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用于区分健康成年人标准化面部表情的无线高分辨率表面面部肌电图面膜。

Wireless high-resolution surface facial electromyography mask for discrimination of standardized facial expressions in healthy adults.

机构信息

Department of Otorhinolaryngology, Jena University Hospital, Friedrich-Schiller-University Jena, Am Klinikum 1, 07747, Jena, Germany.

School of Electrical Engineering, Tel Aviv University, Tel Aviv, Israel.

出版信息

Sci Rep. 2024 Aug 20;14(1):19317. doi: 10.1038/s41598-024-70205-z.

Abstract

Wired high resolution surface electromyography (sEMG) using gelled electrodes is a standard method for psycho-physiological, neurological and medical research. Despite its widespread use electrode placement is elaborative, time-consuming, and the overall experimental setting is prone to mechanical artifacts and thus offers little flexibility. Wireless and easy-to-apply technologies would facilitate more accessible examination in a realistic setting. To address this, a novel smart skin technology consisting of wireless dry 16-electrodes was tested. The soft electrode arrays were attached to the right hemiface of 37 healthy adult participants (60% female; 20 to 57 years). The participants performed three runs of a standard set of different facial expression exercises. Linear mixed-effects models utilizing the sEMG amplitudes as outcome measure were used to evaluate differences between the facial movement tasks and runs (separately for every task). The smart electrodes showed specific activation patterns for each of the exercises. 82% of the exercises could be differentiated from each other with very high precision when using the average muscle action of all electrodes. The effects were consistent during the 3 runs. Thus, it appears that wireless high-resolution sEMG analysis with smart skin technology successfully discriminates standard facial expressions in research and clinical settings.

摘要

使用凝胶电极的有线高分辨率表面肌电图(sEMG)是心理生理、神经和医学研究的标准方法。尽管它被广泛应用,但电极的放置需要精心设计,既耗时又容易受到机械伪影的影响,因此灵活性较差。无线和易于应用的技术将有助于在现实环境中进行更便捷的检查。为了解决这个问题,我们测试了一种由无线干电极组成的新型智能皮肤技术。软电极阵列被贴在 37 名健康成年参与者(60%为女性;20 至 57 岁)的右半脸。参与者进行了三组标准的不同面部表情练习。使用 sEMG 幅度作为因变量的线性混合效应模型被用于评估面部运动任务和运行之间的差异(分别针对每个任务)。智能电极显示出特定的激活模式,适用于每一种练习。当使用所有电极的平均肌肉活动时,82%的练习可以非常精确地区分彼此。在 3 次运行中,效果是一致的。因此,无线高分辨率 sEMG 分析与智能皮肤技术似乎成功地区分了研究和临床环境中的标准面部表情。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6011/11336214/1653264bdafd/41598_2024_70205_Fig1_HTML.jpg

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