Khalil Alipour

Yasin Hasanpoor, Bahram Tarvirdizadeh, Khalil Alipour et al.

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 prevent its hurting effects on the human body and have a healthier life. Stress can be assessed using physiological signals. To this end, Photoplethysmography (PPG) is one of the most favorable physiological signals for stress assessment. This research is focused on developing a novel technique to assess stressful events using raw PPG signals recorded by Empatica E4 sensor. To achieve this goal, an adaptive convolutional neural network (CNN) combined with Multilayer Perceptron (MLP) has been utilized to realize the detection of stressful events. This research will use a dataset that is publicly available and named wearable stress and effect detection (WESAD). This dataset will be used to simulate the proposed model and to examine the advantages of the proposed developed model. The proposed model in this research will be able to distinguish between normal events and stressful events. This model will be able to detect stressful events with an accuracy of 96.7%.

Yasin Hasanpoor, Bahram Tarvirdizadeh, Khalil Alipour et al.

Our research introduces a groundbreaking approach to stress classification through Photoplethysmogram (PPG) signals. By combining Continuous Wavelet Transformation (CWT) with the proven VGG16 classifier, our method enhances stress assessment accuracy and reliability. Previous studies highlighted the importance of physiological signal analysis, yet precise stress classification remains a challenge. Our approach addresses this by incorporating robust data preprocessing with a Kalman filter and a sophisticated neural network architecture. Experimental results showcase exceptional performance, achieving a maximum training accuracy of 98% and maintaining an impressive average training accuracy of 96% across diverse stress scenarios. These results demonstrate the practicality and promise of our method in advancing stress monitoring systems and stress alarm sensors, contributing significantly to stress classification.

Yasin Hasanpoor, Amin Rostami, Bahram Tarvirdizadeh et al.

This paper introduces a robust stress detection system utilizing a Convolutional Neural Network (CNN) designed for the analysis of Photoplethysmogram (PPG) signals. Employing the WESAD dataset, we applied Continuous Wavelet Transform (CWT) to extract informative features from wrist PPG signals, demonstrating enhanced stress detection and learning compared to conventional techniques. Notably, the CNN achieved an impressive accuracy of 93.7% after five epochs, post-implementation on a resource-constrained microcontroller. The optimization process, including pruning and Post-Train Quantization, was crucial to reduce the model size to 1.6 megabytes, overcoming the microcontroller's limited resources of 2 megabytes of Flash memory and 512 kilobytes of RAM. This optimized model not only addresses resource constraints but also outperforms traditional signal processing methods, positioning it as a promising solution for real-time stress monitoring on wearable devices.

Yasin Hasanpoor, Bahram Tarvirdizadeh, Khalil Alipour et al.

This study introduces a novel method that transforms multimodal physiological signalsphotoplethysmography (PPG), galvanic skin response (GSR), and acceleration (ACC) into 2D image matrices to enhance stress detection using convolutional neural networks (CNNs). Unlike traditional approaches that process these signals separately or rely on fixed encodings, our technique fuses them into structured image representations that enable CNNs to capture temporal and cross signal dependencies more effectively. This image based transformation not only improves interpretability but also serves as a robust form of data augmentation. To further enhance generalization and model robustness, we systematically reorganize the fused signals into multiple formats, combining them in a multi stage training pipeline. This approach significantly boosts classification performance. While demonstrated here in the context of stress detection, the proposed method is broadly applicable to any domain involving multimodal physiological signals, paving the way for more accurate, personalized, and real time health monitoring through wearable technologies.