Description: The harAGE corpus is a novel dataset for Human Activity Recognition (HAR) collected with a customised app running on a Garmin Vivoactive 3 smartwatch. This dataset contains 17 h 37 m 20 s of data from 30 participants (14 f, 16 m), with a mean age of 40.0 (± 8.3) years old. The available data is split in 3 (train/devel/test) participant-independent and gender-balanced partitions, and contains samples of the 30 participants performing 8 different activities (lying, sitting, standing, washing hands, walking, running, stairs climbing, and cycling).
harAGE was featured in the ComParE 2022 challenge.
If you use the harAGE Corpus in your research work, you are kindly asked to cite  and  in your publications.
Access: Via Zenodo.
Description: The MuSe-CaR database is a large, multimodal (video, audio, and text) dataset which has been gathered in-the-wild with the intention of further understanding Multimodal Sentiment Analysis in-the-wild, e.g., the emotional engagement that takes place during product reviews (i.e., automobile reviews) where a sentiment is linked to a topic or entity. MuSe-CaR was featured in the MuSe Challenges of 2020 and 2021.
Access: MuSe-CaR can be shared with researchers in academia for non-commercial research purposes. In order to obtain access, please download the EULA and send it to Lukas Christ. Note that the EULA must be filled and signed by a Professor.
Description: The Passau Spontaneous Football Coach Humor (Passau-SFCH) database comprises audiovisual recordings of German football Bundesliga press conferences. It is annotated for humor displayed by the coaches as well as sentiment and direction of humorous utterances according to the Humor Style Questionnaire proposed by Martin et al. A variant of Passau-SFCH was featured in the MuSe 2022 Challenge.
Access: Passau-SFCH can be shared with researchers in academia for non-commercial research purposes only. In order to obtain access, please fill and sign the EULA and send it to Lukas Christ. Please note that the EULA must be filled and signed by a Professor. The data is available on zenodo: Passau-SFCH MuSe-Humor subchallenge
Description: Ulm-TSST is a multimodal (audio, video, text, physiological signals) dataset annotated with continuous valence and arousal signals. The subjects are recorded in the stress-inducing TSST scenario. In addition to audio, video and transcriptions, Ulm-TSST also includes biological recordings, such as Electrocardiogram (ECG), Electrodermal Activity (EDA), Respiration, and Heart Rate (BPM). Ulm-TSST was featured in the MuSe Challenges 2021 and 2022.
Access: Ulm-TSST can be shared with researchers in academia for non-commercial research purposes. In order to obtain access, please download the EULA and send it to Lukas Christ. Note that the EULA must be filled and signed by a Professor.