Human Activity Recognition (HAR) with mobile and wearable devices has been deeply studied in the last decades. Research groups working on this topic evaluated their proposed methods mostly on public datasets. However, most of the existing datasets only include inertial sensor data, while it is well-known that additional context data (e.g., semantic location) has the potential to significantly improve the recognition rate.
Only a few datasets for context-aware HAR are publicly available, and their annotations were mostly self-reported in the wild by the subjects involved in data acquisition. This method harms the quality of annotations, thus discouraging the application of supervised models.
DOMINO is a public dataset for context-aware HAR that includes 25 users (wearing a smartphone and a smartwatch) performing 14 activities. During data acquisition, the mobile devices recorded both inertial and high-level context data while our team monitored the quality of the self-reported annotations.
Please cite this article if you use DOMINO in your research:
Arrotta, L., Civitarese, G., Presotto, R, & Bettini, C. (2023). DOMINO: A Dataset for Context-Aware Human Activity Recognition using Mobile Devices. In 2023 24th IEEE International Conference on Mobile Data Management (MDM). IEEE.
Inertial and Contextual Data
DOMINO contains about nine hours of data collected by monitoring 25 different subjects.
DOMINO includes a combination of inertial sensor data from smartphones and smartwatches and contextual information about the surroundings of each subject.
Among contextual data, we include environment type (indoors/outdoors), semantic place (e.g., at home, at the workplace), speed, weather conditions, nearby public transportation routes, height variation, and the environment's audio level.