Integrating Motor Unit Activity With Deep Learning for Real-Time, Simultaneous and Proportional Wrist Angle and Grasp Force Estimation

The proposed framework has the potential to enhance the independence and quality of life of prosthetic users by enabling them to perform a wider range of tasks with improved precision and control over both kinematics and kinetics.

Authors

Ruye Guo (ORCID: https://orcid.org/0009-0003-3694-9007)
Kezhe Zhu (ORCID: https://orcid.org/0000-0002-7986-9694)
Dongxuan Li (ORCID: https://orcid.org/0000-0001-5504-8296)
Peter B. Shull (ORCID: https://orcid.org/0000-0001-8931-5743)
Chen Chen (ORCID: https://orcid.org/0000-0002-3007-4364)

Institutions

Shanghai Jiao Tong University (CN)

Publication Details

Journal: IEEE Transactions on Biomedical Engineering
Published: 2025-05-30
DOI: https://doi.org/10.1109/tbme.2025.3575252
Citations: 1
Primary Topic: Muscle activation and electromyography studies
Type: article
Field-Weighted Citation Impact: 0.67

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article

Integrating Motor Unit Activity With Deep Learning for Real-Time, Simultaneous and Proportional Wrist Angle and Grasp Force Estimation

Ruye Guo, Kezhe Zhu, Dongxuan Li, Peter B. Shull et al.

May 30, 2025

1 citations

IEEE Transactions on Biomedical Engineering

Muscle activation and electromyography studies

0.67

article

Integrating Motor Unit Activity With Deep Learning for Real-Time, Simultaneous and Proportional Wrist Angle and Grasp Force Estimation

Ruye Guo, Kezhe Zhu, Dongxuan Li, Peter B. Shull, Chen Chen

May 30, 2025article en

1 citations

Abstract

The proposed framework has the potential to enhance the independence and quality of life of prosthetic users by enabling them to perform a wider range of tasks with improved precision and control over both kinematics and kinetics.

IEEE Transactions on Biomedical EngineeringVol. 72(12)

Shanghai Jiao Tong University (CN)

Openalex Percentile: Top 31%

Muscle activation and electromyography studies

0.67

DOI Source OpenAlex

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