1. User Identification From Walking Activity: The dataset collects data from an Android smartphone positioned in the chest pocket from 22 participants walking in the wild over a predefined path.
2. Libras Movement: The data set contains 15 classes of 24 instances each. Each class references to a hand movement type in LIBRAS (Portuguese
name 'LÍngua BRAsileira de Sinais', oficial brazilian signal language).
3. Human Activity Recognition from Continuous Ambient Sensor Data: This dataset represents ambient data collected in homes with volunteer residents. Data are collected continuously while residents perform their normal routines.
4. Hill-Valley: Each record represents 100 points on a two-dimensional graph. When plotted in order (from 1 through 100) as the Y co-ordinate, the points will create either a Hill (a “bump” in the terrain) or a Valley (a “dip” in the terrain).
5. Gesture Phase Segmentation: The dataset is composed by features extracted from 7 videos with people gesticulating, aiming at studying Gesture Phase Segmentation. It contains 50 attributes divided into two files for each video.
6. Bach Choral Harmony: The data set is composed of 60 chorales (5665 events) by J.S. Bach (1675-1750).
Each event of each chorale is labelled using 1 among 101 chord labels and described
through 14 features.
7. Activity Recognition from Single Chest-Mounted Accelerometer: The dataset collects data from a wearable accelerometer mounted on the chest. The dataset is intended for Activity Recognition research purposes.