Comparative Analysis of Muscle Fascia Tracking Algorithms for Real-Time Muscle Monitoring using Wearable Ultrasound

Musculoskeletal ultrasound (MSK-US) enables real-time imaging of muscle structure and function, and wearable ultrasound (WUS) has extended this capability to dynamic movement tasks. Accurate tracking of muscle fascia displacement in M-mode WUS images is essential for quantifying muscle function, yet the relative performance of existing fascia-tracking algorithms remains uncharacterized. This study directly compares five fascia-tracking algorithms: Maximum Pixel Intensity (MPI), Muscle Boundary Tracking Algorithm (MBTA), Principal Component Analysis (PCA), Composite-Factorization PCA (CF-PCA), and U-Net segmentation, against expert-annotated ground truth to identify which approach best supports wearable muscle-monitoring applications. A total of 572 M-mode ultrasound images were collected during isometric quadricep activations (QA) and squats (SQ) using a multi-site WUS system with transducers positioned on the vastus lateralis (VL), rectus femoris (RF), and vastus medialis oblique (VMO). Fascia tracking using U-Net segmentation exhibited the lowest mean absolute error (median QA=0.57, median SQ=1.22; p<0.05), functional range not statistically different from expert traces (QA p=0.33; SQ p=1) and the most accurate estimates of functional error (median QA=-0.21; median SQ=-0.65; p<0.05). PCA-based methods demonstrated the highest correlation with the expert traces (PCA median QA=0.88; CF-PCA median QA=0.88; PCA median SQ=0.78; CF-PCA median SQ=0.75; p<0.005), reflecting superior tracking of relative contraction patterns. These results indicate U-Net segmentation is best suited for applications requiring precise fascia-depth estimation when labeled training data are available, while PCA-based methods are preferable for tracking relative contraction patterns without supervised training, informing algorithm selection for wearable neuromuscular monitoring in clinical and performance settings.