Stress Recognition Using Wearable Sensors and Mobile Phones

By Akane Sano, Rosalind W. Picard. 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, 2013

531 citations recorded by the source corpus.

Subfields: Applied Psychology, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/7208981686079d466926d15f844e6e84fafbdaf7

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In Applied Psychology

In Experimental and Cognitive Psychology

Citations in the corpus

Identifying Objective Physiological Markers and Modifiable Behaviors for Self-Reported Stress and Mental Health Status Using Wearable Sensors and Mobile Phones: Observational Study (2018, methodology): “Consistent with previous studies [7,13,14], personality types were one of the most influential and statistically significant factors for self-reported stress and mental health in this college population.”
Monitoring stress with a wrist device using context (2017, methodology): “Most of them used data from a respiration (Resp.) sensor [8,10,11], ECG sensor [8,10,11], heartrate (HR) sensor [12], acceleration (ACC) sensor [13,14], electrodermal activity (EDA) sensor [8,10,11,14,15], blood volume pulse (BVP) sensor [18] and electromyogram (EMG) sensor [10,19].”
Towards an automatic early stress recognition system for office environments based on multimodal measurements: A review (2016, methodology): “Mean, SD and median of radius and distance have also been measured [66].”
Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and healthcare (2018, background): “In addition, several machine learning techniques have been proposed automatically to detect psycho-physiological stress from bio-signals such as accelerometer and skin conductance [121], [122].”
Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis (2015, methodology): “Several recent projects have investigated the potential use of mobile technologies for monitoring stress, depression and other mental disorders (see, for example, [25, 6, 31, 24, 36, 1, 5, 39], providing new ways for supporting both patients and healthcare officers [8, 20].”
Wearable Sensors for Remote Health Monitoring (2017, background): “Besides this, wearable GSR monitoring system allows patients to monitor GSR level in their home environment to provide a better assessment of their psychophysiological condition than the evaluation made at laboratories or hospitals from short-term measurement [83,84].”
A Review on Mental Stress Detection Using Wearable Sensors and Machine Learning Techniques (2021, result): “From various machine learning classiﬁers used in previous papers, Random forest (RF) [23], [26], [38], SVM [20], [23], [26], [35]–[38], and decision trees [24], [35], [37], [38], were found to be the most effective among all due to their better results as compared to others.”