Description

Activity recognition is a key component of contextaware computing to support people’s physical activity and wellness, but conventional approaches often lack in their generalizability and scalability due to problems of diversity in how individuals perform activities, overfitting when building activity models, and collection of a large amount of labeled data from end users. To address these limitations, we propose a semipopulation-based approach that exploits activity models trained from other users; therefore, a new user does not need to provide a large volume of labeled activity data. Instead of relying on any additional information from users like their weight or height, our approach directly measures the fitness of others’ models on a small amount of labeled data collected from the new user. With these shared activity models among users, we compose a hybrid model of Bayesian networks and support vector machines to accurately recognize the activity of the new user. On activity data collected from 28 people with a diversity in gender, age, weight, and height, our approach produced an average accuracy of 83.4% (kappa: 0.852), compared with individual and (standard) population models that had accuracies of 77.3% (kappa: 0.79) and 77.7% (kappa: 0.743), respectively. Through an analysis on the performance of our approach and users’ demographic information, our approach outperforms others that rely on users’ demographic information for recognizing their activities, which may contradict the commonly held belief that physically similar people would have similar activity patterns.