Multimodal Environmental Data for Behavioural Authentication in Internet of Things

This dataset comprises of multimodal data collected from Internet of Things (IoT) sensors in an office-like environment in which a total of 54 volunteers performed various office tasks. The tasks included typing, gesture-based, and movement-based tasks, where each task was modulated with various levels of difficulty. The assortment of sensors used for the data collection includes multiple inertial measurement units, multiple force sensors, a short milimetre-wave radar, and an 8-channel EEG device. These data are primarily envisioned as a basis for exploratory research in the field of user authentication, however the dataset could be applied to a plethora of different research domains, including human activity recognition, and cognitive load inference. More details on the dataset can be found at: [link to the paper when published].