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Related Experiment Video

Updated: May 20, 2026

Three-Dimensional Finger Motion Tracking during Needling: A Solution for the Kinematic Analysis of Acupuncture Manipulation
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Continuous Kalman Estimation Method for Finger Kinematics Tracking from Surface Electromyography.

Haoshi Zhang1,2, Boxing Peng1,2, Lan Tian1

  • 1CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen 518055, China.

Cyborg and Bionic Systems (Washington, D.C.)
|May 16, 2024
PubMed
Summary
This summary is machine-generated.

This study presents a continuous Kalman estimation method for decoding hand motion intention from surface electromyography (sEMG). The novel approach accurately estimates multiple degrees of freedom (DOFs) finger kinematics in real-time.

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Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Robotics

Background:

  • Decoding hand motion intention from surface electromyography (sEMG) is crucial for intuitive human-computer interaction.
  • Existing methods for continuous kinematics estimation face challenges in accuracy and computational load.
  • Kalman estimation offers adaptability and ease of implementation for real-time systems.

Purpose of the Study:

  • To introduce a continuous Kalman estimation method for estimating multiple degrees of freedom (DOFs) finger kinematics from sEMG.
  • To validate the accuracy and computational efficiency of the proposed method.
  • To explore the potential of this technology for natural and intuitive continuous finger motion estimation.

Main Methods:

  • Developed a continuous Kalman estimation model using sEMG and joint angles as inputs and outputs.
  • Employed model parameter training methods to deduce multiple DOF finger kinematics simultaneously.
  • Validated the method using a publicly accessible database.

Main Results:

  • Achieved a correlation coefficient (CC) of 0.73 in validating the continuous estimation method.
  • Demonstrated an average computation time of under 0.01 seconds per window.
  • Successfully trained Kalman model parameters using over 45,000 windows.

Conclusions:

  • The proposed continuous Kalman estimation method effectively decodes multiple DOF finger kinematics from sEMG.
  • The technique offers a computationally efficient and accurate solution for real-time motion intention recognition.
  • This pilot study highlights significant potential for advancing continuous finger motion estimation technologies.