ContextAct@A4H is a rich, real-life daily living dataset collected in the Amiqual4Home smart apartment.
It contains data from sensors in the appartement, collected while a person was living there during two periods in June and November (summer and fall respectively).
The experiment for collecting ContextAct@A4H was performed in the frame of a collaboration between LIG, Amiqual4Home and Universidad de Los Andes (Colombia).
One of the main contributions of the dataset is the inclusion of context variables (weather, temperature, noise, humidity, presence of visitors, etc) and the high number of properties measured in the apartment.
The dataset was presented in the CONTEXT Conference [ ] . It’s first use is to perform context aware routine learning in the frame of Ambient Assisted Living research. We envision many other uses for this dataset as the following:
* To test different sensor configurations and compare which gives best classification results for activity recognition or to test which sensors suit best your application use case, finding a trade-off between cost and accuracy.
* To evaluate reliability in sensor networks since some properties have been measured using different sensors.
* For energy consumption analysis and prediction using energy related variables.
* For evaluating context-aware services. For example, a context-aware music recommendation using music information available in the dataset.
* For routine learning and analysis since the dataset is a real-life routine.
The link for the download is the following:
Please contact Paula Lago for any questions
Paula Lago email@example.com