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: https://publications.jrc.ec.europa.eu/repository/handle/JRC137672
Krasovec, Andraz; Baldini, Gianmarco; Pejovic, Veljko; Nai Fovino, Igor (2026): Multimodal Environmental Data for Behavioural Authentication in Internet of Things. European Commission, Joint Research Centre [Dataset] doi: 10.2905/JRC.WZM6WSG PID: http://data.europa.eu/89h/7be86ffd-1bac-4d3e-82fc-02b3ea40ab49
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Identifying humans based on their behavioural patterns represents an attractive basis for access control, as such patterns appear naturally, do not require focused effort from the user side, and do not impose additional burden of memorising passwords. One means of capturing behavioural patterns is through passive sensors laid out in a target environment. Thanks to the proliferation of the Internet of Things (IoT), sensing devices are already embedded in our everyday surroundings and representing a rich source of multimodal data. Nevertheless, collecting such data for authentication research purposes is challenging, as it entails management and synchronisation of a range of sensing devices, design of diverse tasks that would evoke different behaviour patterns, storage and pre-processing of data arriving from multiple sources, and the execution of long-lasting user activities. Consequently, to the best of our knowledge, no publicly available datasets suitable for behaviour-based authentication research exist. In this brief article we describe the first multimodal dataset for behavioural authentication research collected in a sensor-enabled IoT setting. The dataset comprises of high-frequency accelerometer, gyroscope, and force sensor data collected from an office-like environment. In addition, the dataset contains 3D point clouds collected with a wireless radar and electroencephalogram (EEG) readings from a wireless EEG cap worn by the study participants. Within the environment, 54 volunteers have conducted 6 different tasks that were constructed to elicit different behaviours and different cognitive load levels, resulting in a total of 16 hours of multimodal data. The richness of the dataset comprising 5 different sensing modalities, a variability of tasks including keyboard typing, hand gesturing, walking, and other activities, opens a range of opportunities for research in behaviour-based authentication, but also the understanding of the role of different tasks and cognitive load levels on human behaviour.
