位置，以便在进行不同手部运动时对最大肌肉变形进行成像，并研究在基于超声的肌计算机接口 (MCI) 中使用稀疏的超声扫描线集进行运动分类的效果。
使用自由手重建休息和运动完成时的肌肉厚度的三维重建来确定沿前臂的超声换能器的最佳放置位置。基于从最佳放置的换能器获得的超声图像，确定在整个横截面视场中使用等距扫描线的分类准确性。此外，还使用 Fisher 准则 (FC) 和互信息 (MI) 确定每条扫描线对运动可分辨性的独特贡献。
对 5 名健康受试者的实验表明，对于多个自由度，最大肌肉变形发生在前臂长度的 40%-50%之间。使用整个 128 扫描线图像的平均分类准确率为 94%±6%，使用四个等距扫描线的平均分类准确率为 94%±5%。然而，使用 FC 和 MI 进行最佳扫描线选择并没有观察到分类准确性的显著提高。
对于最佳放置的换能器，可以使用超声扫描线的一小部分子集，而无需牺牲多个自由度的分类准确性，而无需使用完整的成像阵列。
换能器元件子集的选择可以减少计算量，并简化可穿戴超声肌 MCI 的仪器和功耗，特别是在康复和手势识别应用中。
