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February 27, 2026IEEE Transactions on Neural Systems and Rehabilitation Engineering0 citationsOpen Access

A Lightweight Hybrid Encoder-decoder Framework for Multiple Degree of Freedom Muscle Force Estimation

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YZYang ZhengYLYixin LiHZHaixiong Zhang

Key Points

  • The aim is to develop a hybrid encoder-decoder framework for estimating muscle forces in dexterous movements.
  • Developed a lightweight hybrid encoder-decoder model.
  • Applied the model to predict muscle forces for finger movements.
  • Evaluated the model's performance against existing methods.
  • The hybrid framework showed improved accuracy in force estimation.
  • It reduced computational load compared to traditional models.
  • The method indicates potential for practical human machine interfaces.

Abstract

Further development of the proposed method could potentially provide a robust human machine interface for dexterous finger force prediction in realistic applications.

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Cite This Study

Zheng et al. (2026) studied this question.

synapsesocial.com/papers/69a1344fed1d949a99abe1dchttps://doi.org/10.1109/tnsre.2026.3667588
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Unsupervised Decoding of Multi-Finger Forces Using Neuronal Discharge Information with Muscle Co-Activations2024
  2. 2Decomposing Task-Relevant Information From Surface Electromyogram for User-Generic Dexterous Finger Force Decoding2024
  3. 3Training Explainable and Effective Multi-DoF EMG Decoder Using Additive 1-DoF EMG2024 · 3 citations
  4. 4Finger Force Decoding from Motor Units Activity on Neuromorphic Hardware2025
  5. 5A Resource-Efficient Method for Real-Time Flexion–Extension Angle Estimation with an Under-Sensorized Finger Exoskeleton2026