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Ankle Joint01:10

Ankle Joint

The ankle is formed by the talocrural joint (crural = leg). It consists of the articulations between the talus bone of the foot and the distal ends of the tibia and fibula of the leg. The superior aspect of the talus bone is square-shaped and has three areas of articulation. The top of the talus articulates with the inferior tibia. This is the portion of the ankle joint that carries the body weight between the leg and foot. The sides of the talus are firmly held in position by the articulations...
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The knee joint is the most complicated joint in the body. It consists of three articulations– two tibiofemoral and one patellofemoral. As is characteristic of synovial joints, the knee joint has a thin articular capsule that partially surrounds this joint cavity. Additionally, several ligaments, muscles, and cartilaginous structures support the movement of the knee.
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Net Torque Calculations01:19

Net Torque Calculations

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 rotate...
Bones of the Lower Limb: Femur and Patella01:16

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The femur is the body's longest and strongest bone spanning the thigh region. Its head articulates with the acetabulum of the hip bone to form the hip joint. A minor indentation on the medial side of the femoral head, called the fovea capitis, serves as the site of attachment for the ligament of the head of the femur. This weak ligament spans the femur and acetabulum and supports the hip joint. The narrowed region below the head is the neck of the femur. The inclination angle between the neck...
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Development of the Limb Synovial Joints

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Functional Classification of Joints

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 immobile...

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Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task
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Lower extremity joint torque predicted by using artificial neural network during vertical jump.

Yu Liu1, Shi-Min Shih, Shi-Liu Tian

  • 1School of Kinesiology, Shanghai University of Sport, 200438 Shanghai, China.

Journal of Biomechanics
|March 6, 2009
PubMed
Summary

This study developed an artificial neural network (ANN) to predict lower extremity joint torques from ground reaction force (GRF) data during jumps. The ANN model accurately estimated joint torques, offering a new method for biomechanical analysis.

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Area of Science:

  • Biomechanics
  • Sports Science
  • Artificial Intelligence

Background:

  • Accurate estimation of lower extremity joint torques is crucial for understanding athletic performance and injury prevention.
  • Traditional inverse dynamics methods require complex motion capture systems.
  • Developing efficient predictive models for joint torques is an active area of research.

Purpose of the Study:

  • To develop and validate an artificial neural network (ANN) model for predicting lower extremity joint torques.
  • To utilize ground reaction force (GRF) and derived parameters as inputs for the ANN.
  • To assess the ANN's accuracy in estimating joint torques during counter-movement jump (CMJ) and squat jump (SJ) movements.

Main Methods:

  • A fully connected, feed-forward artificial neural network (ANN) was designed with one input, one hidden, and one output layer.
  • The ANN was trained using the error back-propagation algorithm with the Steepest Descent Method.
  • Input parameters included GRF measurements and related derived parameters; output parameters were three lower extremity joint torques.

Main Results:

  • The ANN model demonstrated a good fit with the results obtained from inverse dynamics calculations.
  • The developed ANN successfully predicted lower extremity joint torques during CMJ and SJ.
  • The model effectively utilized GRF data and related parameters for torque estimation.

Conclusions:

  • The developed ANN model provides a viable and accurate method for estimating lower extremity joint torques.
  • This approach offers a potentially simpler alternative to traditional inverse dynamics for analyzing CMJ and SJ.
  • The findings suggest the ANN model can be a valuable tool in sports science and biomechanical research for performance and injury analysis.