Al-Saggaf Ubaid M, Naqvi Syed Faraz, Moinuddin Muhammad, Alfakeh Sulhi Ali, Ali Syed Saad Azhar
Electrical and Computer Engineering Department, King Abdulaziz University, Jeddah, Saudi Arabia.
Center of Excellence in Intelligent Engineering Systems (CEIES), King Abdulaziz University, Jeddah, Saudi Arabia.
Front Neurorobot. 2022 Feb 4;15:819448. doi: 10.3389/fnbot.2021.819448. eCollection 2021.
Mental stress has been identified as the root cause of various physical and psychological disorders. Therefore, it is crucial to conduct timely diagnosis and assessment considering the severe effects of mental stress. In contrast to other health-related wearable devices, wearable or portable devices for stress assessment have not been developed yet. A major requirement for the development of such a device is a time-efficient algorithm. This study investigates the performance of computer-aided approaches for mental stress assessment. Machine learning (ML) approaches are compared in terms of the time required for feature extraction and classification. After conducting tests on data for real-time experiments, it was observed that conventional ML approaches are time-consuming due to the computations required for feature extraction, whereas a deep learning (DL) approach results in a time-efficient classification due to automated unsupervised feature extraction. This study emphasizes that DL approaches can be used in wearable devices for real-time mental stress assessment.
精神压力已被确认为各种身心障碍的根本原因。因此，考虑到精神压力的严重影响，进行及时的诊断和评估至关重要。与其他与健康相关的可穿戴设备不同，用于压力评估的可穿戴或便携式设备尚未开发出来。开发此类设备的一个主要要求是一种高效的算法。本研究调查了计算机辅助方法在精神压力评估中的性能。在特征提取和分类所需时间方面对机器学习（ML）方法进行了比较。在对实时实验数据进行测试后，发现传统的ML方法由于特征提取所需的计算而耗时，而深度学习（DL）方法由于自动无监督特征提取而实现了高效分类。本研究强调，DL方法可用于可穿戴设备进行实时精神压力评估。
