AI视觉助手机械手让10-12岁患儿低成本实现精准抓握
A Vision-Enabled Prosthetic Hand for Children with Upper Limb Disabilities
- 用微型摄像头+FPGA实现实时物体识别与距离估算
- 抓取分类准确率100%，力值预测误差仅0.018
- 轻量化设计适配儿童使用，低功耗支持长期运行
本文提出一种专为10至12岁上肢残疾儿童设计的新型AI视觉辅助机械手。该假肢具有类人外观、多关节运动功能和轻量化结构，通过3D打印技术结合先进机器视觉、传感与嵌入式计算，提供低成本且可定制的解决方案，克服现有肌电假肢的局限性。系统集成腕部微型摄像头，与低功耗FPGA协同工作，实现物体实时检测。基于深度学习的物体检测与抓取分类模型分别达到96%和100%的准确率，力值预测的平均绝对误差仅为0.018。主要特性包括：a) 腕部微型摄像头实现人工感知，支持多样手部任务；b) 实时物体检测与距离估计，提升抓握精度；c) 超低功耗运行，在资源受限条件下保持高性能。
原文摘要 · Abstract (English)
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.
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