ANALYZING THE IMPACT OF SLEEP TRAIT IN THE DETECTION AND PREDICTION OF STRESS THROUGH MACHINE LEARNING

ABSTRACT

People are thriving towards perfection, performance, and profit in the society which inturn is leading to disturbances among them both mentally and physically. One issue faced by most of the people irrespective of the age groups is "Stress". With the onset of Covid-19 pandemic, Stress has become a disastrous disorder faced by most of the people today. Most of the people are unaware that they are suffering from such a disorder. Stress lays in the hands of at-most all people either knowingly or unknowingly. There are numerous methods to detect stress manually. People don't come forward to take up treatments for stress. This disorder peeps out of humans through various symptoms like irritation, loss of appetite, agitation, depression, anxiety, reduced performance, sleep disturbance, etc. Among the afore mentioned symptoms, sleep disturbance is the major and most influential parameter in detecting and predicting stress. The SaYo Pillow is the "Smart-Yoga Pillow" which assists in concerning the relationship pertaining to sleep and stress. Although there are other methods to track sleep like Fitbit trackers to track sleeping patterns, SaYo Pillow stands out as it detects the psychological behaviors that occurs during sleep. This tracking of psychological behavior is lacking in case of other devices like Fitbit used for sleep pattern detection. The data obtained from this pillow can be used to study how stress can affect sleep. Machine Learning methods are applied to the data to detect if the person is stressed or not. Thereby adding to it, prediction is also done to understand will the person be stressed in near future. Machine Learning algorithms such as Support Vector Machine (SVM), Random Forest Classifier and Gradient Boosting Classifier was used to detect and predict stress among the individuals. The performance of these algorithms was compared to identify the best performing algorithms. After identifying the best performing algorithm, the same was applied to the data to detect and predict the occurrence of stress. In addition to that, an application was developed which suggests some activities to the candidate to overcome stress. © 2022 University of Stockholm. All rights reserved.