利用结构方程模型对下肢肌肉活动和踝关节肌腱生物信号进行纵向分析。

IF 1.8 Q3 MEDICINE, RESEARCH & EXPERIMENTAL
Tatsuhiko Matsumoto, Yutaka Kano
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引用次数: 0

摘要

我们收集了 63 名参与者的生物信号,并提取了与每个用力水平相对应的特征。数据被分为典型模式和非典型模式。我们使用线性潜曲线模型(LCM)和条件线性 LCM 进行了数据分析。典型模式的拟合程度较高。踝围和肌肉质量等因素影响了模型截距。踝围越大,表明从肌腱到皮肤表面的信号传输衰减,导致生物信号值越低。 这些结果表明,使用压电薄膜传感器可以捕捉到脚踝附近肌腱的生物信号。有研究将来自肌腱的生物信号定义为机械伸展图。研究表明,源自肌腱的生物信号与施加的肌肉力量之间的关系可以线性解释。这项研究的启示可能有助于运动控制和康复领域的个性化方法。阐明生物信号产生机制的生理学研究是必要的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Longitudinal analysis of lower limb muscle activity and ankle tendon biosignals using structural equation modeling.

We collected biosignals from 63 participants and extracted the features corresponding to each level of exerted muscle force. Data were classified into typical and atypical patterns. Data analysis was performed using the Linear Latent Curve Model (LCM) and the Conditional Linear LCM. The typical patterns demonstrated a high degree of fit. Factors, such as ankle circumference and muscle mass, influenced the model intercept. A larger ankle circumference indicated attenuation of signal transmission from the tendon to the skin surface, leading to lower biosignal values.  These results indicate that biosignals from the tendons near the ankle can be captured using piezoelectric film sensors. There are studies that define biosignals originating from tendons as mechanotendography. It has been demonstrated that the relationship between biosignals originating from tendons and the exerted muscle force can be explained linearly. Insights from this study may facilitate individualized approaches in the fields of motion control and rehabilitation. Physiological studies to elucidate the mechanisms underlying biosignal generation are necessary.

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来源期刊
European Journal of Translational Myology
European Journal of Translational Myology MEDICINE, RESEARCH & EXPERIMENTAL-
CiteScore
3.30
自引率
27.30%
发文量
74
审稿时长
10 weeks
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