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Related Experiment Video

Updated: Feb 25, 2026

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion
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Beyond Subject-Specific Models in Dynamical Human-Machine Interaction: Benchmarking and Optimization Strategies.

Luca Manneschi, Matthew O A Ellis, Elisa Donati

    IEEE Transactions on Neural Networks and Learning Systems
    |February 23, 2026
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    Summary
    This summary is machine-generated.

    This study benchmarks deep learning models for continuous finger position estimation using surface electromyography (EMG). Temporal Convolutional Networks (TCNs) achieved state-of-the-art results, advancing prosthetic and VR control.

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    Area of Science:

    • Biomedical Engineering
    • Machine Learning
    • Neuroscience

    Background:

    • Surface electromyography (EMG) offers intuitive control for human-machine interfaces.
    • Accurate EMG-based finger position estimation requires robust temporal modeling and user adaptation.
    • Existing methods often struggle with user variability and real-time performance.

    Purpose of the Study:

    • To benchmark various deep learning architectures for continuous finger position estimation from EMG.
    • To investigate adaptive learning strategies for improved cross-subject generalization.
    • To introduce and evaluate neural ordinary differential equations (NODEs) for EMG-based regression.

    Main Methods:

    • Benchmarking recurrent neural networks, temporal convolutional networks (TCNs), Transformers, and neural ordinary differential equations (NODEs).
    • Systematic tuning of model receptive fields based on EMG autocorrelation.
    • Implementation of adaptive learning methods including multitask, transfer, and meta-learning, with lightweight fine-tuning (LoRA, adapter layers).

    Main Results:

    • Temporal Convolutional Networks (TCNs) achieved state-of-the-art performance on the Ninapro DB8 dataset.
    • Mean absolute errors (MAEs) below 5.4 were achieved with multitask and transfer learning.
    • A mean absolute error (MAE) of 6.47 was obtained with two-shot meta-learning.

    Conclusions:

    • TCNs provide a highly effective architecture for EMG-to-kinematics regression.
    • Adaptive learning strategies significantly enhance cross-subject generalization for EMG-based control.
    • These advancements offer practical solutions for personalized, real-time control in prosthetics, VR, and teleoperation.