Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Exercise Stress Test

Exercise Stress Test

Introduction
Exercise stress testing, commonly known as a treadmill test, is a noninvasive procedure used to evaluate cardiovascular function and diagnose heart conditions.
Definition
An exercise stress test measures the heart's response to exertion using a treadmill or stationary bicycle. Chest electrodes record the heart's electrical activity through an ECG, and blood pressure is monitored regularly.
Purposes

You might also read

Related Articles

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

Sort by

Same author

Proportional Control with Pole-Placement-Tuned Gains for GPS-Based Waypoint Following, Experimentally Validated Against Classical Methods.

Sensors (Basel, Switzerland)·2026

Same author

Pathological Insights: Enhanced Vision Transformers for the Early Detection of Colorectal Cancer.

Cancers·2024

Same author

Vision-Transformer-Based Transfer Learning for Mammogram Classification.

Diagnostics (Basel, Switzerland)·2023

Same author

Electroencephalogram-Based Approaches for Driver Drowsiness Detection and Management: A Review.

Sensors (Basel, Switzerland)·2022

Same author

Smart Wearables with Sensor Fusion for Fall Detection in Firefighting.

Sensors (Basel, Switzerland)·2021

Same author

Affective Computing on Machine Learning-Based Emotion Recognition Using a Self-Made EEG Device.

Sensors (Basel, Switzerland)·2021

Same journal

A Coumarin-Based Probe for Sequential ON-OFF-ON Detection of Cu<sup>2+</sup> and Biothiols: Naked-Eye Detection, Smartphone RGB Readout and In Vivo Imaging.

Biosensors·2026

Same journal

Electropolymerized Molecularly Imprinted Polymers Supported on Carbon-Based Materials for (Bio)sensing: Direct and Indirect Detection Strategies.

Biosensors·2026

Same journal

Progress in (Photo)electrochemical Biosensors for the Detection of Amyloid-Beta Oligomer.

Biosensors·2026

Same journal

Design and Simulation of Lamotrigine Intermittent Release from a Subcutaneous Implant with an Enzymatic Biosensor Based on Clinical Data.

Biosensors·2026

Same journal

Prediction of Chronic Kidney Disease Based on Simulated Serum Analysis by Vibrational Spectroscopy.

Biosensors·2026

Same journal

AI/ML-Assisted SERS Biosensing for Biomolecular Detection: From Direct Spectral Response to Integrated Diagnostic Systems.

Biosensors·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Aug 5, 2025

A Community-based Stress Management Program: Using Wearable Devices to Assess Whole Body Physiological Responses in Non-laboratory Settings

A Community-based Stress Management Program: Using Wearable Devices to Assess Whole Body Physiological Responses in Non-laboratory Settings

Published on: January 22, 2018

Mental Stress Detection Using a Wearable In-Ear Plethysmography.

Hika Barki¹, Wan-Young Chung^1,2

¹Department of AI Convergence, Pukyong National University, Busan 48513, Republic of Korea.

|March 29, 2023

Summary

This summary is machine-generated.

This study developed an ear-mounted photoplethysmography (PPG) system for mental stress detection. The system achieved high accuracy, showing promise for early mental health intervention.

Keywords:

continuous wavelet transform (CWT)convolutional neural network (CNN)mental stress photoplethysmography (PPG)scalograms

More Related Videos

Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students

Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students

Published on: June 16, 2018

Psychophysiological Stress Assessment Using Biofeedback

Psychophysiological Stress Assessment Using Biofeedback

Published on: July 31, 2009

Related Experiment Videos

Last Updated: Aug 5, 2025

A Community-based Stress Management Program: Using Wearable Devices to Assess Whole Body Physiological Responses in Non-laboratory Settings

A Community-based Stress Management Program: Using Wearable Devices to Assess Whole Body Physiological Responses in Non-laboratory Settings

Published on: January 22, 2018

Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students

Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students

Published on: June 16, 2018

Psychophysiological Stress Assessment Using Biofeedback

Psychophysiological Stress Assessment Using Biofeedback

Published on: July 31, 2009

Area of Science:

Biomedical Engineering
Signal Processing
Mental Health Technology

Background:

Mental stress significantly impacts health and well-being.
Early detection of mental stress is vital for preventing associated illnesses.
Current detection methods may lack continuous, non-invasive monitoring capabilities.

Purpose of the Study:

To develop and evaluate an ear-mounted photoplethysmography (PPG) system for detecting mental stress.
To assess the system's accuracy and reliability in classifying stress levels.
To explore signal processing techniques for enhancing stress detection performance.

Main Methods:

Utilized an ear-mounted PPG system to collect physiological data from 14 participants under controlled stress-inducing conditions (Stroop test, mathematical calculations).
Preprocessed raw PPG signals and transformed them into scalograms using continuous wavelet transform (CWT).
Employed a convolutional neural network (CNN) classifier to differentiate between stressed and non-stressed states based on PPG signal features.

Main Results:

The PPG system demonstrated high classification accuracy (92.04%) and F1-score (90.8%) for mental stress detection.
Introducing white Gaussian noise to PPG signals further improved performance, achieving 96.02% accuracy and 95.24% F1-score.
The CWT-based feature extraction and CNN classification effectively identified stress-related changes in PPG signals.

Conclusions:

The proposed ear-mounted PPG system is a promising tool for reliable, non-invasive mental stress detection.
The system's high accuracy suggests potential for early intervention and improved mental health management.
Further research and development could integrate this technology into wearable devices for continuous well-being monitoring.