Monitoring stress with a wrist device using context
By Martin Gjoreski, Mitja Lustrek, Matjaz Gams, Hristijan Gjoreski. Journal of Biomedical Informatics, 2017
330 citations recorded by the source corpus.
Subfields: Biomedical Engineering, Cognitive Neuroscience, Experimental and Cognitive Psychology
View Paper: https://www.semanticscholar.org/paper/507a1f32f92ef78c3e87e1864de16defc03ba874
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biomedical Engineering
- Kristof Van Laerhoven
- Philip Schmidt
- Cem Ersoy
- Deniz Ekiz
- Niaz Chalabianloo
- Attila Reiss
- Yekta Said Can
- Sanchita Paul
In Cognitive Neuroscience
In Experimental and Cognitive Psychology
- Kristof Van Laerhoven
- Cem Ersoy
- Deniz Ekiz
- Philip Schmidt
- Niaz Chalabianloo
- Attila Reiss
- Yekta Said Can
- Sanchita Paul
Citations in the corpus
- Deep PPG: Large-Scale Heart Rate Estimation with Convolutional Neural Networks (2019, background): “For example, Gjoreski et al. [37] collected real-life data with the E4 to detect stress in unconstrained environments, or Di Lascio et al. [38] collected data during lectures to discriminate engaged from non-engaged students.”
- Continuous Stress Detection Using Wearable Sensors in Real Life: Algorithmic Programming Contest Case Study (2019, background): “Gjoreski et al. found that the aggregation window lengths between 10 min and 17.5 min have better accuracy in general [34], which is similar to our results.”
- 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].”