Search

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.

Machine Learning (ML) models are widely used to infer human activities. However, collecting data to train ML models in realworld often requires significant time and effort. In this paper, we suggest a novel data collection framework to leverage pre-built VR applications and simulating tools. In particular, we applied the concept of virtual Inertial Measurement Unit (IMU) to capture activities of an avatar in simulation. Our initial results show that Random Forest (RF), Support Vector Machine (SVM), and Long Short Term Memory (LSTM) models built with the virtual sensor data can classify three activities (i.e., standing, running, walking) over a realworld dataset at the accuracy of 80.40% (87.83% precision and 80.12% recall), 67.52% accuracy (72.24% precision and 68.15% recall), and 77.67% accuracy (86.25% precision and 77.63% recall), respectively. The early results show the initial feasibility of simulation-driven machine learning without real-world data