Suppr超能文献

一种用于躯干肌肉组织的非机动车碰撞肌电图标准化技术:第2部分。验证及用于预测脊柱负荷

A non-MVC EMG normalization technique for the trunk musculature: Part 2. Validation and use to predict spinal loads.

作者信息

Marras W S, Davis K G, Maronitis A B

机构信息

The Biodynamics Laboratory, The Ohio State University, 210 Baker Systems, 1971 Neil Avenue, Columbus OH 43210, USA.

出版信息

J Electromyogr Kinesiol. 2001 Feb;11(1):11-8. doi: 10.1016/s1050-6411(00)00040-7.

Abstract

Estimates of the amount of force exerted by a muscle using electromyography (EMG) rely partially upon the accuracy of the reference point used in the normalization technique. Accurate representations of muscle activities are essential for use in EMG-driven spinal loading models. The expected maximum contraction (EMC) normalization method was evaluated to explore whether it could be used to assess individuals who are not capable of performing a maximum exertion such as a person with a low back injury. Hence, this study evaluated the utility of an EMG normalization method (Marras and Davis, A non-MVC EMG normalization technique, Part 1, method development. Journal of Electromyography and Kinesiology 2000) that draws upon sub-maximal exertions to determine the reference points needed for normalization of the muscle activities. The EMC normalization technique was compared to traditional MVC-based EMG normalization by evaluating the spinal loads for 20 subjects (10 males and 10 females) performing dynamic lifts. The spinal loads (estimated via an EMG-assisted model) for the two normalization techniques were very similar with differences being <8%. The model performance variables indicated that both normalization techniques performed well (r(2)>0.9 and average error below 6%) with only the muscle gain being affected by normalization method as a result in different reference points. Based on these results, the proposed normalization technique was considered to be a viable method for EMG normalization and for use in EMG-assisted models. This technique should permit the quantitative evaluation of muscle activity for subjects unable to produce maximum exertions.

摘要

利用肌电图(EMG)估算肌肉施加的力量,部分依赖于归一化技术中使用的参考点的准确性。准确表示肌肉活动对于肌电图驱动的脊柱负荷模型的应用至关重要。对预期最大收缩(EMC)归一化方法进行了评估,以探讨其是否可用于评估无法进行最大用力的个体,例如患有腰伤的人。因此,本研究评估了一种肌电图归一化方法(Marras和Davis,一种非最大自主收缩肌电图归一化技术,第1部分,方法开发。《肌电图与运动学杂志》,2000年)的效用,该方法利用次最大用力来确定肌肉活动归一化所需的参考点。通过评估20名受试者(10名男性和10名女性)进行动态举重时的脊柱负荷,将EMC归一化技术与传统的基于最大自主收缩(MVC)的肌电图归一化进行了比较。两种归一化技术的脊柱负荷(通过肌电图辅助模型估算)非常相似,差异<8%。模型性能变量表明,两种归一化技术的表现都很好(r²>0.9且平均误差低于6%),只是由于参考点不同,归一化方法仅对肌肉增益有影响。基于这些结果,所提出的归一化技术被认为是一种可行的肌电图归一化方法,可用于肌电图辅助模型。该技术应能对无法产生最大用力的受试者的肌肉活动进行定量评估。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验