Sonomyography-Based Decoding of Attempted Hand Movements in Individuals with Spinal Cord Injury.
Authors
Abstract
Restoring hand function is critical for individuals with spinal cord injury (SCI). This study develops and evaluates a sonomyography-based human-machine interface (HMI) for decoding attempted hand movements across individuals with different levels and severities of cervical SCI, using a sparse-ultrasound processing pipeline designed for 8 channel A-mode ultrasound systems. A custom convolutional neural network, SonoSCINet, was developed using sparse B-mode ultrasound data and evaluated in non-disabled individuals and individuals with SCI, and compared with traditional machine learning classifiers. Feature selection and two-dimensional UMAP were used to examine handcrafted and learned representations. The same network architecture and processing pipeline were retrained and evaluated on an 8-channel A-mode ultrasound system in individuals with cervical SCI spanning different neurological injury levels and functional impairments. SonoSCINet outperformed traditional models for gesture classification using sparse B-mode data (non-disabled: $91.3 \pm 12.79\%$; SCI: $82.03 \pm 23.48\%$). With the 8-channel A-mode system, SonoSCINet achieved a mean accuracy of $94.17 \pm 4.75\%$, numerically higher than LDA-based methods ($86.69 \pm 6.27\%$; not statistically significant), with improved robustness and gesture separability in the learned latent space. A real-time feasibility study in individuals with SCI further supports online implementation of the proposed pipeline for HMI applications. SonoSCINet, trained with a subject-specific pipeline, enables accurate decoding of hand gestures across sparse B-mode and A-mode ultrasound systems and across a range of SCI impairment levels, supporting the feasibility of a unified processing framework across modalities. These results support sonomyography combined with deep learning as a promising direction for portable A-mode ultrasound HMIs in individuals with SCI, with real-time feasibility of the underlying acquisition pipeline demonstrated using a lightweight classifier.