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

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. INDEX TERMS deep neural network, head orientation prediction, high-density electromyography, lowlatency virtual reality, surface electromyography.

Wearable Technology and Visual Reality Application for Healthcare Systems

“…Sugiarto [15] CNN High Delsys Trigno IMU Pancholi [16] LDA High I7 core Raurale [17] Subsequently classified Medium ARM Cortex A-53 Wang [18] Enhanced So and Chan Low FPGA Our propose ASM S&C Low nRF52840 Artificial intelligence (AI) has been widely used in the identification and analysis of physiological signals in recent years. Recent common EMG analyses and studies focused on AI application.…”

“…This study combined physiological signal modules (namely, ECG, EEG, PPG, and EMG) with innovative educational technologies, such as the Internet, IoT [7,8], and virtual reality (VR) [9,10], to develop a cloud system with telecare services [11,12]. Using the VR and open-source software (OSS), this study developed an interesting VR digital game that could stimulate the elderly users to utilize the application for health management [13][14][15]. The usage scenario of the proposed VR health game is shown in Figure 1.…”

“…Recent common EMG analyses and studies focused on AI application. For example, Sugiarto [15] analyzed the surface EMG (sEMG) and highdensity EMG (HD-EMG) of the neck muscle (designated muscle) and used Convolutional Neural Network (CNN) for training to correct the end-to-end delay of the VR system and alleviate negative effects such as motion sickness. Sugiarto [15] also employed three pairs of wireless sEMG sensors from Delsys Trigno (Delsys, MA, USA) as the software.…”

“…For example, Sugiarto [15] analyzed the surface EMG (sEMG) and highdensity EMG (HD-EMG) of the neck muscle (designated muscle) and used Convolutional Neural Network (CNN) for training to correct the end-to-end delay of the VR system and alleviate negative effects such as motion sickness. Sugiarto [15] also employed three pairs of wireless sEMG sensors from Delsys Trigno (Delsys, MA, USA) as the software. Pancholi [16] analyzed the peak average power (PAP) of EMG and adopted Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) for training and prosthetics.…”

High-Adhesive Flexible Electrodes and Their Manufacture: A Review

“…As a method for measuring electrical signals, electrodes are the most critical technology in the EP working system. EP signal recording applications include the brain-computer interface, human-computer interface (HCI) [2][3][4], and human-machine interface (HMI) [5], etc., in addition to some innovative applications, such as attention detection [6] and the development of a point-of-care (POC) [7] diagnostic platform.…”