A System to Detect Mental Stress Using Machine Learning and Mobile Development
Publication Date
11-7-2018
Document Type
Conference Proceeding
Publication Title
Proceedings of 2018 International Conference on Machine Learning and Cybernetics
DOI
10.1109/ICMLC.2018.8527004
First Page
161
Last Page
166
Abstract
Hans Selye coined, in 1936, the term 'Stress' and definedit as 'the non-specific response of the body to any demand for change.' Stress was generallyconsidered as being synonymous with distress. English language dictionaries (Oxford Merriam-Webster) defined it as 'physical, mental, or emotional strain or tension when a person perceives that demands exceed the personal and social resources the individual is able to mobilize.' Stress affects over 100 million Americans and is a driver of many chronic diseases. According to American Psychological Association (APA) 2012 study, 'Stress is costing organizations a Fortune' and some cases as much as 300 billion a year. The challenges, importantly, for the individuals and the organizations are lack of proactive detection of the stress and inept preventive actions to manage mental health to circumvent adverse effects of the stress. This research paper addresses the challenge by developing and deploying machine learning enabled data driven Electroencephalogram biosensor integrated mobile application that proactively gleans User's stressful episodes, infuses collaborative intelligence derived from de-identified yet User relevant demographical, physiological, lifestyle and behavioral datasets and preventive healthcare insights to counter otherwise the long term negative effects of the stresson Users health. The paper presents prototyping solution as well as its application and certain experimental results.1Hans Selye was a pioneering Hungarian-Canadian endocrinologist. He conducted much important scientific work on the hypothetical nonspecific response of an organism to stressors-https://www.stress.org/what-is-stress/
Keywords
Bluetooth, Clinical quality metrics(CQM), Electroencephalography (EEG) biosensor, Healthcare, K-Nearest neighbor, Machine learning, Mental stress, MindWave mobile headset, Mobile development, Montreal imaging stress task, Support vector machine
Department
Computer Engineering
Recommended Citation
Chandrasekar Vuppalapati, Mohamad S. Khan, Nisha Raghu, Priyanka Veluru, and Suma Khursheed. "A System to Detect Mental Stress Using Machine Learning and Mobile Development" Proceedings of 2018 International Conference on Machine Learning and Cybernetics (2018): 161-166. https://doi.org/10.1109/ICMLC.2018.8527004
