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

Muscles that Move the Forearm01:16

Muscles that Move the Forearm

4.0K
The muscles that move the forearms can be divided into four groups: forearm flexors, forearm extensors, forearm pronators, and forearm supinators. The flexors and extensors act on the elbow joint, while the pronators and supinators act on the radioulnar joints.
Forearm Flexors
The biceps brachii, brachialis, and brachioradialis are forearm flexors. The biceps brachii is made up of two heads. Its long head originates at the supraglenoid tubercle of the scapula, whereas that of the short head is...
4.0K
Muscles of the Forearm that Move the Hand and Fingers01:16

Muscles of the Forearm that Move the Hand and Fingers

2.7K
The muscles of the forearm that move the wrist, hand, and digits are numerous and diverse. They can be classified into two groups based on their location and function — the anterior and posterior compartment muscles.
Anterior Compartment
The anterior compartment muscles originate from the humerus. They primarily function as flexors and are also known as flexor muscles. They typically insert on the carpals, metacarpals, and phalanges. The superficial layer includes the flexor carpi...
2.7K
Classification of Skeletal Muscle Fibers01:48

Classification of Skeletal Muscle Fibers

59.6K
Skeletal muscles continuously produce ATP to provide the energy that enables muscle contractions. Skeletal muscle fibers can be categorized into three types based on differences in their contraction speed and how they produce ATP, as well as physical differences related to these factors. Most human muscles contain all three muscle fiber types, albeit in varying proportions.
Slow-Twitch Muscle Fibers
Slow oxidative, muscle fibers appear red due to large numbers of capillaries and high levels of...
59.6K
Muscles of the Eye01:20

Muscles of the Eye

4.5K
The muscles of the eye are sophisticated structures that control eye movement and focus, allowing for the precise and rapid adjustments necessary for vision. The human eye is controlled by ten muscles — six extraocular muscles, three intraocular muscles, and one primary eyelid retractor muscle.
Extraocular Muscles
The six extraocular muscles surround the eyeball and control its movements. They are responsible for a wide range of eye motions, including looking up, down, left, right, and...
4.5K
Muscles that Move the Head01:19

Muscles that Move the Head

6.0K
The muscles that move the head are a dynamic and complex group of structures that work together to facilitate a wide range of head movements, including rotation, flexion, extension, and lateral bending.
The bilateral sternocleidomastoid, or SCM, and the suprahyoid and infrahyoid muscles are significant head flexors. The SCM muscles originate at the sternum and clavicle and attach to the mastoid process of the temporal bone. The SCM contracts bilaterally to bend the head forward, whereas...
6.0K
Muscles of the Abdomen01:21

Muscles of the Abdomen

3.6K
The abdominal wall encircles the abdominal cavity, providing flexible protection and shielding the internal organs from harm. It is bordered at the top by the xiphoid process and costal margins, at the back by the vertebral column, and at the bottom by the pelvic bones and inguinal ligament. The abdominal wall is divided into two regions — the anterolateral and posterior regions.
Anterolateral Region
The anterolateral region comprises five paired muscles classified into the lateral and...
3.6K

You might also read

Related Articles

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

Sort by
Same author

Multisignal Collaborative Detection of Thiram Based on Dual-Functional Iron-Based Metal Organic Frameworks.

Journal of agricultural and food chemistry·2026
Same author

Lanzhou Lily (<i>Lilium davidii var. unicolor</i>) Extract Alleviates Chronic Stress-Induced Mood Disturbances by Suppressing Neuroinflammation and Modulating the Gut-Brain Axis in Mice.

Food science & nutrition·2026
Same author

Machine Learning-Assisted Surface Ligand Engineering Strategy for Enhanced Sensitivity of Immunoassay Platform.

Analytical chemistry·2026
Same author

Dynamic task-related prefrontal functional networks evolved in Stroop Color-Word tasks measured by fNIRS.

Cognitive neurodynamics·2026
Same author

Controlling thermoreversibility and hole conductivity in thermoresponsive ionic biogels using phase morphology for neurohaptics.

