Wang Lan, Wu Yanqi, Zhu Min, Zhao Cuilian
Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China.
Department of Oral and Craniofacial Surgery, Shanghai Ninth People's Hospital, College of Stomatology, Shanghai JiaoTong University of Medicine, National Center of Stomatology, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology, Shanghai, China.
Technol Health Care. 2023;31(1):47-56. doi: 10.3233/THC-213545.
Lip incompetence resulting from mouth breathing is a common clinical manifestation, while there are no definite indicators of amplitude and intensity of muscle functional training in clinical practice, which leads to unsatisfactory training results.
The aim was to quantify the relationship between electromyography (EMG) and force in orbicularis oris muscle, so that the indicators of muscle functional training can be evaluated using EMG signals, so as to improve the training effects.
The EMG and the force signals of orbicularis oris muscle from 0% to 100% MVC within 5 s in twelve healthy subjects (six males and six females; age, 25 ± 2 years; mass, 60 ± 15 kg) were recorded simultaneously for three trials. Four EMG features consisting of RMS, WAMP, SampEn and FuzzyEn were analyzed. The regression analyses were performed using first-order and third-order polynomial model.
There were high correlations between the four EMG features and muscle force with the two models. The third-order model yielded a higher coefficient of determination (R2) than the linear model (p< 0.001) and the result of FuzzyEn (R2: 0.884 ± 0.059) was the highest in the four features.
The third-order model with FuzzyEn of EMG signals may be used to guide the muscle functional training.
口呼吸导致的唇功能不全是一种常见的临床表现，而临床实践中尚无明确的肌肉功能训练幅度和强度指标，导致训练效果不尽人意。
量化口轮匝肌肌电图（EMG）与肌力之间的关系，以便利用EMG信号评估肌肉功能训练指标，从而提高训练效果。
对12名健康受试者（6名男性和6名女性；年龄25±2岁；体重60±15 kg）在5秒内从0%到100%最大自主收缩（MVC）的口轮匝肌EMG和力信号进行同步记录，共进行3次试验。分析了均方根（RMS）、波形平均功率（WAMP）、样本熵（SampEn）和模糊熵（FuzzyEn）这四个EMG特征。使用一阶和三阶多项式模型进行回归分析。
两种模型下，四个EMG特征与肌肉力量之间均存在高度相关性。三阶模型的决定系数（R2）高于线性模型（p<0.001），且模糊熵（R2：0.884±0.059）在四个特征中最高。
基于EMG信号模糊熵的三阶模型可用于指导肌肉功能训练。
