1. Indoor User Movement Prediction from RSS data: This dataset contains temporal data from a Wireless Sensor Network deployed in real-world office environments. The task is intended as real-life benchmark in the area of Ambient Assisted Living.
2. Activity Recognition system based on Multisensor data fusion (AReM): This dataset contains temporal data from a Wireless Sensor Network worn by an actor performing the activities: bending, cycling, lying down, sitting, standing, walking.
3. 3W dataset: The first realistic and public dataset with rare undesirable real events in oil wells.
4. Occupancy Detection : Experimental data used for binary classification (room occupancy) from Temperature,Humidity,Light and CO2. Ground-truth occupancy was obtained from time stamped pictures that were taken every minute.
5. WISDM Smartphone and Smartwatch Activity and Biometrics Dataset : Contains accelerometer and gyroscope time-series sensor data collected from a smartphone and smartwatch as 51 test subjects perform 18 activities for 3 minutes each.
6. Parking Birmingham: Data collected from car parks in Birmingham that are operated by NCP from
Birmingham City Council. UK Open Government Licence (OGL).
7. BLE RSSI dataset for Indoor localization: This dataset contains RSSIs obtained on smartphones(Sony Xperia XA1). Signals were transmitted from BLE product called iTAG. Location column denotes the position of iTAG in building's entry.
8. selfBACK: The SELFBACK dataset is a Human Activity Recognition Dataset of 9
activity classes recorded with two tri-axial accelerometers.
9. Character Trajectories: Multiple, labelled samples of pen tip trajectories recorded whilst writing individual characters. All samples are from the same writer, for the purposes of primitive extraction. Only characters with a single pen-down segment were considered.