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Related Concept Videos

Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

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Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
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Relative Motion Analysis using Rotating Axes - Acceleration01:22

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Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame. The absolute velocity of point B is determined by adding the absolute velocity of point A, the relative velocity of point B in the rotating frame, and the effects caused by the angular velocity within the rotating frame.
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Relative Motion Analysis - Velocity01:24

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A stroke engine has a slider-crank mechanism that converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider.
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Absolute Motion Analysis- General Plane Motion01:24

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Visualize a drone, with its propellers spinning rapidly, hovering mid-air. The fascinating movements and operations of this drone can be comprehended by applying the principle of general plane motion.
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Related Experiment Video

Updated: Oct 10, 2025

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field
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Vision-based human joint angular velocity estimation during squat and walking on a treadmill actions.

Konki Sravan Kumar, Ankhzaya Jamsarndorj, Dawoon Jung

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 11, 2021
    PubMed
    Summary

    This study developed a 2D camera system using deep convolution neural networks (CNNs) to accurately measure lower limb joint angular velocity during squat and walking actions, eliminating the need for wearable sensors.

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    Area of Science:

    • Biomechanics
    • Computer Vision
    • Rehabilitation Technology

    Background:

    • Continuous assessment of joint angle and angular velocity is crucial for elderly health monitoring, rehabilitation, and sports supervision.
    • Existing measurement systems often rely on specialized kinematic sensors, limiting accessibility and practicality.
    • There is a need for non-invasive, sensor-free methods to estimate lower limb joint angular velocities.

    Purpose of the Study:

    • To measure lower limb joint angular velocity using a 2D vision camera system and a deep convolution neural network (CNN) architecture.
    • To evaluate the system's accuracy and applicability during squat and treadmill walking actions.
    • To demonstrate the potential for eliminating the need for wearable sensors in joint motion analysis.

    Main Methods:

    • A deep CNN architecture was employed to analyze 2D video data captured by six digital cameras.
    • Experiments involved 12 healthy adults performing squat and treadmill walking actions.
    • Normalized cross-correlation (Cc_norm) analysis was used to compare estimated angular velocities with ground truth data.

    Main Results:

    • The deep CNN model achieved high accuracy, with mean Cc_norm values above 0.90 for treadmill walking and 0.89 for squat actions.
    • The system demonstrated higher estimation performance using lateral and frontal camera views.
    • Accurate joint-wise angular velocities were estimated for the hip, knee, and ankle joints.

    Conclusions:

    • The proposed video-based system effectively measures lower limb joint angular velocities without wearable sensors.
    • This technology shows significant potential for applications in elderly health monitoring, rehabilitation training, and sports supervision.
    • The study validates the applicability of deep learning and 2D vision for non-invasive biomechanical analysis.