Learning temporal context for activity recognition

We investigate how incremental learning of long-term human activity patterns improves the accuracy of activity classification over time. Rather than trying to improve the classification methods themselves, we assume that they can take into account prior probabilities of activities occurring at a particular time. We use the classification results to build temporal models that can provide these priors to the classifiers. As our system gradually learns about typical patterns of human activities, the accuracy of activity classification improves, which results in even more accurate priors. Two datasets collected over several months containing hand-annotated activity in residential and office environments were chosen to evaluate the approach. Several types of temporal models were evaluated for each of these datasets. The results indicate that incremental learning of daily routines leads to a significant improvement in activity classification