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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.
Synarthrosis
An...
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Muscles that Move the Leg01:23

Muscles that Move the Leg

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The movement of the legs is facilitated by numerous muscles located within the anterior, medial, and posterior compartments of the thigh.
Anterior Compartment
The quadriceps femoris, the most visible muscle of the anterior compartment, is integral for leg extension and thigh flexion. It is formed by merging four distinct muscles — the vastus lateralis, vastus medialis, vastus intermedius, and rectus femoris. The quadriceps tendon, a shared tendon of the four quadriceps muscles, is affixed...
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Evaluation of pulse rate variability obtained by the pulse onsets of the photoplethysmographic signal.

Physiological measurementยท2013
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Related Experiment Video

Updated: Mar 27, 2026

Lower Limb Biomechanical Analysis of Healthy Participants
06:36

Lower Limb Biomechanical Analysis of Healthy Participants

Published on: April 15, 2020

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Knee motion pattern classification from trunk muscle based on sEMG signals.

A Lopez-Delis, D Delisle-Rodriguez, A C Villa-Parra

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 7, 2016
    PubMed
    Summary
    This summary is machine-generated.

    Researchers can detect knee motion intention from Erector spinae (ES) muscle signals with over 95% accuracy. This breakthrough enables improved myoelectric control for lower limb assistive exoskeletons, aiding individuals with mobility impairments.

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    Area of Science:

    • Rehabilitation Engineering
    • Biomedical Signal Processing
    • Neuroprosthetics

    Background:

    • Assistive exoskeletons aim to restore locomotion for individuals with lower limb impairments.
    • Surface Electromyography (sEMG) is a key technology for controlling such devices.
    • Accurate detection of movement intention is crucial for effective exoskeleton control.

    Purpose of the Study:

    • To characterize knee motion patterns using sEMG signals from the Erector spinae (ES) muscle.
    • To evaluate feature extraction and pattern classification methods for movement recognition.
    • To determine an optimal electrode configuration for reliable control.

    Main Methods:

    • sEMG signals were recorded from the ES muscle during eight distinct movement classes.
    • Feature extraction included mean absolute value, waveform length, and auto-regressive modeling.
    • Pattern classification employed Linear Discrimination Analysis, K-Nearest Neighbors, and Support Vector Machines.

    Main Results:

    • High accuracy (>95%) was achieved in recognizing knee motion intentions from ES muscle sEMG.
    • Optimal electrode configurations were identified using 2 to 3 channels.
    • The methods demonstrated high sensitivity, specificity, and predictive positive value.

    Conclusions:

    • Knee motion intention can be reliably detected from ES muscle sEMG signals.
    • Reduced electrode count (2-3 channels) is sufficient for effective detection.
    • This approach is applicable for the myoelectric control of lower limb active exoskeletons.