DOMINO: A Dataset for Context-Aware Human Activity Recognition using Mobile Devices

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. In this paper, we propose DOMINO, a new public dataset for context-aware HAR. DOMINO 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. Our experiments on DOMINO show the positive impact of considering high-level context information for Human Activity Recognition.