声肌图肌-机接口的稀疏性分析
Sparsity Analysis of a Sonomyographic Muscle-Computer Interface
摘要：
目的：本文的目的是确定最佳位置的超声换能器放置在前臂前的成像最大肌肉变形在不同的手部运动和研究的效果，使用稀疏的一组超声扫描线的运动分类超声为基础的肌肉计算机接口（MCI）。研究方法：沿前臂的超声换能器的最佳位置是确定使用徒手3D重建的肌肉厚度在休息和运动完成。从最佳放置的换能器采集的超声图像中，我们确定了横截面视场（FOV）上等距扫描线的分类准确性。此外，我们研究了独特的贡献，每一个扫描线类歧视使用Fisher准则（FC）和互信息（MI）的运动可辨别性。结果如下：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30-50%之间。平均分类准确率为94.6%与整个128扫描线图像和94.5%与4等间距扫描线。然而，使用FC和MI的最佳扫描线选择没有观察到分类准确性的显著改善。结论：对于最佳放置的换能器，可以使用超声扫描线的小子集来代替完整的成像阵列，而不会牺牲多个自由度的分类精度方面的性能。重要性：换能器元件的小子集的选择可以使得能够减少计算，并且简化可穿戴声肌描记MCI的仪器和功耗，特别是用于康复和手势识别应用。
英文摘要：
Objective: 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). Methods: The optimal placement of the ultrasound transducer along the forearm is identified using freehand 3D reconstructions of the muscle thickness during rest and motion completion. From the ultrasound images acquired from the optimally placed transducer, we determine classification accuracy with equally spaced scanlines across the cross-sectional field-of-view (FOV). Furthermore, we investigated the unique contribution of each scanline to class discrimination using Fisher criteria (FC) and mutual information (MI) with respect to motion discriminability. Results: Experiments with 5 able-bodied subjects show that the maximum muscle deformation occurred between 30-50% 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 4 equally-spaced scanlines. However, no significant improvement in classification accuracy was observed with optimal scanline selection using FC and MI. Conclusion: 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. Significance: 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.
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