Bao Xuefeng, Zhang Qiang, Fragnito Natalie, Wang Jian, Sharma Nitin
Department of Biomedical Engineering, University of Wisconsin-Milwaukee, Milwaukee, WI, USA.
Joint Department of Biomedical Engineering, North Carolina State University, Raleigh, NC, USA.
Wearable Technol. 2023 Mar 1;4:e6. doi: 10.1017/wtc.2022.30. eCollection 2023.
B-mode ultrasound (US) is often used to noninvasively measure skeletal muscle architecture, which contains human intent information. Extracted features from B-mode images can help improve closed-loop human-robotic interaction control when using rehabilitation/assistive devices. The traditional manual approach to inferring the muscle structural features from US images is laborious, time-consuming, and subjective among different investigators. This paper proposes a clustering-based detection method that can mimic a well-trained human expert in identifying fascicle and aponeurosis and, therefore, compute the pennation angle. The clustering-based architecture assumes that muscle fibers have tubular characteristics. It is robust for low-frequency image streams. We compared the proposed algorithm to two mature benchmark techniques: and The performance of the proposed approach showed higher accuracy in our dataset (frame frequency is 20 Hz), that is, similar to the human expert. The proposed method shows promising potential in automatic muscle fascicle orientation detection to facilitate implementations in biomechanics modeling, rehabilitation robot control design, and neuromuscular disease diagnosis with low-frequency data stream.
B 型超声（US）常用于无创测量骨骼肌结构，其中包含人体意图信息。从 B 型图像中提取的特征有助于在使用康复/辅助设备时改善闭环人机交互控制。传统的从超声图像推断肌肉结构特征的手动方法费力、耗时，且不同研究者之间存在主观性。本文提出了一种基于聚类的检测方法，该方法可以在识别肌束和腱膜方面模仿训练有素的人类专家，从而计算出羽状角。基于聚类的架构假设肌肉纤维具有管状特征。它对低频图像流具有鲁棒性。我们将所提出的算法与两种成熟的基准技术进行了比较：[此处可能遗漏了两种基准技术的具体名称]。在所提出方法在我们的数据集（帧率为 20 Hz）中表现出更高的准确性，即与人类专家相似。所提出的方法在自动肌肉肌束方向检测方面显示出有前景的潜力，以促进在生物力学建模、康复机器人控制设计以及低频数据流的神经肌肉疾病诊断中的应用。
