## 1 Introduction
Features extracted from EMG are also being used for classification purposes of locomotion modes and gait phases with the application of machine and deep learning techniques [26,27]. An average accuracy of 87.4% was achieved using artificial neural networks in the classification of stance and swing phases based on EMG signals [28]. In a study, wearing a head-mounted display (HMD) and watching one of the three scenes in a randomized order, participants completed four standing activities in three distinct situations. During their standing duties, they had to keep their arms at their sides and try their best to maintain their stability. A virtual reality-based tracker placed in the lumbar region was used to capture the body wobble. When performing tandem and one-leg standing tasks, the effects of visual sights on postural stability interact considerably. During these tasks, there are larger sway parameters in the VrS and BS conditions compared to the RS condition and lower BS/VrS ratios compared to BS/RS ratios [29]. Similarly, in another study, when the virtual moving room traversed in the participant's direction and caused an unanticipated movement, it caused noticeably more postural sway than when it moved in the opposite direction and caused an expected movement. The results of the investigation showed a considerable improvement in confidence in balancing without simulator sickness and with genuine immersion. The virtual moving room may create a lifelike virtual reality experience without hurting confidence in balancing or generating VR simulator sickness [30]. To create a VR environment that consistently illustrates the reweighting of sensory information across the vestibular, somatosensory, and visual systems, Virtual Reality Comprehensive Balance Assessment and Training (VR-ComBAT) was used. VR-ComBAT can identify multidirectional shifts in young, healthy participants' postural control in terms of the center of pressure (COP) under various circumstances. To detect the displacement and velocity of COP, VR-ComBAT is composed of a head-mounted device with integrated VR, two VR tracking sensors, and a force plate connected to the VR headset. For every VR-ComBAT exercise, participants were instructed to tread on the force plate or the foam surface and hold their posture. The study's objectives were to evaluate VR-ComBAT's safety, sensation of presence, system use, and face validity in young, healthy subjects [31]. In another study, automated terrain classification in lower limb rehabilitation systems was presented using both the EMG and GRF signals to achieve a high accuracy of 96.8%. The researchers collected EMG and GRF signals from ten healthy subjects and extracted 21 statistical features. A support vector machine (SVM) machine learning model was used for the application of smart neuromuscular-mechanical fusion and machine learning-based terrain classification techniques [32]. Hence, from [27; 28; 29; 30; 31; 32], a clear research gap can be observed in terms of monitoring GRF and EMG while inducing controlled body imbalance in an immersive environment and then classifying the body imbalance intensity into low, medium, and high. Therefore, using Oculus Quest 2 to induce controlled body imbalance and establish an immersive environment, as well as using a force platform and BIOPAC MP 36 to record resulting variations in the GRF and EMG signals and classify body imbalance and its intensities using machine learning techniques, was considered an optimal option to fill that research gap. Machine learning techniques are used to classify body imbalance into low, medium, and high intensities.