The SWELL Knowledge Work Dataset for Stress and User Modeling Research
Date of Archiving2015
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CLST - Centre for Language and Speech Technology
Behavioural and educational sciences
Key wordswell-being at work; computer work; stressors; working behavior; affect; multimodal dataset; sensor data; preprocessed data; work psychology; user modeling; context aware systems; machine learning
This is the multimodal SWELL knowledge work (SWELL-KW) dataset for research on stress and user modeling. The dataset was collected in an experiment, in which 25 people performed typical knowledge work (writing reports, making presentations, reading e-mail, searching for information). We manipulated their working conditions with the stressors: email interruptions and time pressure. A varied set of data was recorded: computer logging, facial expression from camera recordings, body postures from a Kinect 3D sensor and heart rate (variability) and skin conductance from body sensors. Our dataset not only contains raw data, but also preprocessed data and extracted features. The participants' subjective experience on task load, mental effort, emotion and perceived stress was assessed with validated questionnaires as a ground truth. The resulting dataset on working behavior and affect is suitable for several research fields, such as work psychology, user modeling and context aware systems. The collection of this dataset was supported by the Dutch national program COMMIT (project P7 SWELL). SWELL stands for Smart Reasoning Systems for Well-being at Work and at Home.