Study on Human Activity Recognition Using Semi-Supervised Active Transfer Learning

In recent years, various studies have begun to use deep learning models to conduct research in the field of human activity recognition (HAR). However, there has been a severe lag in the absolute development of such models since training deep learning models require a lot of labeled data. In fields such as HAR, it is difficult to collect data and there are high costs and efforts involved in manual labeling. The existing methods rely heavily on manual data collection and proper labeling of the data, which is done by human administrators. This often results in the data gathering process often being slow and prone to human-biased labeling. To address these problems, we proposed a new solution for the existing data gathering methods by reducing the labeling tasks conducted on new data based by using the data learned through the semi-supervised active transfer learning method. This method achieved 95.9% performance while also reducing labeling compared to the random sampling or active transfer learning methods.

Automated summary

In recent years, deep learning models have been used for research in human activity recognition, but the lack of labeled data has led to slow development. Existing methods heavily rely on manual data collection and labeling, resulting in slow and biased processes. By proposing a solution using semi-supervised active transfer learning to reduce labeling tasks, performance was improved while reducing the amount of labeling required.