PCH Data 2017: A Diverse, Realistic and Annotated Dataset for Wearable Activity Recognition

This dataset contains wrist-worn triaxial accelerometer recordings collected for research into wearable Human Activity Recognition. Data were collected during the spring semester of 2017 from 141 undergraduate students at Ulster University in a controlled environment.

Each participant performed three activities from one of six activity scenarios: self-care, cardio exercise, house cleaning, weights exercise, sport and food preparation. Across the dataset, 18 activities are represented and the dataset contains 423 CSV recordings.

Accelerometer data were collected using a Shimmer 2R wearable sensor positioned on the dorsal aspect of the participant's dominant wrist. Data were recorded at a sampling rate of 51.2 Hz. Individual activities were recorded for approximately two minutes and saved as separate files.

The dataset has been anonymised for release. Original participant identifiers have been replaced with non-identifying participant codes. The CSV files contain raw and calibrated triaxial accelerometer measurements and relative sensor timestamps.

The collection methodology and analysis of this dataset are described in: Cleland, I., Donnelly, M. P., Nugent, C. D., Hallberg, J., Espinilla, M. and Garcia-Constantino, M. (2018), “Collection of a Diverse, Realistic and Annotated Dataset for Wearable Activity Recognition”, 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). DOI: 10.1109/PERCOMW.2018.8480322.

The license link for this data is https://creativecommons.org/licenses/by/4.0/. This work is licensed under CC BY 4.0, which allows users to distribute, merge, reanalyze, adapt, and build upon the data, even commercially, in any medium or format, so long as the following attribution is given to the data producer and publisher:

Cleland, I. (2026) PCH Data 2017: A Diverse, Realistic and Annotated Dataset for Wearable Activity Recognition. Ulster University. http://doi.org/10.21251/b9566edc-c68c-4eee-b67e-c0228d0d3833. CC BY 4.0.

Users should acknowledge the associated publication: Cleland, I., Donnelly, M. P., Nugent, C. D., Hallberg, J., Espinilla, M. and Garcia-Constantino, M. (2018), “Collection of a Diverse, Realistic and Annotated Dataset for Wearable Activity Recognition”, DOI: 10.1109/PERCOMW.2018.8480322.