Abstract
Understanding Activities of Human Daily Life is a fundamental and essential AI problem for Ubiquitous Computing and Human-Computer Interaction. Activity inference has attracted enormous research on activity recognition from mobile sensor data. However, it is not clear how different signals can influence activity inference. To this end, we investigated the problem of activity recognition and prediction. Experiments showed that contextual signals like time, location, previous activity and related person are much more useful than demographical signals for activity recognition and prediction. We improved the accuracy of activity recognition by more than 15% comparing to existing work on the same dataset. What's more, we revealed that we can predict what will you do next with high accuracy.
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