Muscle Architecture Parameters Inferred from Simulated Single-Element Ultrasound Traces

Wearable robots have shown great promise in aiding individuals with reduced mobility and enhancing human performance across various applications. To achieve optimal assistance, accurate estimation of muscle dynamics has shown promise in designing adaptive control strategies. Among different techniques, B-mode ultrasonography produced with linear array transducers have gained popularity as a gold-standard imaging tool, providing non-invasive solutions to measure in-vivo muscle dynamics. B-mode ultrasound has been employed to infer muscle thickness, fascicle lengths, pennation angles, and muscle force using neural networks, offering a valuable tool for designing individualized control strategies. The effectiveness of this measuring tool depends on integrating transducers into the wearable robot, but B-mode relies on large transducers. Studies have explored smaller single-element transducers for better wearability for muscle thickness estimation. However, their ability to infer more complex muscle architecture parameters using automated techniques is yet to be determined. In this study, we propose an approach to extract M-mode traces from B-mode images to simulate signals from single-element transducers. We then employ various machine learning architectures to infer muscle pennation angle and fascicle length. Preliminary results indicate promising performance from the CNN+Transformer (2-layer spatial) + Transformer (2-layer temporal) models, with results from the CNN+LSTM models (with a RMSE of 0.02 radian for pennation angle and 2.54mm for fascicle lengths). This study paves the way for enabling the use of smaller and more portable single-element transducers for wearable robotic applications. The link to the code is https://github.com/raku-slyu/AB-Mode-Utrasound.