Creation and evaluation of in-home activities simulation data for evaluation of transfer learning models

Abstract

In recent years, research on in-home behavior recognition has been active against the backdrop of the spread of smart homes. One of the challenges in this field is the time-consuming process of collecting sensor data installed in the home for a certain period of time and annotating the data in order to construct a model for in-home behavior recognition. In response to this, methods to reduce the cost of sensor data collection and annotation by transfer learning have attracted attention. However, a lack of datasets for experiments is an issue when considering the adaptation of transfer learning to in-home behavior recognition. A common method to create a dataset is to measure real data, but this is expensive to prepare a residential facility for the experiment. In addition, it is difficult to create data sets under various conditions because subjects are held for a long period of time. The purpose of this study is to facilitate the creation of datasets by simulating in-home behavior and generating simulated datasets. Using the generated dataset, we will conduct experiments on the application of transfer learning to in-home behavior recognition.