1. Smartphone-Based Recognition of Human Activities and Postural Transitions: Activity recognition data set built from the recordings of 30 subjects performing basic activities and postural transitions while carrying a waist-mounted smartphone with embedded inertial sensors.
2. Simulated Falls and Daily Living Activities Data Set: 20 falls and 16 daily living activities were performed by 17 volunteers with 5 repetitions while wearing 6 sensors (3.060 instances) that attached to their head, chest, waist, wrist, thigh and ankle. 3. sEMG for Basic Hand movements: The sEMG for Basic Hand movements includes 2 databases of surface electromyographic signals of 6 hand movements using Delsys' EMG System. Healthy subjects conducted six daily life grasps. 4. Localization Data for Person Activity: Data contains recordings of five people performing different activities. Each person wore four sensors (tags) while performing the same scenario five times. 5. ICU: Data set prepared for the use of participants for the 1994 AAAI Spring Symposium on Artificial Intelligence in Medicine. 6. Horton General Hospital: Horton General Hospital is in the town Banbury not far from Oxford, UK. 7. Epileptic Seizure Recognition: This dataset is a pre-processed and re-structured/reshaped version of a very commonly used dataset featuring epileptic seizure detection. 8. EMG data for gestures: These are files of raw EMG data recorded by MYO Thalmic bracelet 9. EEG Steady-State Visual Evoked Potential Signals: This database consists on 30 subjects performing Brain Computer Interface for Steady State Visual Evoked Potentials (BCI-SSVEP). 10. EEG Eye State: The data set consists of 14 EEG values and a value indicating the eye state. 11. Daphnet Freezing of Gait: This dataset contains the annotated readings of 3 acceleration sensors at the hip and leg of Parkinson's disease patients that experience freezing of gait (FoG) during walking tasks.
12. Breath Metabolomics: Breath analysis is a pivotal method for biological phenotyping. In a pilot study, 100 experiments with four subjects have been performed to study the reproducibility of this technique. 13. Bar Crawl: Detecting Heavy Drinking: Accelerometer and transdermal alcohol content data from a college bar crawl. Used to predict heavy drinking episodes via mobile data. 14. Bar Crawl: Detecting Heavy Drinking: Accelerometer and transdermal alcohol content data from a college bar crawl. Used to predict heavy drinking episodes via mobile data. | 
