Machine learning and deep learning techniques significantly enhance the accuracy of stress detection using various physiological signals, including ECG and PPG.
Can machine learning and deep learning techniques accurately detect mental stress using physiological signals?
Machine learning and deep learning algorithms integrated with physiological signals and wearable or contactless technologies offer promising accuracy for detecting mental stress.
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ABSTRACT This paper presents a comprehensive review on the various techniques and methodologies employed to detect stress among individuals. The review encompasses a broad spectrum of methods, including physiological measurements, wearable technology, machine learning and deep learning algorithms, and contactless image‐based techniques. The paper outlines the physiological markers commonly associated with stress, such as Electrocardiogram (ECG), Electroencephalography (EEG), Photoplethysmography (PPG), and Skin Galvanic response. It examines the various wearable and contactless techniques to acquire data. Furthermore, it explores the integration of machine learning and deep learning techniques for the development of predictive stress detection models, highlighting their accuracy. It also addresses the potential of multispectral and hyperspectral imaging in this area. Some of the publicly available datasets are also discussed in this paper. This article is categorized under: Application Areas > Health Care Technologies > Machine Learning
Khandelwal et al. (Mon,) reported a other. Machine learning and deep learning techniques significantly enhance the accuracy of stress detection using various physiological signals, including ECG and PPG.
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