The Tracking Individual Performance with Sensors (TILES) is a project holding multimodal data sets for the analysis of stress, task performance, behavior, and other factors pertaining to professionals engaged in a high-stress workplace environments. Biological, environmental, and contextual data was collected from hospital nurses, staff, and medical residents both in the workplace and at home over time. Labels of human experience were collected using a variety of psychologically validated questionnaires sampled on a daily basis at different times during the day. The data sets are publicly available and we encourage researchers to use it for data mining and testing their own human behavior models.
@article{feng2024understanding,title={Understanding Stress, Burnout, and Behavioral Patterns in Medical Residents Using Large-scale Longitudinal Wearable Recordings},author={Feng, Tiantian and Narayanan, Shrikanth},journal={46th IEEE Engineering in Medicine and Biology Conference (EMBC)},year={2024},}
2023
IEEE JBHI
Scaling Representation Learning from Ubiquitous ECG with State-Space Models
@article{avramidis2023scaling,title={Scaling Representation Learning from Ubiquitous ECG with State-Space Models},author={Avramidis, Kleanthis and Kunc, Dominika and Perz, Bartosz and Adsul, Kranti and Feng, Tiantian and Kazienko, Przemys{\l}aw and Saganowski, Stanis{\l}aw and Narayanan, Shrikanth},journal={IEEE Journal of Biomedical and Health Informatics},year={2023},}