development and functionality of the Human-Stress-Detection-System, detailing its architecture, data processing methodologies, and the effectiveness of its AI-driven features. By combining machine learning with real-time interaction, the system aims to provide a comprehensive solution for stress management,
A Transformer Architecture for Stress Detection from ECG
Sep 21, 2021 · This paper presents a deep neural network based on convolutional layers and a transformer mechanism to detect stress using ECG signals. We perform leave-one-subject-out experiments on two publicly available datasets, WESAD and SWELL-KW, to evaluate our method.
Abstract: This project presents a novel stress detection system that leverages machine learning and sensor technology to monitor and assess stress levels in real-time. As stress has become a widespread concern affecting mental health, our system integrates wearable sensors—such as heart rate monitors, galvanic skin
Automating the detection of stress levels using physiological signals provides a more accurate and objective approach to minimizing health risks and promoting the welfare of society. The detection of stress levels is a significant social contribution that can enhance people's lifestyles.
Deep learning-based stress detection for daily life use using …
Five convolutional neural network (CNN) architectures and one Vision Transformer model, including a multiple-column structure combining EEG and GSR features, showed heightened predictive capabilities and an enhanced area under the receiver operating characteristic curve (AUROC) in stress prediction compared to single-column models.
Electrocardiogram (ECG) has been widely used for emotion recog-nition. This paper presents a deep neural network based on convo-lutional layers and a transformer mechanism to detect stress using ECG signals. We perform leave-one-subject-out experiments on two publicly available datasets, WESAD and SWELL-KW, to evalu-ate our method.
Human Stress Detection With Wearable Sensors Using Convolutional Neural ...
Jan 1, 2022 · In this article, we propose and evaluate a deep learning architecture based on convolutional neural networks for human stress detection using wearable sensors. This architecture has a first part that includes three convolutional layers for learning features from several biosignals.
stress detection. Maria Vituperate al. present a method for anticipating mental stress that relies only on GSR as a physiological sensor and uses a standalone stress detecting device.
Machine and Deep Learning Models for Stress Detection Using …
Jan 3, 2025 · Stress can disrupt daily activities and harm health if prolonged or severe. Early detection of mental stress, indicated by changes in bio-signals like thermal, electrical, and acoustic signals, can prevent related health issues. This study employs machine learning and deep learning techniques on multimodal dataset from wearable sensors, focusing on processed metrics for the three-axis ...
Real-time mental stress detection using multimodality …
In this paper, a real-time deep learning framework is proposed to fuse ECG, voice, and facial expressions for acute stress detection.