## 1 Introduction  
Virtual reality (VR) is being integrated into various fields such as gaming, education, healthcare, art, entertainment, and business as a new interface. Particularly during the COVID-19 era, there has been active research and content production (e.g., social VR platforms) centered around the metaverse, leading to a significant increase in the number of VR content users [1]. Specifically, when used for rehabilitation, learning, and simulated training, VR is expected to be an effective training tool owing to its unique sense of immersion, which can facilitate adaptation to tasks and improve efficiency [2; 3; 4; 5]. This is attributed to the concept of presence inherent in VR, which induces flow, thereby affecting learning outcomes [3]. In other words, enhancing immersion in the VR environment leads to improved learning outcomes, and various interactive tools such as hand tracking, eye tracking, haptic suits, and data gloves have been developed for this purpose. Meanwhile, research has been conducted to utilize biosignals, such as electromyography (EMG), electroencephalography, and electrooculography, in VR environments to enhance sensory immersion, a step beyond traditional physical controllers and sensors [6]. Among various biosignals, surface electromyography (SEMG) is one of the more reliable methods to recognize human motion intentions because it can facilitate human-robot-environment integration with the help of an intelligent perception system [7]. This is because it allows users to actively control their motion intentions and can be easily attached to the skin without any surgical procedures, making it a useful tool. This study aimed to investigate the impact on immersion when SEMG is utilized for interaction with objects within VR content. The acquisition site of the EMG was set to the flexor digitorum profundus (FDP), which is a muscle that bends the index, middle, ring, and little fingers; finger-bending motions can be assessed by measuring the EMG of the FDP. Moreover, using a standard VR controller may lack realism compared to using one's hands [8]. Therefore, by employing hand tracking instead of a VR controller, a non-restrictive environment was established, allowing free movement of the hands without having to hold a controller. To accomplish the principal aims of this work, the specific objectives were as follows:  
* To develop a novel interface that reflects hand strength, rather than hand movement, in hand tracking. * To propose an electromyography signal-processing method that can measure grip strength. * To develop breakable objects that generate particles in a VR environment. * To develop an interface that can destroy objects with different intensities in a VR environment. * To develop a VR interface that reflects grip strength and apply it to VR contents such as rehabilitation, muscle strengthening, exercise, games, and education in the future. Figure 1 shows the overall system structure proposed in this study. The user wears a head-mounted display (HMD), attaches electrodes to the arm's surface to acquire EMG signals from the FDP, and experiences VR using hand tracking instead of a controller. A system was developed in Unity where, while holding VR objects with their hands, the objects respond based on the measured EMG values, and the user can visually confirm this through the HMD. The effectiveness of the proposed method was verified through a mixed-methods research approach. The research problems to be solved according to the research objectives described above were as follows:  
* Does the proposed EMG signal-processing method measure the user's muscle activity? * Does the user's grip strength reflect the destruction of objects with different strengths in the VR content? * Are users satisfied with the proposed grip strength reflection interface? In this paper, we describe the proposed system modules by analyzing and interpreting the results for the modules' performance and the feasibility of achieving these research purposes.