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A Vision-Enabled Prosthetic Hand for Children with Upper Limb Disabilities

Md Abdul Baset Sarker, Art Nguyen, Sigmond Kukla, Kevin Fite, Masudul H. Imtiaz

TL;DR

A novel AI vision-enabled pediatric prosthetic hand designed to assist children aged 10-12 with upper limb disabilities 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.

Abstract

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.

A Vision-Enabled Prosthetic Hand for Children with Upper Limb Disabilities

TL;DR

A novel AI vision-enabled pediatric prosthetic hand designed to assist children aged 10-12 with upper limb disabilities 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.

Abstract

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.

Paper Structure

This paper contains 25 sections, 8 figures, 1 table.

Table of Contents

  1. Introduction
  2. The Big Picture
  3. Methodology
  4. Mechanical Design
  5. Electronic Design
  6. FPGA
  7. System Firmware
  8. Fingertip sensors
  9. Finger Control System
  10. Object detection model
  11. Data collection and training object detection model
  12. Force and Grasp Classification
  13. Force and Grasp Classification Training
  14. Gesture Classification
  15. Gesture data collection and training
  16. ...and 10 more sections

Figures (8)

  • Figure 1: Example of 3D printed vision-enabled prosthetic hand demonstrating grasping cylindrical object BasetSarker2022.
  • Figure 2: Block Diagram of Sensors and electronic connections with FPGA
  • Figure 3: Measurements are shown in the mechanical design of the hand.
  • Figure 4: Sensor placement on the finger.
  • Figure 5: (Top) Object detection model wang2023yolov7 and (bottom) Force and Grasp Classification model.
  • ...and 3 more figures