Discriminating Stress From Cognitive Load Using a Wearable EDA Device
By Cornelia Setz, Bert Arnrich, Johannes Schumm, Roberto La Marca, Gerhard Tröster, Ulrike Ehlert. IEEE Transactions on Information Technology in Biomedicine, 2010
719 citations recorded by the source corpus.
Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology
View Paper: https://www.semanticscholar.org/paper/f1d6e10b81f163c2902db11f9ee26b6f500fd2e8
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cognitive Neuroscience
- Kristof Van Laerhoven
- Rosalind W. Picard
- Philip Schmidt
- Attila Reiss
- Sumit Majumder
- Cristina Conati
- Juha Reunanen
In Experimental and Cognitive Psychology
- Manolis Tsiknakis
- Giorgos Giannakakis
- Dimitris Grigoriadis
- Katerina Giannakaki
- Olympia Simantiraki
- Alexandros Roniotis
- Kristof Van Laerhoven
- Rosalind W. Picard
Citations in the corpus
- 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].”
- Wearable Sensors for Remote Health Monitoring (2017, methodology): “[90] Distinguishing stress from cognitive load in an office environment by EDA Dry Ag/AgCl electrodes left index and middle fingers Bluetooth 41 mm × 67 mm 16 Hz Power consumption: 182 mW Investigated 6 classifiers to discriminate cognitive load from stress Accuracy 82.”
- 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): “Additionally, Hernandez et al discriminated stressful and nonstressful calls at a call center environment using SC features with 78% accuracy [55], and Setz et al automatically classified SC responses from cognitive load and stress with accuracy higher than 80% [56].”
- Stress Recognition Using Wearable Sensors and Mobile Phones (2013, background): “The correlation analysis showed that the higherreported stress level was related to activity level, SMS and screen on/off patterns.”
- Activity-Aware Mental Stress Detection Using Physiological Sensors (2010, methodology): “Setz et al. demonstrated the discriminative power of GSR in distinguishing stress caused by a cognitive load and psychosocial stress by using a wearable GSR device in an office environment [25].”
- Review on Psychological Stress Detection Using Biosignals (2019, methodology): “It is a measure directly related to the arousal [178] as well as the fact that is influenced exclusively by the sympathetic and not by the parasympathetic nervous system as other physiological measures which lead to the usage of SCR as a reliable indicator of stress [7, 179].”