Data · dataset · 2025
Dados de replicação para: DAGHAR: a benchmark for domain adaptation and generalization in smartphone-based human activity recognition
Listed in REDU - Unicamp Institutional Research Data Repository
DAGHAR benchmark is a curated dataset collection designed for domain adaptation and domain generalization studies in HAR tasks, using inertial sensors such as accelerometers and gyroscopes, from "A benchmark for domain adaptation and generalization in smartphone-based human activity recognition" work.
Description
It features raw inertial sensor data sourced exclusively from smartphones. Six public datasets were selected and standardized in terms of accelerometer units of measurement, sampling rate, gravity component, activity labels, user partitioning, and time window size.
This standardization process allows for creating a comprehensive benchmark for evaluating the generalization capabilities of HAR models in cross-dataset scenarios. The benchmark is based on the following datasets: Ku-HAR, from "Sikder, N. and Nahid, A.A., 2021. KU-HAR: An open dataset for heterogeneous human activity recognition.
Read the rest (7 more)
Pattern Recognition Letters, 146, pp.46-54", avaliable at Mendeley. Distributed under CC BY 4.0. MotionSense, from "Malekzadeh, M., Clegg, R.G., Cavallaro, A. and Haddadi, H., 2019, April.
Mobile sensor data anonymization. In Proceedings of the international conference on internet of things design and implementation (pp. 49-58)", available at Kaggle. Distributed under Open Data Commons Open Database License (ODbL) v1.0.
RealWorld, from "Sztyler, T. and Stuckenschmidt, H., 2016, March. On-body localization of wearable devices: An investigation of position-aware activity recognition. In 2016 IEEE international conference on pervasive computing and communications (PerCom) (pp. 1-9).
IEEE", available at this link. We obtained explicitly permission to distribute a copy of the preprocessed data from the original authors. UCI-HAR, from "Reyes-Ortiz, J.L., Oneto, L., Samà, A., Parra, X. and Anguita, D., 2016.
Transition-aware human activity recognition using smartphones. Neurocomputing, 171, pp.754-767", available at UCI Repository. Distributed under CC BY 4.0.
WISDM, from "Weiss, G.M., Yoneda, K. and Hayajneh, T., 2019. Smartphone and smartwatch-based biometrics using activities of daily living. Ieee Access, 7, pp.133190-133202", available at UCI repository.
Distributed under CC BY 4.0.
Links
Where it is published
- Dataverse dataset page redu.unicamp.br/dataset.xhtml?persistentId=doi%3A10.25824%2Fredu%2F51VZSW ↗
landing page · from redu unicamp br
- DOI doi.org/10.25824/redu/51vzsw ↗
DOI / persistent id · from redu unicamp br
Catalogue records · 1
- Dataverse API redu.unicamp.br/api/datasets/:persistentId/?persistentId=doi%3A10.25824%2Fredu… ↗
metadata API · from redu unicamp br
Topics
- Stated by source
- Computer and Information Science
- Inferred from text
- Applied computing 69%
Provenance · 1 source records, 8 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| REDU - Unicamp Institutional Research Data Repository | doi:10.25824/redu/51VZSW | 12 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| concepts[field].anzsrc:group:4601 | enrichment · redu unicamp br | taxonomy-embedding@1.1.0 | title+keywords+description (69%) |
| concepts[field].dataverse_subject:computer-and-information-science | source · redu unicamp br | connector:redu_unicamp_br@1.0.0 | /subjects |
| created_date | source · redu unicamp br | connector:redu_unicamp_br@1.0.0 | |
| description | source · redu unicamp br | connector:redu_unicamp_br@1.0.0 | /description |
| publication_date | source · redu unicamp br | connector:redu_unicamp_br@1.0.0 | |
| title | source · redu unicamp br | connector:redu_unicamp_br@1.0.0 | /name |
| updated_date | source · redu unicamp br | connector:redu_unicamp_br@1.0.0 | |
| version_label | source · redu unicamp br | connector:redu_unicamp_br@1.0.0 |