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Functional Classification of Joints01:09

Functional Classification of Joints

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Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
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Updated: Sep 1, 2025

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
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Continuous Estimation of Human Joint Angles From sEMG Using a Multi-Feature Temporal Convolutional Attention-Based

Shurun Wang, Hao Tang, Lifu Gao

    IEEE Journal of Biomedical and Health Informatics
    |August 15, 2022
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel Multi-Feature Temporal Convolutional Attention Network (MFTCAN) for accurate continuous motion estimation using surface electromyography (sEMG) signals in human-machine interaction. MFTCAN demonstrates superior performance over existing methods like LSTM, improving intention recognition for human-exoskeleton systems.

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    Area of Science:

    • Biomedical Engineering
    • Machine Learning
    • Human-Machine Interaction

    Background:

    • Accurate continuous motion estimation from surface electromyography (sEMG) signals is crucial for effective human-machine interaction (HMI).
    • Traditional Convolutional Neural Networks (CNNs) struggle with time-series data dependencies, often necessitating Recurrent Neural Networks (RNNs) or Long Short-Term Memory (LSTM) networks for motion estimation.
    • Existing methods face challenges in achieving high accuracy and robustness in continuous motion estimation tasks.

    Purpose of the Study:

    • To propose and evaluate a novel Multi-Feature Temporal Convolutional Attention-based Network (MFTCAN) for continuous joint angle recognition.
    • To investigate the integration of MFTCAN with statistical algorithms (KNR, SVR, LR) for enhanced motion estimation.
    • To validate the proposed method's performance against established models like LSTM and Backpropagation (BP) on both custom and open datasets.

    Main Methods:

    • Signal acquisition experiments were conducted with ten subjects performing various motion patterns.
    • A Multi-Feature Temporal Convolutional Attention-based Network (MFTCAN) was developed for feature extraction and time-series analysis.
    • MFTCAN was integrated with K-Nearest Neighbors Regression (KNR), Support Vector Regression (SVR), and Linear Regression (LR) models.
    • Performance was evaluated using Root Mean Square Error (RMSE) and a correlation coefficient ([Formula: see text]), including validation on the Ninapro DB2 open dataset.

    Main Results:

    • The MFTCAN-KNR model achieved an average RMSE of 0.14, significantly outperforming LSTM (0.20) and BP (0.21) on the custom dataset.
    • The average [Formula: see text] for MFTCAN-KNR reached 0.87, indicating strong anti-disturbance capabilities.
    • MFTCAN-KNR demonstrated high performance on the Ninapro DB2 open dataset, confirming its generalizability and effectiveness.

    Conclusions:

    • The proposed MFTCAN, particularly when integrated with KNR, offers a robust and accurate solution for continuous motion estimation from sEMG signals.
    • The MFTCAN architecture effectively captures temporal dependencies and extracts relevant features, surpassing traditional methods.
    • The developed approach holds significant potential for practical implementation in human-exoskeleton interaction systems and other HMI applications.