A Vision-Enabled Prosthetic Hand for Children with Upper Limb Disabilities
This addresses the need for accessible, low-cost prosthetic solutions for children in low-income families, though it is incremental in improving upon existing myoelectric prostheses.
The paper tackles the problem of providing affordable and functional prosthetic hands for children with upper limb disabilities by developing a vision-enabled prosthetic hand that achieved 96% accuracy in object detection and 100% in grasp classification, with a mean absolute error of 0.018 in force prediction.
This paper introduces a novel AI vision-enabled pediatric prosthetic hand designed to assist children aged 10-12 with upper limb disabilities. The prosthesis features an anthropomorphic appearance, multi-articulating functionality, and a lightweight design that mimics a natural hand, making it both accessible and affordable for low-income families. Using 3D printing technology and integrating advanced machine vision, sensing, and embedded computing, the prosthetic hand offers a low-cost, customizable solution that addresses the limitations of current myoelectric prostheses. A micro camera is interfaced with a low-power FPGA for real-time object detection and assists with precise grasping. The onboard DL-based object detection and grasp classification models achieved accuracies of 96% and 100% respectively. In the force prediction, the mean absolute error was found to be 0.018. The features of the proposed prosthetic hand can thus be summarized as: a) a wrist-mounted micro camera for artificial sensing, enabling a wide range of hand-based tasks; b) real-time object detection and distance estimation for precise grasping; and c) ultra-low-power operation that delivers high performance within constrained power and resource limits.