Improving Activity Recognition while Reducing Misclassification of Unknown Activities

Automated recognition of Activities of daily living is a significant task since it allows monitoring patients remotely using wearable devices. It could also help doctors and specialists to easily track the health status of elderly people and of patients suffering from a variety of geriatric diseases. Most of the literature regarding activity recognition does not face the problem of classifying activities of interest performed among activities that are not, i.e., unknown activities for the classifier. In this paper, we propose a novel method, based on the acceleration data recorded by wearable smartwatches, aimed at improving the activity recognition accuracy in presence of unknown activities (i.e., in real-world settings). The approach is based on an ensemble of different classifiers, a filtering procedure, and a final voting mechanism. The empirical evaluation, carried out on 8 different subjects, shows that our approach improves the overall F1 score by 5.5%, increases the recognition of unknown activities by 11.1%, and decreases the amount of data wrongly classified as an unknown activity by 15.5%. The first two observed differences are respectively statistically significant (Wilcoxon test p-value<0.01).