Activity recognition using transfer learning

Abstract

The technology for human activity recognition has become an active research topic in recent years as it has many potential applications, such as surveillance systems, healthcare systems, and human-computer interaction. In the research of activity recognition, supervised machine learning approaches have been widely used for activity recognition. However, the cost of collecting labeled sensor data in new environments is high. Furthermore, these methods do not work well in a cross- domain environment using conventional machine learning approaches. In this study, we proposed a transfer learning framework based on principal component analysis (PCA) transformation, Gale- Shapley similarity measurement, and Jensen-Shannon divergence (JSD) feature mapping. Transfer learning aims to apply new information learned from the source domain to the target domain. The experimental results showed that the proposed approach performs better than the approach merely learned in the source environment.