Chai Guozhong, Wang Yinghao, Wu Jianfeng, Yang Hongchun, Tang Zhichuan, Zhang Lekai
College of Mechanical Engineering, Zhejiang University of Technology, 310023 Hangzhou, China.
Healthcare (Basel). 2019 Nov 20;7(4):150. doi: 10.3390/healthcare7040150.
A running exhaustion experiment was used to explore the correlations between the time-frequency domain indexes extracted from the surface electromyography (EMG) signals of targeted muscles, heart rate and exercise intensity, and subjective fatigue. The study made further inquiry into the feasibility of reflecting and evaluating the exercise intensity and fatigue effectively during running using physiological indexes,thus providing individualized guidance for running fitness. Twelve healthy men participated in a running exhaustion experiment with an incremental and constant load. The percentage of heart rate reserve (%HRR), mean power frequency (MPF) and root mean square (RMS) from surface EMG (sEMG) signals of the rectus femoris (RF), biceps femoris (BF), tibialis anterior muscle (TA), and the lateral head of gastrocnemius (GAL) were obtained in real-time. The data were processed and analyzed with the rating of perceived exertion (RPE) scale. The experimental results show that the MPF on all the muscles increased with time, but there was no significant correlation between MPF and RPE in both experiments. Additionally, there was no significant correlation between RMS and RPE of GAL and BF, but there was a negative correlation between RMS and RPE of RF. The correlation coefficient was lower in the constant load mode, with the value of only -0.301. The correlation between RMS and RPE of TA was opposite in both experiments. There was a significant linear correlation between %HRR and exercise intensity (r = 0.943). In the experiment, %HRR was significantly correlated with subjective exercise fatigue (r = 0.954). Based on the above results,the MPF and RMS indicators on the four targeted muscles could not conclusively identify fatigue of lower extremities during running. The %HRR could be used to identify exercise intensity and human fatigue during running and could be used as an indicator of recognizing fatigue and exercise intensity in runners.
采用跑步耐力实验,探究从目标肌肉的表面肌电图(EMG)信号中提取的时频域指标、心率、运动强度与主观疲劳之间的相关性。该研究进一步探讨了利用生理指标有效反映和评估跑步过程中的运动强度和疲劳的可行性,从而为跑步健身提供个性化指导。12名健康男性参与了递增负荷和恒定负荷的跑步耐力实验。实时获取股直肌(RF)、股二头肌(BF)、胫骨前肌(TA)和腓肠肌外侧头(GAL)表面肌电图(sEMG)信号的心率储备百分比(%HRR)、平均功率频率(MPF)和均方根(RMS)。采用主观用力感觉等级(RPE)量表对数据进行处理和分析。实验结果表明,所有肌肉的MPF均随时间增加,但在两个实验中MPF与RPE之间均无显著相关性。此外,GAL和BF的RMS与RPE之间无显著相关性,但RF的RMS与RPE之间呈负相关。在恒定负荷模式下相关系数较低,仅为-0.301。TA的RMS与RPE之间的相关性在两个实验中相反。%HRR与运动强度之间存在显著线性相关(r = 0.943)。在实验中,%HRR与主观运动疲劳显著相关(r = 0.954)。基于上述结果,四个目标肌肉的MPF和RMS指标不能确凿地识别跑步过程中下肢的疲劳。%HRR可用于识别跑步过程中的运动强度和人体疲劳,可作为跑步者疲劳和运动强度识别的指标。