Sparsity Analysis of a Sonomyographic Muscle–Computer Interface

Abstract

The objectives of this paper are to determine the optimal location for ultrasound transducer placement on the anterior forearm for imaging maximum muscle deformations during different hand motions and to investigate the effect of using a sparse set of ultrasound scanlines for motion classification for ultrasoundbased muscle computer interfaces (MCIs). Methods: The optimal placement of the ultrasound transducer along the forearm is identified using freehand 3D reconstructions of the muscle thickness during rest and motion completion. From the ultrasound images acquired from the optimally placed transducer, we determine classification accuracy with equally spaced scanlines across the cross-sectional field-of-view (FOV). Furthermore, we investigated the unique contribution of each scanline to class discrimination using Fisher criteria (FC) and mutual information (MI) with respect to motion discriminability. Results: Experiments with 5 able-bodied subjects show that the maximum muscle deformation occurred between 30-50% of the forearm length for multiple degrees-offreedom. The average classification accuracy was 94±6% with the entire 128 scanline image and 94±5% with 4 equally-spaced scanlines. However, no significant improvement in classification accuracy was observed with optimal scanline selection using FC and MI. Conclusion: For an optimally placed transducer, a small subset of ultrasound scanlines can be used instead of a full imaging array without sacrificing performance in terms of classification accuracy for multiple degrees-of-freedom. Significance: The selection of a small subset of transducer elements can enable the reduction of computation, and simplification of the instrumentation and power consumption of wearable sonomyographic MCIs particularly for rehabilitation and gesture recognition applications.

Sparse Sonomyography-based Estimation of Isometric Force: A Comparison of Methods and Features

“…al. [20] and the results have been corroborated by several studies utilizing wearable ultrasound systems [27], [28]. We intend to confirm our findings with a custom-developed wearable ultrasound imaging system in the future.…”

“…for sonomyographic gesture recognition applications. [20]. Hence, we believe that a simple equispaced transducer array can be used in most sonomyography-based muscle activity estimation applications.…”

Motion prediction using electromyography and sonomyography for an individual with transhumeral limb loss

“…In the future, we plan to use single-element transducers with low-power electronics that could be integrated into a standalone prosthesis socket. Although our prior work suggests that classification accuracy is not reduced with a sparse sensing strategy [41], this has not yet been confirmed in individuals with limb loss. Finally, the sampling rate for EMG was considerably higher than for SMG, so more frames of EMG data were available to train the classifier.…”

Classification Performance and Feature Space Characteristics in Individuals with Upper Limb Loss Using Sonomyography

“…For translation of SMG technology to practical prosthesis sockets, we anticipate utilizing singleelement transducers with low power electronics. Our previous work has indicated that the classification accuracy with sparse sensing is not compromised [56]. However, this result has yet to be validated in individuals with limb loss.…”