Research on Memory-Efficient Approach to Sleep Quality Estimation for Edge Devices

Lifelog data has become easier to collect due to the development of wearable technology and IoT devices, and its importance has increased with the growth of the healthcare industry. In particular, personalized services such as personalized healthcare and stress management are possible because of the personal characteristics of lifelog data. In this study, we build a sleep quality classification system using lifelog datasets. As a preprocessing of the time series data, we propose a method for using different time periods depending on the type of question to be considered, a method for synchronizing variable lengths, and a process for normalizing the data. In addition, this paper suggests Weighted Validation Loss Propagation and Set Lower Bound methods to enhance generalization performance in the process of building and training classification deep learning models with Convolution Neural Network (CNN) and Attention-based models. Then, we discuss model significance, applications, and limitations. We also propose future research to overcome the limitations.