Abstract
Many applications based on activity recognition techniques with wearable sensors are currently developed. However, present activity recognition techniques require tons of labeled data to learn. Moreover, the sort of output labels are restricted by learning data, and they sometimes mismatch with applications'purposes. Thus, this study proposes a method to solve these problems by integrating annotation and analysis tools of human activities employing active learningtechniques and hierarchical label definition. Active learning techniques in this method provides efficient and continual label collection. Hierarchical label definition and its dynamical changes provide flexibility of utilization of recognition results. Increased labeling effort by introduction of hierarchical label is relaxed by propagation of changed labels to higher and lower layers. ATLAYA(Annotation and analysis Tool with LAYered activity and Active learning) is implemented as the prototype of proposed method. Evaluation with ATLAYA showed that the proposed method can decrease labeling effort and effectiveness of hierarchical label definition.
