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Related Concept Videos

Functional Classification of Joints

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...

Structural Classification of Joints

Structural Classification of Joints

Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...

Rigid Body Equilibrium Problems - I

Rigid Body Equilibrium Problems - I

A rigid body is said to be in static equilibrium when the net force and the net torque acting on the system is equal to zero. To solve for rigid body equilibrium problems, do the following steps.

Rigid Body Equilibrium Problems - II

Rigid Body Equilibrium Problems - II

A rigid body is in static equilibrium when the net force and the net torque acting on the system are equal to zero.
Consider two children sitting on a seesaw, which has negligible mass. The first child has a mass (m1) of 26 kg and sits at point A, which is 1.6 meters (r1) from the pivot point B; the second child has a mass (m2) of 32 kg and sits at point C. How far from the pivot point B should the second child sit (r2) to balance the seesaw?

Multi-input and Multi-variable systems

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...

Kinematic Equations - II

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The second kinematic equation expresses the final position of an object in terms of its initial position, the distance traveled with the initial constant velocity, and the distance traveled due to a change in velocity. Similar to the first kinematic equation, this equation is also only valid when the acceleration is constant throughout the motion of an object.
Suppose a car merges into freeway traffic on a 200 m long ramp. If its initial velocity is 10 m/s and it accelerates at 2 m/s2, then the...

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Related Experiment Video

Updated: May 24, 2025

Corticospinal Excitability Modulation During Action Observation

Corticospinal Excitability Modulation During Action Observation

Published on: December 31, 2013

Weighted Errors-in-Variables Modelling for Detection of Cortico-Muscular Couplings.

Zhenghao Guo, Verity M McClelland, Wei Dai

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|March 5, 2025

Summary

This study enhances cortico-muscular coherence (CMC) detection by using weighted errors-in-variables (EIV) modeling to extract clearer brain and muscle signals from noisy electroencephalogram (EEG) and electromyogram (EMG) data.

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Last Updated: May 24, 2025

Corticospinal Excitability Modulation During Action Observation

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Published on: December 31, 2013

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Published on: April 11, 2018

Area of Science:

Neuroscience
Biomedical Engineering
Signal Processing

Background:

Cortico-muscular coherence (CMC) measures linear dependency between brain (EEG) and muscle (EMG) signals.
Noise in EEG and EMG signals often hinders reliable CMC detection.

Purpose of the Study:

To introduce a novel method for enhancing CMC estimation by extracting underlying cortical and muscular signals.
To improve the detection of functional cortico-muscular couplings in the presence of signal noise.

Main Methods:

Utilized weighted errors-in-variables (EIV) modeling to address noise in EEG and EMG signals.
Developed two algorithms for EIV system identification: total least squares and weighted total least squares.
Incorporated knowledge of unequal observation variance into the regression for weighted total least squares.

Main Results:

Demonstrated substantial improvements in CMC detection using the proposed EIV modeling approach.
Validated the method's effectiveness on both synthetic and real neurophysiological data.
Successfully extracted relevant cortical and muscular signal components from noisy data.

Conclusions:

Weighted EIV modeling offers a robust solution for enhancing CMC estimation in noisy EEG/EMG data.
The developed algorithms provide effective tools for analyzing functional cortico-muscular couplings.
This approach advances the reliable assessment of brain-muscle interactions during movement control.