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The growing popularity of virtual reality (VR) applications has been reflected in numerous studies, particularly those examining the effects of VR on the human body, physical activity, and sports training. Comparative research suggests that simulated environments can influence physiological and psychological responses in distinct ways. The integration of VR with electromyographic (EMG) systems opens new opportunities to study biofeedback and muscle activation during exercise in real-time. However, only a limited number of studies have provided quantitative data on muscle fatigue. In the present research eight healthy male participants from previously described studies were examined using a VR environment to explore muscle fatigue. EMG signals were recorded from three muscle groups, and knee flexion angles were monitored. A VR simulation developed in Unreal Engine 5 was designed to reproduce a natural river scene for rowing training. The Discrete Wavelet Transform (DWT) was applied to both previously collected and VR-based data, calculating median frequency (MDF) distributions and linear regression for lower extremity muscles. Wilcoxon signed-rank tests comparing VR and non-VR conditions for the measured muscles: the Rectus Femoris, Biceps Femoris, and Gastrocnemius Lateralis, did not reveal statistically significant differences (all p > 0.05). Although no significant differences were observed, the proposed methodology introduces a valuable framework for quantitative fatigue assessment. By integrating VR with EMG analysis, this approach provides new perspectives for investigating muscle fatigue and its modulation in immersive environments.

虚拟现实（VR）应用的日益普及，已为诸多研究所证实，尤其是针对VR对人体机能、身体活动及运动训练影响的相关研究。对比研究表明，模拟环境可通过独特方式影响生理与心理反应。将VR与肌电图（EMG）系统相结合，为实时探究运动过程中的生物反馈机制与肌肉激活状态提供了全新机遇。然而，目前仅有少量研究提供了肌肉疲劳的定量观测数据。本研究采用VR环境，对既往研究中涉及的8名健康男性受试者开展实验，以探究肌肉疲劳情况。研究采集了3组肌肉的肌电信号，并同步监测膝关节屈曲角度。本研究借助虚幻引擎5（Unreal Engine 5）开发了VR模拟场景，复刻自然河道环境用于划船训练。研究对既往采集的数据集与VR环境下采集的实测数据均实施了离散小波变换（DWT），计算了下肢肌肉的中值频率（MDF）分布，并开展线性回归分析。针对股直肌、股二头肌与腓肠肌外侧头这3块被测肌肉，对比VR与非VR条件的威尔科克森符号秩检验结果显示，未发现统计学意义上的显著差异（所有p值均＞0.05）。尽管未观测到显著差异，但本研究提出的方法为定量肌肉疲劳评估提供了极具价值的研究框架。通过将VR技术与肌电分析手段相结合，该方法为沉浸式环境下的肌肉疲劳及其调控机制研究提供了全新视角。