DAGHAR: A Benchmark for Domain Adaptation and Generalization in Smartphone-Based Human Activity Recognition

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. 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. 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.