optical data is often the preferred choice for geospatialĪpplications, but requires clear skies and little cloud cover to work well.Ĭonversely, Synthetic Aperture Radar (SAR) sensors have the unique capability Yet, most of theĬurrent literature and open datasets only deal with electro-optical (optical)ĭata for different detection and segmentation tasks at high spatial Our primary motivation to create this dataset was to conduct a stress study under real-world work conditions.Authors: Jacob Shermeyer, Daniel Hogan, Jason Brown, Adam Van Etten, Nicholas Weir, Fabio Pacifici, Ronny Haensch, Alexei Bastidas, Scott Soenen, Todd Bacastow, Ryan Lewis Download PDF Abstract: Within the remote sensing domain, a diverse set of acquisition modalitiesĮxist, each with their own unique strengths and weaknesses. The dataset offers physiological stress signals collected using signal streams from Empatica E4 during the COVID-19 outbreak in a nursing population. MDPSD is a comprehensive multi-modal stress detection dataset on university students using EDA and PPG signals while performing different tests(e.g., TSST and color-word tests ). The SWELL dataset used Mobi, uLog, video, and Kinect to study stress and the associated postures and facial expressions of 25 students. The WESAD data-set used RespiBAN and Empatica E4 to study stress of 15 students watching a movie and taking a TSST test. The AffectiveRoad dataset used Empatica E4 and Zephyr Bioharness 3 to study the effect of driving conditions on stress of 10 drivers, for which each driver took a 1h26m driving test. Our work was inspired by previous work on wearables to monitor physiological signals related to stress. Signals, stress events, and survey responses is available upon request. A periodic smartphone-administered survey also captured theĬontributing factors for the detected stress events. Specific physiological variables that were monitored included electrodermalĪctivity, heart rate, skin temperature, and accelerometer data of the nurse

Physiological data and associated context pertaining to the stress events. In order to address these concerns, we captured both the Many social, cultural and individuals experience in dealing with stressfulĬonditions. Stress 'in the wild' in a work environment is complex due to the influence of This dataset is aĬollection of biometric data of nurses during the COVID-19 outbreak. This paper provides a unique stress detection dataset that wasĬreated in a natural working environment in a hospital. Individuals better manage health to minimize the negative impacts of long-term

Stress detection has gained increasedĪttention in recent years, especially because early stress detection can help Advances in wearable technologies provide the opportunity to continuously

Uh multi sensor dataset