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Related Concept Videos

Muscles that Move the Forearm01:16

Muscles that Move the Forearm

The muscles that move the forearms can be divided into four groups: forearm flexors, forearm extensors, forearm pronators, and forearm supinators. The flexors and extensors act on the elbow joint, while the pronators and supinators act on the radioulnar joints.
Forearm Flexors
The biceps brachii, brachialis, and brachioradialis are forearm flexors. The biceps brachii is made up of two heads. Its long head originates at the supraglenoid tubercle of the scapula, whereas that of the short head is...
Muscles of the Forearm that Move the Hand and Fingers01:16

Muscles of the Forearm that Move the Hand and Fingers

The muscles of the forearm that move the wrist, hand, and digits are numerous and diverse. They can be classified into two groups based on their location and function — the anterior and posterior compartment muscles.
Anterior Compartment
The anterior compartment muscles originate from the humerus. They primarily function as flexors and are also known as flexor muscles. They typically insert on the carpals, metacarpals, and phalanges. The superficial layer includes the flexor carpi radialis,...

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Related Experiment Video

Updated: May 10, 2026

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Forearm Motion and Hand Grasp Prediction Based on Target Muscle Bioimpedance for Human-Machine Interaction.

Tianyang Yao, Yu Wu, Dai Jiang

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |March 3, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study presents a new method using muscle bioimpedance to predict hand grasp and forearm motion. Long short-term memory (LSTM) models show superior accuracy for multi-degree of freedom (DoF) predictions.

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    Area of Science:

    • Biomedical Engineering
    • Rehabilitation Technology
    • Human-Computer Interaction

    Background:

    • Accurate prediction of hand grasp and forearm motion is crucial for advanced prosthetic control.
    • Existing methods often require extensive training and lack real-time adaptability.
    • Muscle bioimpedance offers a non-invasive signal source for motion intention detection.

    Purpose of the Study:

    • To develop and validate a novel methodology for simultaneous multi-degree of freedom (DoF) prediction of hand grasp and forearm motion.
    • To compare the efficacy of different regression models, including Long Short-Term Memory (LSTM), for bioimpedance-based motion prediction.
    • To explore the potential for direct mapping of bioimpedance variations for amputee control without model retraining.

    Main Methods:

    • Utilized six channels from nine electrodes to measure target muscle bioimpedance.
    • Employed various regression models: Linear Regression (LR), Support Vector Regression (SVR), Multilayer Perceptron (MLP), and Long Short-Term Memory (LSTM).
    • Performed intra-subject cross-validation to assess prediction accuracy for hand grasping angle and forearm motion.

    Main Results:

    • LSTM regression demonstrated superior performance in multi-DoF prediction compared to LR, SVR, and MLP.
    • MLP achieved an average R-squared (R2) of 0.9256 for hand grasping angle prediction.
    • LSTM achieved an average R2 of 0.9483 for predicting simultaneous two-DoF forearm motion.
    • Demonstrated real-time object grasping task performance, validating the prediction approaches.

    Conclusions:

    • Muscle bioimpedance combined with LSTM regression provides a robust and accurate method for predicting complex hand and forearm movements.
    • The proposed approach shows promise for intuitive control of prosthetic devices, particularly for amputees, by enabling direct mapping of bioimpedance to motion.
    • This methodology advances non-invasive human-machine interfaces for improved functional restoration.