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

Design Example: Resistive Touchscreen01:14

Design Example: Resistive Touchscreen

951
A device engineer plays a crucial role in designing user interfaces for mobile devices. One such interface is the resistive touchscreen, which fundamentally consists of two metallic layers: a flexible upper layer and a rigid lower layer, separated by a narrow gap. The high resistance between these two layers is a key characteristic of this design.
When a user touches the screen, the two layers make contact at a specific point known as the touchpoint. This contact reduces the resistance between...
951

You might also read

Related Articles

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

Sort by
Same author

Acetylcholinesterase Inhibition Reverses Age-Related Pulmonary Decline and Increases Bronchus-Associated Lymphoid Tissue Formation in Aged Mice.

Biology·2026
Same author

Sentence-Level Silent Speech Recognition Using a Wearable EMG/EEG Sensor System with AI-Driven Sensor Fusion and Language Model.

Sensors (Basel, Switzerland)·2025
Same author

Wireless Mouth Motion Recognition System Based on EEG-EMG Sensors for Severe Speech Impairments.

Sensors (Basel, Switzerland)·2024
Same author

A Wearable Multimodal Wireless Sensing System for Respiratory Monitoring and Analysis.

Sensors (Basel, Switzerland)·2023
Same author

Techniques for characterizing mechanical properties of soft tissues.

Journal of the mechanical behavior of biomedical materials·2022
Same author

Characterization of mechanical properties of soft tissues using sub-microscale tensile testing and 3D-Printed sample holder.

Journal of the mechanical behavior of biomedical materials·2022

Related Experiment Video

Updated: May 7, 2026

Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification
07:47

Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification

Published on: February 14, 2018

11.1K

A Wearable Silent Text Input System Using EMG and Piezoelectric Sensors.

John S Kang1, Kee S Moon1, Sung Q Lee1

  • 1Department of Mechanical Engineering, San Diego State University, San Diego, CA 92182, USA.

Sensors (Basel, Switzerland)
|April 26, 2025
PubMed
Summary

This study presents a wearable system using Electromyography (EMG) and piezoelectric (PZT) sensors for silent text input. Machine learning models achieved 95.63% accuracy, showing promise for silent communication.

Keywords:
biomedical signal processinghuman–computer-interfacemachine learningsensor fusionsilent text inputspeech disabilitywearable biomedical sensors

More Related Videos

Conformable Wearable Electrodes: From Fabrication to Electrophysiological Assessment
10:03

Conformable Wearable Electrodes: From Fabrication to Electrophysiological Assessment

Published on: July 22, 2022

4.2K
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

2.2K

Related Experiment Videos

Last Updated: May 7, 2026

Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification
07:47

Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification

Published on: February 14, 2018

11.1K
Conformable Wearable Electrodes: From Fabrication to Electrophysiological Assessment
10:03

Conformable Wearable Electrodes: From Fabrication to Electrophysiological Assessment

Published on: July 22, 2022

4.2K
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

2.2K

Area of Science:

  • Biomedical Engineering
  • Human-Computer Interaction
  • Signal Processing

Background:

  • Silent speech recognition is crucial for assistive communication and discreet input.
  • Existing methods often require bulky equipment or invasive procedures.
  • Wearable, non-audible systems offer a novel approach to silent text input.

Purpose of the Study:

  • To develop and evaluate a wearable silent text input system using Electromyography (EMG) and piezoelectric lead zirconate titanate (PZT) sensors.
  • To compare the performance of various machine learning models for classifying silent speech signals.
  • To assess the system's accuracy and real-time capabilities for practical applications.

Main Methods:

  • Integration of miniaturized EMG and PZT sensors into a chin-attachable wearable device.
  • Acquisition of sensor data corresponding to silent articulation of English alphabet letters.
  • Analysis of time and frequency domain features from sensor signals.
  • Comparison of feature-based and non-feature-based machine learning models for classification.

Main Results:

  • Non-feature-based machine learning models, specifically Fea-Shot Learning, demonstrated superior performance.
  • The fused EMG and PZT signal approach achieved the highest accuracy (95.63%) and F1-score (95.62%).
  • The system effectively captured subtle variations in muscle activity and skin vibrations associated with silent speech.

Conclusions:

  • The developed wearable system provides an accurate and efficient method for silent text input.
  • The combination of EMG and PZT sensors with advanced ML models shows significant potential for assistive communication.
  • This technology offers a discreet and non-audible alternative for text entry in various environments.