Bio-robotics research for non-invasive myoelectric neural-interfaces for prosthetic control—a ten-year perspective review

Abstract A decade ago, a group of researchers from academia and industry identified a dichotomy between the industrial and academic state-of-the-art in upper-limb prosthesis control, a widely used bio-robotics application. They proposed four key technical challenges, if addressed, could bridge this gap and translate academic research into clinically and commercially viable products. These challenges are unintuitive control schemes, lack of sensory feedback, poor robustness, and single sensor modality. Here, we provide a perspective review on the research effort that occurred in the last decade, aiming at addressing these challenges. In addition, we discussed three research areas essential to the recent development in upper-limb prosthetic control research but were not envisioned in the review ten years ago: deep learning methods, sEMG decomposition and open-source databases. To conclude the review, we provided an outlook into the near future of the research and development in upper-limb prosthetic control and beyond.