Stress-Level Detection Using RepVGG Neural Network

Stress used to impact our mental and physical well-being,productivity,and overall quality of life. Detecting stress accurately is vital for timely intervention and effective management. In this study, we introduce a new method for detecting stress levels using the RepVGG deep learning architecture. RepVGG stands out for its efficient performance and straightforward structure, making it ideal for analyzing physiological signals and other stress indicators. We using standard metrics to calculate the things like accuracy, precision, recall and etc. Ourfindings reveal that the RepVGG-based method excels in detecting stress levels, surpassing many traditional methods and other deep learning models. Moreover, the model shows strong generalization capabilities across various datasets and conditions. This research underscores the potential of advanced deep learning models like RepVGG in stress detection, opening doors for real-time, scalable, and precise stress monitoring systems. Looking ahead, weaim to integrate this model into wearable devices andmobile apps, enabling continuous stress monitoring and offering personalized stress management advice. Some approach utilizes a rich dataset consists of signals such (HRV) and (GSR), along with other relevant biomarkers. To ensure the model’s robustness, we preprocess and augment this data. We then train the RepVGG architecture on this dataset, harnessing its Neural layers for feature extraction and its unique re-parameterizable design for efficient use.