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

L-Dopa Comparably Improves Gait and Limb Movements in Parkinson's Disease: A Wearable Sensor Analysis.

Biomedicines·2025
Same author

Quasi-Static and Dynamic Measurement Capabilities Provided by an Electromagnetic Field-Based Sensory Glove.

Biosensors·2025
Same author

Design, Calibration and Morphological Characterization of a Flexible Sensor with Adjustable Chemical Sensitivity and Possible Applications to Sports Medicine.

Sensors (Basel, Switzerland)·2024
Same author

Recognition of Hand Gestures Based on EMG Signals with Deep and Double-Deep Q-Networks.

Sensors (Basel, Switzerland)·2023
Same author

Artificial Intelligence-Based Voice Assessment of Patients with Parkinson's Disease Off and On Treatment: Machine vs. Deep-Learning Comparison.

Sensors (Basel, Switzerland)·2023
Same author

Hand Gesture Recognition Using EMG-IMU Signals and Deep Q-Networks.

Sensors (Basel, Switzerland)·2022

Related Experiment Video

Updated: Dec 14, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

5.0K

Sign Language Recognition Using Wearable Electronics: Implementing k-Nearest Neighbors with Dynamic Time Warping and

Giovanni Saggio1, Pietro Cavallo2, Mariachiara Ricci1

  • 1Department of Electronic Engineering, University of Rome "Tor Vergata", Via Politecnico 1, 00133 Rome, Italy.

Sensors (Basel, Switzerland)
|July 16, 2020
PubMed
Summary

This study introduces a sign language recognition system using wearable sensors and advanced AI classifiers. Both k-Nearest Neighbors with Dynamic Time Warping and Convolutional Neural Networks achieved high accuracy in recognizing Italian sign language words.

Keywords:
IMUclassifiersgesture recognitionsensory glovesign languagewearable electronics

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

1.0K
A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare
06:34

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

Published on: July 7, 2023

3.0K

Related Experiment Videos

Last Updated: Dec 14, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

5.0K
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

1.0K
A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare
06:34

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

Published on: July 7, 2023

3.0K

Area of Science:

  • Computer Science
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Sign language recognition is crucial for communication accessibility.
  • Existing systems often lack comprehensive movement tracking.
  • Wearable technology offers a promising avenue for real-time sign language interpretation.

Purpose of the Study:

  • To develop and evaluate a sign language recognition system using wearable electronics.
  • To compare the performance of k-Nearest Neighbors with Dynamic Time Warping and Convolutional Neural Networks for sign language classification.
  • To assess the system's accuracy in recognizing a set of Italian and international sign words.

Main Methods:

  • A wearable system comprising a sensory glove and inertial measurement units was developed to capture hand, wrist, and arm movements.
  • Two classification algorithms were implemented: k-Nearest Neighbors with Dynamic Time Warping and Convolutional Neural Networks.
  • Seven participants (five male, two female) performed 100 repetitions of ten distinct sign words, including Italian and international terms.

Main Results:

  • The k-Nearest Neighbors with Dynamic Time Warping classifier achieved an accuracy of 96.6% ± 3.4%.
  • The Convolutional Neural Networks classifier demonstrated a higher accuracy of 98.0% ± 2.0%.
  • Both classifiers exhibited high performance, outperforming many existing sign language recognition systems.

Conclusions:

  • The proposed wearable sign language recognition system is effective and highly accurate.
  • Convolutional Neural Networks offer superior performance compared to k-Nearest Neighbors with Dynamic Time Warping for this task.
  • The system represents a significant advancement in wearable technology for sign language interpretation.