5 papers
Multimodal signal fusion for stress detection using deep neural networks: a novel approach for converting 1D signals to unified 2D images
Yasin Hasanpoor, Bahram Tarvirdizadeh, Khalil Alipour +1
This study introduces a novel method that transforms multimodal physiological signalsphotoplethysmography (PPG), galvanic skin response (GSR), and acceleration (ACC) into 2D image…
Continuous Wavelet Transformation and VGG16 Deep Neural Network for Stress Classification in PPG Signals
Yasin Hasanpoor, Bahram Tarvirdizadeh, Khalil Alipour +1
Our research introduces a groundbreaking approach to stress classification through Photoplethysmogram (PPG) signals. By combining Continuous Wavelet Transformation (CWT) with the p…
Stress Assessment with Convolutional Neural Network Using PPG Signals
Yasin Hasanpoor, Bahram Tarvirdizadeh, Khalil Alipour +1
Stress is one of the main issues of nowadays lifestyle. If it becomes chronic it can have adverse effects on the human body. Thus, the early detection of stress is crucial to preve…
Real-Time Stress Detection via Photoplethysmogram Signals: Implementation of a Combined Continuous Wavelet Transform and Convolutional Neural Network on Resource-Constrained Microcontrollers
Yasin Hasanpoor, Amin Rostami, Bahram Tarvirdizadeh +2
This paper introduces a robust stress detection system utilizing a Convolutional Neural Network (CNN) designed for the analysis of Photoplethysmogram (PPG) signals. Employing the W…
Stress Detection Using PPG Signal and Combined Deep CNN-MLP Network
Yasin Hasanpoor, Koorosh Motaman, Bahram Tarvirdizadeh +2
Stress has become a fact in people's lives. It has a significant effect on the function of body systems and many key systems of the body including respiratory, cardiovascular, and…