Abstract
Stress is a major issue for every person. There are various machine learning methods and sensor systems that are widely used to detect the stress. Mobile phone-sensing mechanism is a cheaper technique to detect the stress, as mobile phones are easily available and every single person is using it. The work here deals with the detection of stress by measuring the physiological parameters of the human body. The results show good performance of the proposed system. Hybrid approach that involves the combination of heuristic algorithm and Bayesian classifier with the neural network used here provides a good accuracy of 92.86% with the involvement of Blood Pressure Measurement (BPM) as one physiological parameter and 85.71% with the Heart Rate (HR) as another physiological parameter of human body to detect the stress of a person.
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Kaur, P., Malhotra, S. (2019). Improved SLReduct Framework for Stress Detection Using Mobile Phone-Sensing Mechanism in Wireless Sensor Network. In: Panigrahi, C., Pujari, A., Misra, S., Pati, B., Li, KC. (eds) Progress in Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 714. Springer, Singapore. https://doi.org/10.1007/978-981-13-0224-4_45
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DOI: https://doi.org/10.1007/978-981-13-0224-4_45
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