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Related Experiment Video

Updated: Nov 16, 2025

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
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A contactless method to measure real-time finger motion using depth-based pose estimation.

Yean Zhu1, Wei Lu2, Weihua Gan3

  • 1Bioengineering College of Chongqing University, Chongqing, China; School of Transportation and Logistics, East China Jiaotong University, Nanchang, China.

Computers in Biology and Medicine
|February 25, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a contactless finger motion measurement system using Azure Kinect depth cameras. The system accurately assesses fine motor skills, offering a low-cost solution for hand rehabilitation and clinical use.

Keywords:
Computer visionDepth imageFine motor skillsPose estimation

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Area of Science:

  • Biomedical Engineering
  • Computer Vision
  • Rehabilitation Technology

Background:

  • Finger mobility is vital for daily activities and a key metric in hand rehabilitation.
  • Depth-based hand pose estimation offers a cost-effective method for assessing finger motion limitations in clinical and home settings.

Purpose of the Study:

  • To develop a contactless finger motion measurement system using depth-based hand pose estimation with Azure Kinect cameras and transfer learning.
  • To evaluate the system's accuracy against a gold-standard three-dimensional motion analysis (3DMA) system.

Main Methods:

  • Utilized Azure Kinect depth cameras and transfer learning with ensemble convolutional neural networks for real-time hand pose estimation.
  • Developed algorithms to calculate finger joint motion angles by tracking 24 hand joints.
  • Experimentally validated the system by comparing its measurements against a 3DMA system.

Main Results:

  • The Azure Kinect system demonstrated high correlation with 3DMA for hand joint coordinates (mean error distance: 0.23–1.05 cm).
  • Achieved excellent inter-trial reliability (ICC: 0.89–0.97) and concurrent validity (Pearson's r: 0.88–0.97) for joint angles.
  • The system operates in real-time at over 45 frames per second and is validated for measuring index finger and thumb joint angles in pinching tasks.

Conclusions:

  • The proposed depth-based hand pose estimation method is a valid and reliable tool for measuring finger joint angles.
  • This technology shows significant potential for assessing fine motor skills, aiding in hand rehabilitation and remote patient monitoring.