Understanding Compatibility-based Classifier Personalization in Activity Recognition

Personalization related research has been conducted for decades with the objective of investigating personal-level effectiveness in irregular or abnormal cases among many types of domains. However, the activity recognition community generally investigates one-fits-all model, i.e., a single recognition model for all people and suffers from poor performance generalization or a huge amount of training data for high generalization. In this paper, we propose Compatibility-based classifier personalization (CbCP) as a subject-dependent activity recognition method that uses the classifier with the highest compatibility (similarity) with a particular user. Furthermore, we explore the effectiveness of classifier personalization through a particular group of activities, which results in a hierarchical recognition model. We present a comparative evaluation of 1) the traditional one-fits-all classifier vs. CbCP models and 2) compatibility evaluation through all activities (non-hierarchical classification) vs. a group of activities (hierarchical classification). The results of the two public datasets imply the effectiveness of compatibility-based approach for a hierarchical classifier formation.