Surface EMG vs. High-Density EMG: Tradeoff between Performance and Usability for Head Orientation Prediction in VR Application

Tommy Sugiarto, Chun Lung Hsu, Chi Tien Sun, Wei Chun Hsu, Shu Hao Ye, Kuan Ting Lu

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

Head orientation prediction is one of the solutions to reduce end-to-end latency on Virtual Reality (VR) systems and is important since it can alleviate negative effects like motion sickness. This study compared head orientation prediction models from two different electromyography (EMG) systems: surface EMG (sEMG) and High-Density EMG (HD-EMG). The deep learning method was used to train the prediction model, and the results showed that the model with input from the pre-processed sEMG + IMU sensor outperformed the model with input from the HD-EMG + IMU sensor. However, the decreasing performance from HD-EMG was compensated by its comfort and the ease of use of its electrode. This tradeoff between performance and usability with sEMG compared to HD-EMG should be a consideration for users who want to choose between performance and ease of use for head orientation prediction purposes. Comparison with state-of-the-art head prediction methods proved that the sEMG-based model offers better performance in predictions when users change their head directions, which was quantified by calculating the dt peaks. In other words, our sEMG-based prediction model is suitable for VR applications, which require the user to perform high-intensity or abrupt movements, such as in FPS games or exercise/sports games.

Original languageEnglish
Article number9381217
Pages (from-to)45418-45427
Number of pages10
JournalIEEE Access
Volume9
DOIs
StatePublished - 2021

Keywords

  • Deep neural network
  • head orientation prediction
  • high-density electromyography
  • low-latency virtual reality
  • surface electromyography

Fingerprint

Dive into the research topics of 'Surface EMG vs. High-Density EMG: Tradeoff between Performance and Usability for Head Orientation Prediction in VR Application'. Together they form a unique fingerprint.

Cite this