Sleep-EDF Database Expanded Introduction
The sleep-edf database contains 197 whole-night PolySomnoGraphic sleep recordings, containing EEG, EOG, chin EMG, and event markers. Some records also contain respiration and body temperature. Corresponding hypnograms (sleep patterns) were manually scored by well-trained technicians according to the Rechtschaffen and Kales manual, and are also available.
Link:
https://physionet.org/content/sleep-edfx/1.0.0/


The OPPORTUNITY Dataset for Human Activity Recognition from Wearable, Object, and Ambient Sensors is a dataset devised to benchmark human activity recognition algorithms (classification, automatic data segmentation, sensor fusion, feature extraction, etc).

A subset of this dataset was used for the "OPPORTUNITY Activity Recognition Challenge" organized for the 2011 IEEE conf on Systems, Man and Cybernetics Workshop on "Robust machine learning techniques for human activity recognition".

The dataset comprises the readings of motion sensors recorded while users executed typical daily activities:
* Body-worn sensors: 7 inertial measurement units, 12 3D acceleration sensors, 4 3D localization information
* Object sensors: 12 objects with 3D acceleration and 2D rate of turn
* Ambient sensors: 13 switches and 8 3D acceleration sensors
* Recordings: 4 users, 6 runs per users. Of these, 5 are Activity of Daily Living runs characterized by a natural execution of daily activities. The 6th run is a "drill" run, where users execute a scripted sequence of activities.
* Annotations/classes: the activities of the user in the scenario are annotated on different levels: "modes of locomotion" classes; low-level actions relating 13 actions to 23 objects; 17 mid-level gesture classes; and 5 high-level activity classes
Link: https://archive.ics.uci.edu/ml/datasets/opportunity+activity+recognition