<p>Data file 1.</p>

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）应用的普及率持续提升，这一趋势在众多研究中均有体现，尤以探讨VR对人体、身体活动及运动训练影响的相关研究为甚。对比研究显示，模拟环境可通过差异化路径影响机体的生理与心理反应。将VR与肌电图（EMG）系统相结合，为实时研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途径。但目前仅有少数研究提供了肌肉疲劳相关的定量数据。本研究借助VR环境，对此前已有研究中报道的8名健康男性受试者展开肌肉疲劳相关探索。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。本研究采用虚幻引擎5（Unreal Engine 5）开发了VR模拟场景，还原自然河道环境以应用于划船训练。研究对既往采集数据及VR场景下获取的数据均应用离散小波变换（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（Rectus Femoris）、股二头肌（Biceps Femoris）及腓肠肌外侧头（Gastrocnemius Lateralis）这三块被测肌肉，对比VR与非VR条件的威尔科克森符号秩检验（Wilcoxon signed-rank tests）结果显示，二者无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肌肉疲劳的定量评估提供了极具价值的研究框架。通过将VR与肌电分析相结合，该方法为沉浸式环境下肌肉疲劳及其调控机制的研究提供了全新视角。