期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2021
卷号：12
期号：7
DOI：10.14569/IJACSA.2021.0120745
语种：English
出版社：Science and Information Society (SAI)
摘要：Today's busy lifestyle often leads to frequent stress, the accumulation of which may lead to severe consequences for humans. Smartwatches are widely distributed and accessible, and as such deserve intelligent solutions that deal with the processing of such collected data and ensuring the improvement of the quality of life of end-users. The goal of this research is to create a stress detection technology that can correctly, constantly, and unobtrusively monitor psychological stress in real time. Due to the importance of stress detection and prevention, many traditional and advanced techniques have been proposed likewise we provide a unique stress-detection technique that is context-based. Due to the importance of stress detection and prevention, many traditional and advanced techniques have been proposed. In this research, a novel approach to designing and using a deep neural network for stress detection is presented. To provide a desirable training environment for network development, an open-source data set based on motion and physiological information collected from wrist and chest-worn devices was acquired and exploited. Raw data were analyzed, filtered, and preprocessed to create the best possible training data. For the proposed solution to have wide use value, further focus was placed on the data recorded using only smartwatches. Smartwatches are widely distributed and accessible, and as such deserve intelligent solutions that deals with the processing of such collected data and ensuring the improvement of the quality of life of end-users. Finally, two network types with proven capabilities of processing time series data are examined in detail: a fully convolutional network (FCN) and a ResNet deep learning model. The FCN model showed better empirical performances, and further efforts were made to select an optimal network structure. In the end, the proposed solution demonstrated performance similar to state-of-the-art solutions and significantly better than some traditional machine learning techniques, providing a good foundation for reliable stress detection and further development efforts.
