Sparsity Analysis of a Sonomyographic Muscle–Computer Interface

Objective: Sonomyography has been shown to be a promising method for decoding volitional motor intent from analysis of ultrasound images of the forearm musculature. 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 ultrasound-based muscle-computer interfaces (MCIs). Methods: The optimal placement of the ultrasound transducer along the forearm was identified using freehand three-dimensional reconstructions of the muscle thickness during rest and motion completion. Based on the ultrasound images acquired from the optimally placed transducer, classification accuracy with equally spaced scanlines across the cross-sectional field of view was determined. Furthermore, the unique contribution of each scanline to class discrimination using Fisher criterion (FC) and mutual information (MI) with respect to motion discriminability was determined. Results: Experiments with five able-bodied subjects show that the maximum muscle deformation occurred between 40%-50% of the forearm length for multiple degrees-of-freedom. The average classification accuracy was 94% ± 6% with the entire 128-scanline image and 94% ± 5% with four 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.