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

Classification of Skeletal Muscle Fibers01:48

Classification of Skeletal Muscle Fibers

57.1K
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...
57.1K
Classification of Bones01:18

Classification of Bones

7.7K
The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The...
7.7K

You might also read

Related Articles

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

Sort by
Same author

Muscle Fatigue Assessment in Healthcare Application by Using Surface Electromyography: A Transfer Learning Approach.

Sensors (Basel, Switzerland)·2026
Same author

Radar-Based Activity Recognition in Strictly Privacy-Sensitive Settings Through Deep Feature Learning.

Biomimetics (Basel, Switzerland)·2025
Same author

A Systematic Review of Surface Electromyography in Sarcopenia: Muscles Involved, Signal Processing Techniques, Significant Features, and Artificial Intelligence Approaches.

Sensors (Basel, Switzerland)·2025
Same author

A Transfer Learning Approach for Toe Walking Recognition Using Surface Electromyography on Leg Muscles.

Sensors (Basel, Switzerland)·2025
Same author

Preliminary Study on Wearable Smart Socks with Hydrogel Electrodes for Surface Electromyography-Based Muscle Activity Assessment.

Sensors (Basel, Switzerland)·2025
Same author

Exploring Dance as a Therapeutic Approach for Parkinson Disease Through the Social Robotics for Active and Healthy Ageing (SI-Robotics): Results From a Technical Feasibility Study.

JMIR aging·2025

Related Experiment Video

Updated: Sep 27, 2025

Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos
13:35

Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos

Published on: March 21, 2021

10.8K

Comparative Analysis of Supervised Classifiers for the Evaluation of Sarcopenia Using a sEMG-Based Platform.

Alessandro Leone1, Gabriele Rescio1, Andrea Manni1

  • 1National Research Council of Italy, Institute for Microelectronics and Microsystems, 73100 Lecce, Italy.

Sensors (Basel, Switzerland)
|April 12, 2022
PubMed
Summary

Early detection of sarcopenia is crucial for managing this geriatric condition. This study developed a platform using surface electromyography to classify sarcopenia levels, with Support Vector Machines showing the best accuracy.

Keywords:
ageingmachine learningsarcopeniasurface EMG

More Related Videos

Non-invasive Skeletal Muscle Quantification in Small Animals Using Micro-computed Tomography
07:33

Non-invasive Skeletal Muscle Quantification in Small Animals Using Micro-computed Tomography

Published on: November 8, 2024

577
Semi-automated Analysis of Mouse Skeletal Muscle Morphology and Fiber-type Composition
08:36

Semi-automated Analysis of Mouse Skeletal Muscle Morphology and Fiber-type Composition

Published on: August 31, 2017

10.7K

Related Experiment Videos

Last Updated: Sep 27, 2025

Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos
13:35

Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos

Published on: March 21, 2021

10.8K
Non-invasive Skeletal Muscle Quantification in Small Animals Using Micro-computed Tomography
07:33

Non-invasive Skeletal Muscle Quantification in Small Animals Using Micro-computed Tomography

Published on: November 8, 2024

577
Semi-automated Analysis of Mouse Skeletal Muscle Morphology and Fiber-type Composition
08:36

Semi-automated Analysis of Mouse Skeletal Muscle Morphology and Fiber-type Composition

Published on: August 31, 2017

10.7K

Area of Science:

  • Geriatrics
  • Biomedical Engineering
  • Signal Processing

Background:

  • Sarcopenia, a geriatric condition, significantly impacts older adults' health, functional independence, and quality of life.
  • Early recognition of sarcopenia's level and progression is vital for effective disease management.
  • Surface electromyography (sEMG) is increasingly important for sarcopenia diagnosis, aided by wearable devices.

Purpose of the Study:

  • Design and implement a hardware/software platform for sarcopenia assessment using sEMG signals.
  • Analyze muscle strength from Gastrocnemius Lateralis and Tibialis Anterior muscles to differentiate three sarcopenia confidence levels.
  • Compare the efficiency of state-of-the-art supervised classifiers for sarcopenia evaluation.

Main Methods:

  • Developed a hardware/software platform for sEMG signal processing.
  • Utilized signals from Gastrocnemius Lateralis and Tibialis Anterior muscles.
  • Compared supervised classifiers on an augmented dataset from 32 patients across three sarcopenia levels.

Main Results:

  • The platform successfully distinguished between different sarcopenia confidence levels.
  • Support Vector Machine (SVM) classifier achieved the highest accuracy.
  • SVM outperformed other classifiers by an average of 7.7% in accuracy.

Conclusions:

  • The proposed sEMG-based platform is effective for differentiating sarcopenia levels.
  • SVM demonstrates superior performance in classifying sarcopenia stages.
  • This technology holds promise for early sarcopenia detection and management.