Good pervasive computing studies require laborious data labeling efforts: Our experience in activity recognition and indoor positioning studies

Abstract

Preparing and labeling sensing data are necessary when we develop state-of-the-art sensing devices or methods in our studies. Since developing and proposing new sensing devices or modalities are important in the pervasive computing and ubicomp research communities, we need to provide high quality labeled data by making use of our limited time whenever we develop a new sensing device. In this keynote talk, we first introduce our recent studies on activity recognition and indoor positioning based on machine learning. Later, we discuss important aspects of producing labeled data and share our experiences gathered during our research activities.