![]() ![]() The cstress model compensated for the unpredictable lag that occurred between the stressor and its logging in EMA self-report. The collected EMA self-report acted as the reference value for stress validation. To collect the data in this study, the participants were asked to fill out an Ecological Momentary Assessment (EMA) questionnaire 15 times a day, at random hours. ( 7) used wearable devices and proposed a data-driven stress assessment model, called the cstress model. To develop an acceptable standard for continuous stress monitoring, Hovsepian et al. The time-bound nature of these questionnaire-based assessments unveils a major problem for the validation of new stress monitoring systems as there is no precise recording of which task or activity caused the participants' stress. These questionnaires are limited to capturing stress at a particular time and do not allow continuous as well as real-time stress monitoring ( 6). The use of psychological assessment questionnaires, filled out on different instances in a day, is the most common technique to determine human stress. The recent development of wearable sensor technology has made it easier to collect different physiological parameters of stress in daily-life. Furthermore, both these aspects are triggered by multiple factors and are difficult to capture ( 5). The concept of detecting stress is quite complex, as stress has physiological as well as psychological aspects to it. Thus, it is of utmost importance to develop robust techniques that can detect and monitor stress continuously, in real-time. Particularly, chronic stress leads to a weakened immune system, substance addiction, diabetes, cancer, stroke, and cardiovascular disease ( 4). Stress deteriorates the physical and mental well-being of a human. There has been a notable increase in depression, anxiety, stress and other stress-related diseases, worldwide ( 1– 3). The analysis and results of this comparative study demonstrate the potential of unsupervised learning for the development of non-invasive, continuous, and robust detection and monitoring of physiological and pathological stress. The classification results of unsupervised machine learning classifiers are found comparable to supervised machine learning classifiers on two publicly available datasets. Traditional supervised machine learning (linear, ensembles, trees, and neighboring models) classifiers require hand-crafted features and labels while on the other hand, the unsupervised classifier does not require any labels of perceived stress levels and performs classification based on clustering algorithms. This paper explores the potential feasibility of unsupervised learning clustering classifiers such as Affinity Propagation, Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH), K-mean, Mini-Batch K-mean, Mean Shift, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Ordering Points To Identify the Clustering Structure (OPTICS) for implementation in stress monitoring wearable devices. Moreover, self-reporting is subjective and prone to inaccuracies. These questionnaires only capture stress levels at a specific point in time. Commonly, different types of self-reporting questionnaires are used to label the perceived stress instances. One of the most challenging tasks in physiological or pathological stress monitoring is the labeling of the physiological signals collected during an experiment. These systems rely on the collection of sensor and reference data during the development phase. Most of the wearable stress monitoring systems are built on a supervised learning classification algorithm. Over the past decade, there has been a significant development in wearable health technologies for diagnosis and monitoring, including application to stress monitoring. 3Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, United Kingdom.2Electrical and Electronics Engineering, National University of Ireland Galway, Galway, Ireland.1Smart Sensors Lab, Lambe Institute of Translational Research, National University of Ireland Galway, Galway, Ireland.Talha Iqbal 1 * Adnan Elahi 2 William Wijns 1 Atif Shahzad 1,3 ![]()
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