Science advances·2026
Same author

Role of nutritional indices (PNI, CONUT, GNRI) in predicting delirium in hospitalised individuals: A systematic review and meta-analysis.

General hospital psychiatry·2026

Related Experiment Video

Updated: Feb 8, 2026

Author Spotlight: Isolation of Long Muscle Fibers from Mouse Hindlimb Muscles for Studying Excitation-Contraction Coupling Across Fiber Types
08:12

Author Spotlight: Isolation of Long Muscle Fibers from Mouse Hindlimb Muscles for Studying Excitation-Contraction Coupling Across Fiber Types

Published on: December 1, 2023

3.6K

Extracting and Classifying Spatial Muscle Activation Patterns in Forearm Flexor Muscles Using High-Density

Chenyun Dai1, Xiaogang Hu1

  • 11 Joint Department of Biomedical Engineering, University of North Carolina - Chapel Hill and North Carolina State University, Raleigh, NC, USA.

International Journal of Neural Systems
|June 30, 2018
PubMed
Summary

High-density surface electromyogram (sEMG) reveals distinct spatial activation patterns in forearm muscles during individual finger movements. These patterns, though overlapping, are highly classifiable, highlighting the need for detailed sEMG recordings.

Keywords:
Finger flexionflexor activationflexor digitorum superficialisforearm flexor muscleshigh-density EMGmuscle compartmentpattern recognition

More Related Videos

Physiological Recordings of High and Low Output NMJs on the Crayfish Leg Extensor Muscle
10:00

Physiological Recordings of High and Low Output NMJs on the Crayfish Leg Extensor Muscle

Published on: November 17, 2010

12.2K
Manual Muscle Testing: A Method of Measuring Extremity Muscle Strength Applied to Critically Ill Patients
09:44

Manual Muscle Testing: A Method of Measuring Extremity Muscle Strength Applied to Critically Ill Patients

Published on: April 12, 2011

83.2K

Related Experiment Videos

Last Updated: Feb 8, 2026

Author Spotlight: Isolation of Long Muscle Fibers from Mouse Hindlimb Muscles for Studying Excitation-Contraction Coupling Across Fiber Types
08:12

Author Spotlight: Isolation of Long Muscle Fibers from Mouse Hindlimb Muscles for Studying Excitation-Contraction Coupling Across Fiber Types

Published on: December 1, 2023

3.6K
Physiological Recordings of High and Low Output NMJs on the Crayfish Leg Extensor Muscle
10:00

Physiological Recordings of High and Low Output NMJs on the Crayfish Leg Extensor Muscle

Published on: November 17, 2010

12.2K
Manual Muscle Testing: A Method of Measuring Extremity Muscle Strength Applied to Critically Ill Patients
09:44

Manual Muscle Testing: A Method of Measuring Extremity Muscle Strength Applied to Critically Ill Patients

Published on: April 12, 2011

83.2K

Area of Science:

  • Neuroscience
  • Biomechanics
  • Biomedical Engineering

Background:

  • The human hand's dexterity relies on intricate neuromuscular control of finger movements.
  • Understanding forearm muscle activation patterns is crucial for precise motor control analysis.

Purpose of the Study:

  • To quantify spatial activation patterns of forearm flexor muscles during individual finger flexions.
  • To differentiate and characterize unique muscle activation signatures for each finger.

Main Methods:

  • Acquired high-density (HD) surface electromyogram (sEMG) signals from forearm flexor muscles.
  • Decomposed individual motor units from sEMG data.
  • Analyzed macro-level EMG patterns and micro-level motor unit distributions using pattern recognition.

Main Results:

  • Ring finger flexion exhibited distinct forearm flexor spatial activation patterns compared to other fingers.
  • Middle finger activation patterns were the least distinguishable among all fingers.
  • Pattern recognition achieved high classification accuracy (94-100%) for different finger activation patterns.

Conclusions:

  • Partial overlap in neural activation limits precise finger movement identification from limited sEMG data.
  • HD sEMG recordings are essential for capturing detailed spatial activation patterns at macro- and micro-levels for accurate analysis.