Machine learning to extract muscle fascicle length changes from dynamic ultrasound images in real-time

B-mode ultrasound has become one-off, if not the main way of measuring muscle fascicle fiber lengths non-invasively. Yet, the gold standard for tracking these is still time-intensive hand-tracking, and even with semi-automated approaches, the process takes time and has to be done post hoc. Hence, towards greatly improving current processing capabilities by tracking these muscle fasicle lengths in real-time, we trained and optimized machine learning models with collected B-mode ultrasound data.

We focused on soleus muscle ultrasound data given the relationships existing between soleus and whole body energetics while walking and our intention to use these measurements in the loop. To ensure these data were representative of different muscle fiber loading and displacement levels, we collected B-mode ultrasound data from the soleus muscle of six participants performing five defined ankle motion tasks: (a) seated, constrained ankle plantarflexion, (b) seated, free ankle dorsi/plantarflexi...

B型超声已成为（即便不是主要方式，也）是无创测量肌束纤维长度的重要方法之一。然而，追踪这些参数的金标准仍是耗时的手动追踪；即便采用半自动化方法，该过程仍需耗费时间且需事后完成。因此，为通过实时追踪肌束长度大幅提升现有处理能力，我们利用采集的B型超声数据训练并优化了机器学习模型。
鉴于步行时比目鱼肌（soleus muscle）与全身能量代谢的关联，以及我们将这些测量值纳入闭环的意图，我们重点关注比目鱼肌的超声数据。为确保数据能代表不同肌纤维负荷与位移水平，我们采集了6名受试者在完成5项规定踝关节运动任务时的比目鱼肌B型超声数据，这些任务包括：（a）坐姿约束性踝关节跖屈；（b）坐姿自由踝关节背屈/跖屈……