A comparison of a maximum exertion method and a model-based, sub-maximum exertion method for normalizing trunk EMG.
A comparison of a maximum exertion method and a model-based, sub-maximum exertion method for normalizing trunk EMG.
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DOI：
10.1016/j.jelekin.2011.05.003
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发表时间：
2011-10
影响因子：
2.5
通讯作者：
Reeves, N. Peter
中科院分区：
文献类型：
--
作者：
Cholewicki, Jacek;van Dieen, Jaap;Lee, Angela S.;Reeves, N. Peter
将来自具有疼痛症状（例如，腰痛）的患者的EMG数据标准化的问题在于，这些患者可能不愿意或不能进行最大用力。此外，对从最大或次最大任务获得的参考信号的归一化倾向于掩盖可能由于病理而存在的差异。因此，我们提出了一种新的方法（增益法）规范化躯干肌电信号数据，克服了这两个问题。GAIN方法不需要最大用力（MVC），并且倾向于在各种任务的肌肉募集模式中保留不同的特征。10名健康受试者进行了各种等长躯干运动，同时记录了10块肌肉的EMG数据，随后使用GAIN和MVC方法进行了标准化。当任务在三个相对力水平（10%、20%和30%MVC）下执行时，MVC方法导致受试者之间的变化较小，而当任务在三个绝对力水平（50 N、100 N和145 N）下执行时，GAIN方法导致受试者之间的变化较小。这一结果意味着MVC方法提供了肌肉努力的相对测量，而GAIN归一化的EMG数据给出了绝对肌肉力量的估计。因此，GAIN标准化的EMG数据倾向于保留受试者之间的EMG差异，因为受试者招募他们的肌肉来执行各种任务，而MVC标准化的数据将倾向于抑制这种差异。EMG标准化方法的适当选择将取决于实验者试图回答的具体问题。
The problem with normalizing EMG data from patients with painful symptoms (e.g. low back pain) is that such patients may be unwilling or unable to perform maximum exertions. Furthermore, the normalization to a reference signal, obtained from a maximal or sub-maximal task, tends to mask differences that might exist as a result of pathology. Therefore, we presented a novel method (GAIN method) for normalizing trunk EMG data that overcomes both problems. The GAIN method does not require maximal exertions (MVC) and tends to preserve distinct features in the muscle recruitment patterns for various tasks. Ten healthy subjects performed various isometric trunk exertions, while EMG data from 10 muscles were recorded and later normalized using the GAIN and MVC methods. The MVC method resulted in smaller variation between subjects when tasks were executed at the three relative force levels (10%, 20%, and 30% MVC), while the GAIN method resulted in smaller variation between subjects when the tasks were executed at the three absolute force levels (50 N, 100 N, and 145 N). This outcome implies that the MVC method provides a relative measure of muscle effort, while the GAIN-normalized EMG data gives an estimate of the absolute muscle force. Therefore, the GAIN-normalized EMG data tends to preserve the EMG differences between subjects in the way they recruit their muscles to execute various tasks, while the MVC-normalized data will tend to suppress such differences. The appropriate choice of the EMG normalization method will depend on the specific question that an experimenter is attempting to answer.
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