Virtual IMU Data Augmentation by Spring-Joint Model for Motion Exercises Recognition without Using Real Data
Chengshuo Xia, Yuta Sugiura
Abstract
A conventional motion exercises recognition system only tracks designated motion types, and it enables users cannot use a customized system according to personal needs. The virtual IMU data provides a new opportunity to reduce the cost of training datasets and flexibly design the activity recognition system using online resources. To better design a user-customized motion exercises recognition system using virtual IMU data, this paper proposes a virtual IMU sensor module with a spring-joint model to augment the virtual acceleration signal from the limited online 2D video. The original virtual acceleration signal is extended with data from different acceleration distributions generated by the spring-joint model and used to train a motion exercises recognition system. The proposed method can design a classifier for three motions with limited video resources, showing an average accuracy of 85.5 on the real motion data of seven individuals.