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

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

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
Same author

Movement Disorders and Smart Wrist Devices: A Comprehensive Study.

Sensors (Basel, Switzerland)·2025
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
Same journal

Three-Dimensional Modeling and Performance Analysis of Dynamic mmWave V2I Networks Based on Stochastic Geometry.

Sensors (Basel, Switzerland)·2026
See all related articles
  1. Home
  3. A Transfer Learning Approach For Toe Walking Recognition Using Surface Electromyography On Leg Muscles.
  1. Home
  3. A Transfer Learning Approach For Toe Walking Recognition Using Surface Electromyography On Leg Muscles.

Related Experiment Video

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

43.2K

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

Andrea Manni1, Gabriele Rescio1, Anna Maria Carluccio1

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

Sensors (Basel, Switzerland)
|March 17, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Detecting toe walking early is vital for preventing health issues. This study uses surface Electromyography (sEMG) and Transfer Learning (TL) to accurately identify toe walking patterns from muscle signals.

Keywords:
gaitsEMGsensorstoe walkingtransfer learning

More Related Videos

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

333
Standing Neurophysiological Assessment of Lower Extremity Muscles Post-Stroke
08:23

Standing Neurophysiological Assessment of Lower Extremity Muscles Post-Stroke

Published on: July 26, 2021

2.4K

Related Experiment Videos

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

43.2K
Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

333
Standing Neurophysiological Assessment of Lower Extremity Muscles Post-Stroke
08:23

Standing Neurophysiological Assessment of Lower Extremity Muscles Post-Stroke

Published on: July 26, 2021

2.4K

Area of Science:

  • Biomedical Engineering
  • Neurology
  • Sports Medicine

Background:

  • Gait monitoring is essential for early detection of motor abnormalities like toe walking.
  • Persistent toe walking can lead to significant musculoskeletal issues, balance problems, and reduced quality of life.
  • Surface Electromyography (sEMG) offers potential for early detection by capturing pre-movement muscle electrical activity.

Purpose of the Study:

  • To propose a novel method for detecting toe walking using lower limb sEMG signals.
  • To address the complexity and noise inherent in sEMG data for robust classification.
  • To leverage Transfer Learning (TL) to improve model accuracy and generalizability across different sEMG devices.

Main Methods:

  • Utilized surface Electromyography (sEMG) sensors to record lower limb muscle electrical activity.
  • Applied Continuous Wavelet Transform (CWT) to convert 1-second sEMG signal windows into 2D scalogram images.
  • Employed Transfer Learning (TL) with pre-trained neural network architectures to classify toe walking patterns.

    • Main Results:

    • Achieved approximately 95% classification accuracy on a public dataset using the InceptionResNetV2 architecture.
    • Demonstrated the effectiveness of the proposed sEMG and TL approach in identifying toe walking.
    • Highlighted the potential of CWT-generated scalograms for robust feature extraction from noisy sEMG data.

    Conclusions:

    • The developed sEMG-based method, enhanced by Transfer Learning and CWT, shows high accuracy in detecting toe walking.
    • This approach offers a promising non-invasive tool for early diagnosis and monitoring of gait abnormalities.
    • Further research can explore broader applications in clinical settings and diverse populations for gait analysis.