SonoMyoNet: A Convolutional Neural Network for Predicting Isometric Force From Highly Sparse Ultrasound Images

Ultrasound imaging or sonomyography has been found to be a robust modality for measuring muscle activity due to its ability to image deep-seated muscles directly while providing superior spatiotemporal specificity compared with surface electromyography-based techniques. Quantifying the morphological...

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Bibliographic Details
Published in:IEEE transactions on human-machine systems 2024-06, Vol.54 (3), p.317-324
Main Authors: Kamatham, Anne Tryphosa, Alzamani, Meena, Dockum, Allison, Sikdar, Siddhartha, Mukherjee, Biswarup
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Convolutional neural networks
Convolutional neural networks (CNNs)
Dynamometers
Feature extraction
Force
force estimation
Muscle function
Muscles
Neural networks
Noise
Sonogram
sonomyography (SMG)
Tracking
Transducers
Ultrasonic imaging
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Summary:Ultrasound imaging or sonomyography has been found to be a robust modality for measuring muscle activity due to its ability to image deep-seated muscles directly while providing superior spatiotemporal specificity compared with surface electromyography-based techniques. Quantifying the morphological changes during muscle activity involves computationally expensive approaches for tracking muscle anatomical structures or extracting features from brightness-mode (B-mode) images and amplitude-mode signals. This article uses an offline regression convolutional neural network called SonoMyoNet to estimate continuous isometric force from sparse ultrasound scanlines. SonoMyoNet learns features from a few equispaced scanlines selected from B-mode images and utilizes the learned features to estimate continuous isometric force accurately. The performance of SonoMyoNet was evaluated by varying the number of scanlines to simulate the placement of multiple single-element ultrasound transducers in a wearable system. Results showed that SonoMyoNet could accurately predict isometric force with just four scanlines and is immune to speckle noise and shifts in the scanline location. Thus, the proposed network reduces the computational load involved in feature tracking algorithms and estimates muscle force from the global features of sparse ultrasound images.
ISSN:2168-2291
2168-2305
DOI:10.1109/THMS.2024.3389690