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/plantarflexion, (c) weight-bearing, calf
raises (d) walking, and then a (e) mix. We collected 24-seconds
of 60 frames-pre-second data for each
task using Telemed, ArtUs EXT-1H,
LV8-5N60-A2 ultrasound probe wrapped to the right calf
with 3M Vetrap Bandaging Tape. The probe was aligned so that both
aponeuroses were as close to horizontal as possible in the live ultrasound
video feed.