Optimization of the Placement of the Ultrasound Scanlines on the Forearm for an Upper Limb Prosthesis
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Abstract
Sonomyography is an emerging technique that uses ultrasound to detect muscle deformation and is being explored as a real-time alternative to surface electromyography for deriving control signals from functional activity. Many groups have demonstrated the feasibility of using commercial ultrasound systems to control upper limb prostheses; however, these systems are bulky and not optimized for wearable use. In this study, a novel 4-channel ultrasound system with miniaturized electronics optimized for forearm applications has been used. While previous work has demonstrated that data from 4 channels may be sufficient to classify multiple grasps, the performance may be dependent on the anatomical placement of the individual transducers on the forearm. In this study, we evaluated the effects of transducer placement on classification performance and explored different measurements to determine optimal anatomical region for placement. These metrics consisted of Mutual Information (MI), Structural Similarity Index (SSIM), and Sum of Squared Distance (SSD); which quantify the amount of information is derived from every ultrasound transducer. Ultrasound M-mode images of different hand/wrist gestures were collected from 4 subjects with ultrasound transducers placed at three different positions on the forearm. The first position was at the flexor muscles, the second observing the extensor muscles, and the third was a custom placement of the transducers targeting specific muscle compartments and regions on the forearm. MI/SSIM/SSD were used to calculate how much information each ultrasound transducer contained, and the values were correlated to the performance of a LDA classifier's ability to differentiate between the gestures. The results show that the LDA was able to discriminate between the different hand gestures with an average accuracy of 76.4 ± 4.04% for the extensor muscle position,
97.5 ± 1.72% and 99.4 ± 0.61% for the flexor muscles and custom targeted position, respectively. No correlation was found between MI and the classification performance. Strong statistically significant correlation was found between SSD and SSIM values and classification performance (p-value < 0.001) . This study demonstrates the feasibility of using 4-channel single element M-mode ultrasound transducers to recognize complex hand gestures and emphasizes the importance of targeting specific muscle compartments and regions on the forearm to obtain high classification accuracy.