Context-guided universal hybrid decision tree for activity classification

Obtaining accurate measurements of human activities is important for a broad set of health applications. We propose a context-based hybrid decision tree classifier with a real-time portable solution for reliably classifying daily life activities and for providing instant feedback. At first, to determine user contexts, we utilize sensors typically found on smart phones or tablets to collect environment data. Then, we select different types of hybrid decision tree classifiers based on detected human context. The tree classifier can flexibly implement different decision rules at its internal nodes, and can be adapted from a population-based model when supplemented by training data for individuals. In addition, with the introduction of portable devices, the users can receive instant feedback of their current mobility status.