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Updated: Aug 25, 2025
09:41
Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
Published on: April 21, 2023
1.7K
Continuous Grasping Force Estimation With Surface EMG Based on Huxley-Type Musculoskeletal Model.
Summary
This study presents an efficient method for estimating grasping force using electromyography (EMG) signals. The approach balances accuracy and computational resources for practical applications like prosthetic control.
Area of Science:
- Biomechanics
- Neuroscience
- Robotics
Background:
- Accurate grasping force estimation using electromyography (EMG) signals is crucial for applications like prosthetic control.
- Existing methods often involve a trade-off between computational precision and resource demands, particularly when using complex muscle models like the Huxley-type model.
Purpose of the Study:
- To develop a grasping force estimation method that achieves high accuracy with low time delay.
- To simplify complex musculoskeletal models for practical, real-time applications.
Main Methods:
- Utilized a reduced Huxley-type musculoskeletal model and balanced truncation for dimensionality reduction.
- Implemented a Kalman filter for processing armband-acquired EMG signals, outperforming traditional filters.
- Employed a particle swarm optimization method for effective model parameter identification.
Main Results:
- Achieved grasping force estimation with high accuracy and low time delay.
- Demonstrated improved real-time performance and accuracy using the Kalman filter for EMG signal processing.
- Successfully identified model parameters through particle swarm optimization.
Conclusions:
- The developed method offers a practical and efficient solution for continuous grasping force estimation.
- This approach has the potential to significantly enhance real-world applications in prosthetic control and human force observation.
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