  Objective: 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 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-of-freedom. 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.
