The MUG Facial Expression Database was created by the MUG group. It contains several image sequences of an adequate number of subjects for the development and the evaluation of facial expression recognition systems that use posed and induced expressions. The image sequences were captured in a laboratory environment with high resolution and no occlusions. In the first part of the database eighty six subjects performed the six basic expressions according to the ’emotion prototypes’ as defined in the Investigator’s Guide in the FACS manual. The image sequences start and stop at neutral state and follow the onset, apex, offset temporal pattern. The publicly available sequences count up to 1462. Landmark point annotation of 1222 images is available as well. In the second part of the database eighty two of the subjects were recorded while they were watching an emotion inducing video. This way several authentic expressions are recorded.
The Food Intake Cycle (FIC) was created by the MUG group towards the investigation of in-meal eating behavior. FIC contains the 6-DoF IMU sensor recordings from the 21 eating activities of 12 unique subjects in the restaurant of Aristotle University of Thessaloniki, with an average duration of 11.7 minutes. In more detail, FIC contains the 3D accelerometer and 3D gyroscope signals originating from off-the-shelf commercial smartwatches (Microsoft Band 2 and Sony Smartwatch 2). For groundtruth, we also provide the hand-labeled start and end points for each in-meal hand micromovement.
The SPLENDID chewing detection challenge dataset was created in the context of the EU funded SPLENDID project. This dataset contains approximately 60 hours of recordings from a prototype chewing detection system. The sensor signals include photoplethysmography (PPG) and processed audio from the ear-worn chewing sensor, and signals from a belt-mounted 3D accelerometer. The recording sessions include 14 participants and were conducted in the context of the EU funded SPLENDID project, at Wageningen University, The Netherlands, during the summer of 2015. The purpose of the dataset is to help develop effective algorithms for chewing detection based PPG, audio and accelerometer signals.