Enhanced Mind Stress Detection Using CNN Deep and Machine Learning Models for Multi Sensor Stress Detection

Abstract

The increased rates of stress in the modern hectic society have predisposed mental health monitoring as a vital field of study and technological development. Stress is a significant influence on the cognitive skills, emotional stability, and physical wellbeing and in most cases, may cause serious chronic illnesses in the event that it is not addressed. The traditional methods of stress detection, e.g., questionnaires and clinical analysis, are rather subjective and have no opportunity to provide real-time monitoring. To eliminate these restrictions, the intended system is called Mind Under Machine: Deep and Machine Learning Models of Multi-Sensors Stress Detection that is based on the application of multi-sensor physiological data and the modern methods of machine learning and deep learning to detect stress accurately. The system also includes other physiological cues such as the heart rate, electrodermal activity, electrobrainwave, respiration rate and skin temperature, which are measured by wearable electronics. These are signals which are indicative of the work of the autonomic nervous system in stress conditions. The framework uses both traditional machine learning algorithms and deep learning models to classify stress levels using effective preprocessing methods like noise removal and feature extraction of complex time-series data. Deep learning models, specifically Convolutional Neural Network (CNN) and Long short memory (LSTM) networks allow automatic feature extraction and improvement of time patterns. Also, multi-sensor data fusion enhances reliability and reduces the reliance on a single physiological parameter dependency. The suggested framework is both accurate and robust, and it is highly scalable, hence suitable in practice in wearable healthcare systems. The system can offer a reliable method of monitoring stress constantly and early intervention in mental health management by integrating physiological sensing with intelligent analytics.