No abstract

Ultrasound imaging or sonomyography has been found to be a robust modality for sensing muscle activity due to its ability to directly image deep-seated muscles while providing superior spatiotemporal specificity compared to surface electromyography-based techniques. Quantifying the morphological changes during muscle activity involves computationally expensive approaches to track muscle anatomical structures or extracting features from B-mode images and A-mode signals. In this paper an offline regression convolutional neural network (CNN) called SonoMyoNet for estimating continuous isometric force from sparse ultrasound scanlines has been presented. SonoMyoNet learns features from a few equispaced scanlines selected from B-mode images and utilizes the learned features to accurately estimate continuous isometric force. 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 global features of sparse ultrasound images.

Noninvasive methods for estimation of joint and muscle forces have widespread clinical and research applications. Surface electromyography or sEMG provides a measure of the neural activation of muscles which can be used to estimate the force produced by the muscle. However, sEMG based measures of force suffer from poor signal-to-noise ratio and limited spatiotemporal specificity. In this paper, we propose an ultrasound imaging or sonomyography-based approach for estimating continuous isometric force from a sparse set of ultrasound scanlines. Our approach isolates anatomically relevant features from A-mode ultrasound signals, greatly reducing the dimensionality of the feature space and the computational complexity involved in traditional ultrasound-based methods. We evaluate the performance of four regression methodologies for force prediction using the reduced feature set. We also evaluate the feasibility of a practical wearable sonomyography-based system by simulating the effect of transducer placement and varying the number of transducers used in force prediction. Our results demonstrate that Gaussian process regression models outperform other regression methods in predicting continuous force levels from just four equispaced transducers and are tolerant to speckle noise. These findings will aid in the design of wearable sonomyography-based force prediction systems with robust, computationally efficient operation.