Activity-Aware Mental Stress Detection Using Physiological Sensors

By Feng-Tso Sun, Cynthia Kuo, Heng-Tze Cheng, S. Buthpitiya, Patricia Collins, Martin L. Griss. Mobile Computing, Applications, and Services, 2010

432 citations recorded by the source corpus.

Continuous stress monitoring may help users better under- stand their stress patterns and provide physicians with more reliable data for interventions. Previously, studies on mental stress detection were lim- ited to a laboratory environment where participants generally rested in a sedentary position. However, it is impractical to exclude the effects of physical activity while developing a pervasive stress monitoring appli- cation for everyday use. The physiological responses caused by mental stress can be masked by variations due to physical activity.

Subfields: Biomedical Engineering, Cardiology and Cardiovascular Medicine, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/b562599cc4ddc208e90fb64d9e8e5bdd3196eb35

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In Biomedical Engineering

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): “…detection approaches are Heart Rate (HR) [17], [22]–[26], Heart Rate Variability (HRV), Skin Temperature (ST) [23]–[26], Skin Conductance (also called Galvanic Skin Response (GSR)) [17], [19]–[22], [24]–[26], [35], [37], Blood Pressure (BP) [23], [37], and Respiration Rate (RR) [18], [36], [55].”
Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013, methodology): “healthcare services are mentioned in [29,39,41] Generally, decision tree methods are limited to the space of the constructed features as the inputs”
Advanced internet of things for personalised healthcare systems: A survey (2017, background): “monitoring for intervention s[8] Inclusive of PA impact on the”
A Stress Sensor Based on Galvanic Skin Response (GSR) Controlled by ZigBee (2012, neutral): “They were chosen because there are studies that have obtained good results with them [29].”
Introducing WESAD, a Multimodal Dataset for Wearable Stress and Affect Detection (2018, methodology): “First, a 5 Hz lowpass filter was applied to the raw EDA signal, similar to related work [26, 27].”
cStress: Towards a Gold Standard for Continuous Stress Assessment in the Mobile Environment (2015, background): “In the ﬁrst case, the research efforts are focused on discovery and analysis of effective indicators of stress [49, 46, 19, 10, 51, 39].”