Stress detection in daily life scenarios using smart phones and wearable sensors: A survey
By Yekta Said Can, Bert Arnrich, Cem Ersoy. Journal of Biomedical Informatics, 2019
416 citations recorded by the source corpus.
Stress has become a significant cause for many diseases in the modern society. Recently, smartphones, smartwatches and smart wrist-bands have become an integral part of our lives and have reached a widespread usage. This raised the question of whether we can detect and prevent stress with smartphones and wearable sensors. In this survey, we will examine the recent works on stress detection in daily life which are using smartphones and wearable devices. Although there are a number of works related to stress detection in controlled laboratory conditions, the number of studies examining stress detection in daily life is limited. We will divide and investigate the works according to used physiological modality and their targeted environment such as office, campus, car and unrestricted daily life conditions. We will also discuss promising techniques, alleviation methods and research challenges.
Subfields: Cardiology and Cardiovascular Medicine, Experimental and Cognitive Psychology
View Paper: https://www.semanticscholar.org/paper/ef2258c22414945dfe62a51cbee0cbe676750edb
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In Cardiology and Cardiovascular Medicine
In Experimental and Cognitive Psychology
Citations in the corpus
- A Review on Mental Stress Detection Using Wearable Sensors and Machine Learning Techniques (2021, background): “[14] presented a review of the recent works on stress detection in daily life using wearable devices and smartphones.”
- Continuous Stress Detection Using Wearable Sensors in Real Life: Algorithmic Programming Contest Case Study (2019, methodology): “Mean amplitude, standard deviation, minimum and maximum values, RMS, the delay between applied stimuli and response, number of peaks, peak height, rising time, recovery time, the position of maximum and minimum features were used in the literature to measure the stress levels of the user [49].”