Physiological-Based Smart Stress Detector using Machine Learning Algorithms

This paper is focused on the development of an intelligent system to identify if one person is stress or not stress using physiological parameters through machine learning. In this study, the dataset was acquired from three hundred (300) male and female participants ages 18 to 25. The gathered dataset is composed of five (5) features (i.e. heart rate, systolic blood pressure, diastolic blood pressure, galvanic skin response and gender). An intelligent system was developed using machine learning algorithms for classification such as Linear Regression (LR), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM) using Python IDE with sci-kit learn machine learning libraries. Google Colaboratory (Colab) was utilized to perform optimization using Gridsearch to identify the best parameters of each algorithm. Feature selection methods are implemented to identify the most significant features related to stress condition of one person. After optimization, the results showed that SVM has the best performance to classify if one person is stress or not stress with optimized training-testing accuracy score of 95.00% - 96.67%.

[1]  Rubita Sudirman,et al.  GSM and GPS Based Real-Time Remote Physiological Signals Monitoring and Stress Levels Classification , 2018, 2018 2nd International Conference on BioSignal Analysis, Processing and Systems (ICBAPS).

[2]  Akane Sano,et al.  Stress Recognition Using Wearable Sensors and Mobile Phones , 2013, 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction.

[3]  Diana Civitello,et al.  How do physiological responses such as respiratory frequency, heart rate, and galvanic skin response (GSR) change under emotional stress? , 2014 .

[4]  Adrian Emiell U. Berbano,et al.  Classification of stress into emotional, mental, physical and no stress using electroencephalogram signal analysis , 2017, 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA).

[5]  Lenka Lhotska,et al.  Application of Neural Network for Stress Classification , 2000 .

[6]  Ronnie S. Concepcion,et al.  Application of Hybrid Soft Computing for Classification of Reinforced Concrete Bridge Structural Health Based on Thermal-Vibration Intelligent System Parameters , 2019, 2019 IEEE 15th International Colloquium on Signal Processing & Its Applications (CSPA).

[7]  Soniya F. Lakudzode,et al.  Review on human stress monitoring system using wearable sensors , 2016 .

[8]  Wanhui Wen,et al.  Recognition of Real-Scene Stress in Examination with Heart Rate Features , 2017, 2017 9th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC).

[9]  Michael Lawo,et al.  Developing a System for Recognizing the Emotional States Using Physiological Devices , 2016, 2016 12th International Conference on Intelligent Environments (IE).

[10]  Sylvie Charbonnier,et al.  A Multi-User Multi-Task Model For Stress Monitoring From Wearable Sensors , 2018, 2018 21st International Conference on Information Fusion (FUSION).

[11]  Yong Deng,et al.  Evaluating feature selection for stress identification , 2012, 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI).

[12]  Lejla Gurbeta,et al.  Classification of stress recognition using Artificial Neural Network , 2016, 2016 5th Mediterranean Conference on Embedded Computing (MECO).

[13]  Alvin B. Culaba,et al.  Identification of philippine herbal medicine plant leaf using artificial neural network , 2017, 2017IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM).

[14]  Javad Frounchi,et al.  Machine learning-based signal processing using physiological signals for stress detection , 2015, 2015 22nd Iranian Conference on Biomedical Engineering (ICBME).

[15]  Filippo Cavallo,et al.  Evaluation of an Integrated System of Wearable Physiological Sensors for Stress Monitoring in Working Environments by Using Biological Markers , 2018, IEEE Transactions on Biomedical Engineering.

[16]  Argel A. Bandala,et al.  Quality assessment of lettuce using artificial neural network , 2017, 2017IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM).

[17]  Mustafa Spahic,et al.  Recognition of stress levels among students with wearable sensors , 2019, 2019 18th International Symposium INFOTEH-JAHORINA (INFOTEH).

[18]  G. R. Suresh,et al.  Hybrid SVM classification technique to detect mental stress in human beings using ECG signals , 2013, 2013 International Conference on Advanced Computing and Communication Systems.