Machine Learning without Real-world Data

Hyunwoo Jung, Cholmin Kang, Youngki Lee

Abstract: It has been a common approach to apply Machine Learning (ML) techniques over sensory data for inferring human behavior, activities, emotions, and surrounding contexts. Especially, IMU (Inertial Measurement Unit) sensors are widely used to obtain dataset to train ML models for human activity recognition. A key challenge in building highly accurate ML models lies in collecting a wide variety of activity data from a large number of users. Such data collection is often a highly time-consuming and costly process.