Nazeer Mohd, Salagrama Shailaja, Kumar Pardeep, Sharma Kanhaiya, Parashar Deepak, Qayyum Mohammed, Patil Gouri
Vidya Jyothi Institute of Technology, Hyderabad 500075, India.
Computer Information System, University of the Cumberland's, Williamsburg, KY 40769, USA.
MethodsX. 2024 Jan 23;12:102581. doi: 10.1016/j.mex.2024.102581. eCollection 2024 Jun.
Maintaining an optimal stress level is vital in our lives, yet many individuals struggle to identify the sources of their stress. As emotional stability and mental awareness become increasingly important, wearable medical technology has gained popularity in recent years. This technology enables real-time monitoring, providing medical professionals with crucial physiological data to enhance patient care. Current stress-detection methods, such as ECG, BVP, and body movement analysis, are limited by their rigidity and susceptibility to noise interference. To overcome these limitations, we introduce STRESS-CARE, a versatile stress detection sensor employing a hybrid approach. This innovative system utilizes a sweat sensor, cutting-edge context identification methods, and machine learning algorithms. STRESS-CARE processes sensor data and models environmental fluctuations using an XG Boost classifier. By combining these advanced techniques, we aim to revolutionize stress detection, offering a more adaptive and robust solution for improved stress management and overall well-being.•In the proposed method, we introduce a state-of-the-art stress detection device with Galvanic Skin Response (GSR) sweat sensors, outperforming traditional Electrocardiogram (ECG) methods while remaining non-invasive•Integrating machine learning, particularly XG-Boost algorithms, enhances detection accuracy and reliability.•This study sheds light on noise context comprehension for various wearable devices, offering crucial guidance for optimizing stress detection in multiple contexts and applications.
在我们的生活中，维持最佳压力水平至关重要，但许多人难以确定自己压力的来源。随着情绪稳定性和心理意识变得越来越重要，可穿戴医疗技术近年来越来越受欢迎。这项技术能够进行实时监测，为医疗专业人员提供关键的生理数据，以改善患者护理。当前的压力检测方法，如心电图（ECG）、血容量脉搏（BVP）和身体运动分析，受到其刚性和对噪声干扰敏感性的限制。为了克服这些限制，我们推出了STRESS-CARE，一种采用混合方法的通用压力检测传感器。这个创新系统利用了一个汗液传感器、前沿的情境识别方法和机器学习算法。STRESS-CARE使用XGBoost分类器处理传感器数据并对环境波动进行建模。通过结合这些先进技术，我们旨在彻底改变压力检测，为改善压力管理和整体幸福感提供一种更具适应性和鲁棒性的解决方案。•在所提出的方法中，我们引入了一种采用皮肤电反应（GSR）汗液传感器的先进压力检测设备，在保持非侵入性的同时优于传统心电图（ECG）方法•集成机器学习，特别是XGBoost算法，提高了检测的准确性和可靠性。•这项研究揭示了各种可穿戴设备对噪声情境的理解，为在多种情境和应用中优化压力检测提供了关键指导。
