DETECTION OF MENTAL STRESS USING BIOSIGNALS THROUGH MACHINE LEARNING- A BRIEF SURVEY
Keywords:
Stress, Emotion Detection, biosignals,WearablesAbstract
This paper explores the classification of various existingbiosignals used forstressdetection and evaluates their effectiveness. Out of all the biosignals reviewed, ECG was chosenasthebestbiosignals.Furtherreviewwasconductedto determine the most suitable machine learning model for the chosen ECG signal. Results have shown that KNN gives the best accuracy of96.41% followed bySVM witha considerably high accuracy of 90.10%. The best ML models were then evaluated on other biosignals for comparison of effectiveness ondifferentdatasignals.Thefindingsofthisstudyofferuseful insights into the selection of optimal biosignals and machine learning algorithms for detecting stress, which can contribute to development of personalized stress management technologies and improving mental health.
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