Simultaneous Proportional Control of Two Degrees-of-Freedom Human Machine Interface Using Highly Sparse Sonomyography

Non-invasive human-machine interfaces (HMIs) are critical in developing prosthetic systems that offer intuitive, simultaneous, and proportional control over multiple degrees of freedom (DOFs). This study introduces a novel system for intuitive concurrent control of hand and wrist movements using sonomyography based imaging of muscle activity. Our method uses a sparse set of ultrasound scanlines to reduce computational complexity while enhancing usability. We evaluated four regression techniques for wrist and hand angle prediction, focusing on performance with a reduced sonomyographic feature set. We also explored the feasibility of a sonomyography-based system by simulating various factors that could affect prediction, including feature selection and scanline count. Our findings demonstrate that Gaussian process regression excels in predicting wrist and hand angles with just eight equispaced transducers in offline settings. Real-time evaluations with 10 non-disabled participants showed a 93 % success rate for two-DOF tasks using linear regression. The system was tested with an individual with amputation, achieving a 46 % success rate for two-DOF control in a 2D space, even though the ground truth data for model training was collected from the contralateral limb. This study validates our sonomyography-based approach for accurate wrist and hand angle estimation, reducing complexity and demonstrating potential in real-world scenarios.