单调的工作
肌电图
物理医学与康复
斯皮尔曼秩相关系数
相关性
物理疗法
心理学
考试(生物学)
皮尔逊积矩相关系数
医学
统计
数学
几何学
生物
古生物学
作者
Natalia Daniel,Kamil Sybilski,Wojciech Kaczmarek,Dariusz Siemiaszko,Jerzy Małąchowski
出处
期刊:Sensors
[Multidisciplinary Digital Publishing Institute]
日期:2023-05-23
卷期号:23 (11): 5004-5004
被引量:19
摘要
In the scientific literature focused on surface electromyography (sEMG) and functional near-infrared spectroscopy (fNIRS), which have been described together and separately many times, presenting different possible applications, researchers have explored a diverse range of topics related to these advanced physiological measurement techniques. However, the analysis of the two signals and their interrelationships continues to be a focus of study in both static and dynamic movements. The main purpose of this study was to determine the relationship between signals during dynamic movements. To carry out the analysis described, the authors of this research paper chose two sports exercise protocols: the Astrand-Rhyming Step Test and the Astrand Treadmill Test. In this study, oxygen consumption and muscle activity were recorded from the gastrocnemius muscle of the left leg of five female participants. This study found positive correlations between EMG and fNIRS signals in all participants: 0.343-0.788 (median-Pearson) and 0.192-0.832 (median-Spearman). On the treadmill, the signal correlations between the participants with the most active and least active lifestyle achieved the following medians: 0.788 (Pearson)/0.832 (Spearman) and 0.470 (Pearson)/0.406 (Spearman), respectively. The shapes of the changes in the EMG and fNIRS signals during exercise suggest a mutual relationship during dynamic movements. Furthermore, during the treadmill test, a higher correlation was observed between the EMG and NIRS signals in participants with a more active lifestyle. Due to the sample size, the results should be interpreted with caution.
科研通智能强力驱动
Strongly Powered by AbleSci AI