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Joints form during embryonic development in conjunction with the formation and growth of the associated bones. The embryonic tissue that gives rise to all bones, cartilage, and connective tissues of the body is called mesenchyme.
The mesenchymal stem cells differentiate into chondrocytes that form the hyaline cartilage, and later the cartilaginous model of the bone. This model further transforms into a bone. This process is known as endochondral ossification.
During development, the limbs...
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Torque01:10

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Torque is an important quantity for describing the dynamics of a rotating rigid body. We see the application of torque in many ways in the world, such as when pressing the accelerator in a car, which causes the engine to apply additional torque on the drivetrain. Here, we define torque and provide a framework to create an equation to calculate torque for a rigid body with fixed-axis rotation.
Torque can be considered as the rotational counterpart to force. Since forces change the translational...
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The process of converting very light nuclei into heavier nuclei is also accompanied by the conversion of mass into large amounts of energy, a process called fusion. The principal source of energy in the sun is a net fusion reaction in which four hydrogen nuclei fuse and ultimately produce one helium nucleus and two positrons.
A helium nucleus has a mass that is 0.7% less than that of four hydrogen nuclei; this lost mass is converted into energy during the fusion. This reaction produces about...
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Torque Free Motion01:15

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The torque-free motion refers to the movement of a rigid body in space when no external torques are acting upon it. This type of motion can be observed in environments where there are no external forces or frictions, like in outer space. For example, a rotation of Mars in space is a torque-free motion. Mars is an axisymmetric object, meaning it has an axis of symmetry along which it rotates, designated as the z-axis. The rotating frame of reference is defined such that the center of mass of...
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Net Torque Calculations01:19

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When a mechanic tries to remove a hex nut with a wrench, it is easier if the force is applied at the farthest end of the wrench handle. The lever arm is the distance from the pivot point (the hex nut in this case) to the person’s hand. If this distance is large, the torque is higher. Only the component of the force perpendicular to the lever arm contributes to the torque. Therefore, pushing the wrench perpendicular to the lever arm is more advantageous. If multiple people apply force to...
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Structural Joints: Synovial Joints01:16

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Synovial joints are the most common type of joint in the body. A key structural characteristic for a synovial joint is the presence of a joint cavity. This fluid-filled space is where the articulating surfaces of the bones contact each other. Also, unlike fibrous or cartilaginous joints, the articulating bone surfaces at a synovial joint are not directly connected to each other with fibrous connective tissue or cartilage. This gives the bones of a synovial joint the ability to move smoothly...
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Author Spotlight: Assessing Brain Activity in Robotic-Assisted Lower Limb Rehabilitation Using fNIRS
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Red de Fusión de Doble Rama: Decodificación Precisa del Par de Múltiples Articulaciones de las Extremidades

Fei Liang, Xin Shi, Hao Lu

    IEEE transactions on bio-medical engineering
    |February 4, 2026
    PubMed
    Resumen
    Este resumen es generado por máquina.

    Este estudio presenta un novedoso marco de aprendizaje profundo de doble rama para la estimación precisa en tiempo real del par de articulaciones de las extremidades inferiores. El método mejora la interacción humano-exoesqueleto al proporcionar un control de par adaptativo rápido y fiable.

    Palabras clave:
    estimación de parexoesqueletointeracción humano-robotaprendizaje profundoredes neuronales convolucionales temporalesTransformers

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    Área de la Ciencia:

    • Ingeniería Biomédica
    • Robótica
    • Aprendizaje Automático

    Sus antecedentes:

    • La estimación precisa en tiempo real del par de las articulaciones de las extremidades inferiores es crucial para la interacción adaptativa humano-exoesqueleto.
    • Los métodos existentes tienen dificultades con la locomoción diversa y los entornos dinámicos.

    Objetivo del estudio:

    • Desarrollar un marco novedoso para la estimación precisa en tiempo real del par de las articulaciones de las extremidades inferiores en diversas condiciones de locomoción.
    • Mejorar la interacción adaptativa humano-exoesqueleto a través de un control de par preciso.

    Principales métodos:

    • Se desarrolló una arquitectura de doble rama que combina Redes Convolucionales Temporales (TCN) y Transformers.
    • TCN procesó la dinámica temporal local, mientras que Transformers capturó las dependencias globales.
    • Se empleó un mecanismo de fusión residual específico para cada articulación y consciente de la tarea con mejora residual para la síntesis de características.

    Principales resultados:

    • El marco logró una alta precisión en doce patrones de locomoción con bajos errores cuadráticos medios (por ejemplo, 0,1405 Nm/kg para la rodilla) y altos coeficientes de correlación de Pearson (por ejemplo, 0,9904 para el tobillo).
    • Mantuvo una baja latencia de 4,2912 ms, lo que demuestra eficiencia computacional.
    • Mostró una fuerte adaptabilidad en conjuntos de datos públicos.

    Conclusiones:

    • El método propuesto equilibra eficazmente la alta precisión de estimación con la eficiencia computacional requerida para aplicaciones en tiempo real.
    • Aborda con éxito las limitaciones en la adaptación a entornos dinámicos para sistemas humano-exoesqueleto.
    • Este avance proporciona una solución rápida y fiable para el control adaptativo del par del exoesqueleto, mejorando la interacción natural humano-robot.