CASL: Capturing Activity Semantics Through Location Information for Enhanced Activity Recognition

Using portable tools to monitor and identify daily activities has increasingly become a focus of digital healthcare, especially for elderly care. One of the difficulties in this area is the excessive reliance on labeled activity data for corresponding recognition modeling. Labeled activity data is expensive to collect. To address this challenge, we propose an effective and robust semi-supervised active learning method, which combines the mainstream semi-supervised learning method with expert collaboration. Our method takes a user's trajectory as the only input. In addition, this novel method uses expert collaboration to judge the valuable samples further to enhance its performance. Our method relies on very few semantic activities, outperforms all baseline activity recognition methods, and is close to the performance of supervised learning methods. On the adlnormal dataset with 200 semantic activities data, our work achieved an accuracy of 89.07%, and supervised learning has 91.77%. Our ablation study validated the components in our method using a query strategy and a data fusion approach.