Head-Orientation-Prediction Based on Deep Learning on sEMG for Low-Latency Virtual Reality Application

Reducing end-to-end latency on virtual reality system is important since it can remove several negative effects like motion-sickness and head orientation prediction is one of the solution to do that. On this study, signal from surface Electromyography (sEMG) was utilized to predict future head orientation with model trained from various deep learning algorithms. Total 20 subjects were participated with 6 muscles on neck were recorded for training purpose. The result showed that for both intra-subject and inter-subject method pre-processed sEMG signal + IMU input outperformed model with input from sEMG features + IMU. The result of inter-subject testing method on this study extended opportunity for real-world application in which the user data has never been include in training database.