Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Muscle Coordination and Action01:24

Muscle Coordination and Action

3.3K
Muscle coordination is a complex and finely tuned process essential for smooth and purposeful movements like flexion, extension, adduction, abduction, and rotation. The human body orchestrates the actions of various muscles working in concert, each with a specific role. Four functional types describe how muscles work together: agonist, antagonist, synergist, and fixator.
Agonists
Agonist muscles, often called prime movers, are the primary muscles responsible for producing a specific movement....
3.3K
Coordination Number and Geometry02:57

Coordination Number and Geometry

19.2K
For transition metal complexes, the coordination number determines the geometry around the central metal ion. Table 1 compares coordination numbers to molecular geometry. The most common structures of the complexes in coordination compounds are octahedral, tetrahedral, and square planar.
19.2K
Anatomical Movements00:51

Anatomical Movements

16.6K
Anatomical movements refer to the various actions or motions that can be performed by the body's joints and muscles. These movements are described using specific terms to provide a standardized way of discussing and understanding the range of motion at different joints.
Here are some common anatomical movements:
Flexion and extension motions are in the sagittal (anterior–posterior) plane of motion. These movements take place at the shoulder, hip, elbow, knee, wrist,...
16.6K
Functional Classification of Joints01:09

Functional Classification of Joints

8.2K
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...
8.2K
Bones of the Upper Limb: Ulna01:15

Bones of the Upper Limb: Ulna

10.1K
The ulna and radius are parallel bones of the antebrachium or the forearm. The ulna lies medially and consists of a bony tip called the olecranon process at its proximal end. This hook-like projection articulates with the olecranon fossa of the humerus and forms the "hinged" ulnohumeral part of the elbow joint. This joint facilitates forearm extension and flexion while preventing its hyperextension. Similarly, the coronoid process, another bony projection on the proximal/anterior side...
10.1K
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

1.0K
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.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it...
1.0K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Alternative cue and response modalities maintain the Simon effect but impact task performance.

Applied ergonomics·2022
Same author

Effects of Concurrent and Terminal Visual Feedback on Ankle Co-Contraction in Older Adults during Standing Balance.

Sensors (Basel, Switzerland)·2021
Same author

A Neural Network Estimation of Ankle Torques From Electromyography and Accelerometry.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2021
Same author

Quantifying warfighter performance during a bounding rush (prone-sprinting-prone) maneuver.

Applied ergonomics·2021
Same author

Body-worn IMU array reveals effects of load on performance in an outdoor obstacle course.

PloS one·2019
Same author

Objective Metrics Quantifying Fit and Performance in Spacesuit Assemblies.

Aerospace medicine and human performance·2018

Related Experiment Video

Updated: Feb 23, 2026

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy
07:43

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy

Published on: July 2, 2021

3.7K

Quantification and visualization of coordination during non-cyclic upper extremity motion.

Richard A Fineman1, Leia A Stirling2

  • 1Harvard-MIT Division of Health Science & Technology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

Journal of Biomechanics
|September 4, 2017
PubMed
Summary
This summary is machine-generated.

A new relative coordination metric (RCM) quantifies joint coordination during discrete movements, offering a more versatile tool for biomechanical analysis in tele-monitoring systems.

Keywords:
CoordinationGraspPerformance metricsTele-rehabilitationUpper extremity

More Related Videos

Efficiently Recording the Eye-Hand Coordination to Incoordination Spectrum
07:30

Efficiently Recording the Eye-Hand Coordination to Incoordination Spectrum

Published on: March 21, 2019

8.4K
An Objective and Child-friendly Assessment of Arm Function by Using a 3-D Sensor
07:25

An Objective and Child-friendly Assessment of Arm Function by Using a 3-D Sensor

Published on: February 12, 2018

7.3K

Related Experiment Videos

Last Updated: Feb 23, 2026

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy
07:43

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy

Published on: July 2, 2021

3.7K
Efficiently Recording the Eye-Hand Coordination to Incoordination Spectrum
07:30

Efficiently Recording the Eye-Hand Coordination to Incoordination Spectrum

Published on: March 21, 2019

8.4K
An Objective and Child-friendly Assessment of Arm Function by Using a 3-D Sensor
07:25

An Objective and Child-friendly Assessment of Arm Function by Using a 3-D Sensor

Published on: February 12, 2018

7.3K

Area of Science:

  • Biomechanics
  • Rehabilitation Engineering
  • Human Movement Science

Background:

  • Tele-monitoring systems face challenges in quantifying complex biomechanical behavior for clinical decision-making.
  • Accurate assessment of joint coordination is crucial but difficult with current methods, especially for non-cyclic movements.

Purpose of the Study:

  • To introduce and validate a novel measure, the relative coordination metric (RCM), for quantifying joint coordination.
  • To assess the applicability of RCM in analyzing discrete motions, specifically a grasping task.

Main Methods:

  • Developed the relative coordination metric (RCM) and explored various normalization techniques.
  • Fifteen healthy participants performed a reach, grasp, transport, and release task with different objects.
  • Calculated RCM time-series for shoulder-elbow, shoulder-wrist, and elbow-wrist joints, applying normalization based on degrees of freedom, angular velocity, magnitude, and range of motion.

Main Results:

  • RCM values varied significantly based on the normalization scheme, task stage, object grasped, and motion trajectory.
  • Percent time spent in specific RCM ranges was evaluated as a composite metric.
  • The study identified key factors influencing joint coordination during the grasping task.

Conclusions:

  • The relative coordination metric (RCM) provides a robust method for quantifying joint coordination in discrete, non-cyclic movements.
  • RCM overcomes limitations of existing measures by being applicable to varied motions and utilizing velocity-based data.
  • RCM shows promise for tele-monitoring and clinical decision-making in evaluating human movement and potential patient populations.