Sarmiento Jhon F, Benevides Alessandro B, Moreira Marcelo H, Elias Arlindo, Bastos Teodiano F, Silva Ian V, Pelegrina Claudinei C
Programa de Pós-graduação em Biotecnologia RENORBIO and Departamento de Engenharia Elétrica, Universidade Federal do Espírito Santo, Vitoria 29075-910, Brazil.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:7163-6. doi: 10.1109/IEMBS.2011.6091810.
The study of fatigue is an important tool for diagnostics of disease, sports, ergonomics and robotics areas. This work deals with the analysis of sEMG most important fatigue muscle indicators with use of signal processing in isometric and isotonic tasks with the propose of standardizing fatigue protocol to select the data acquisition and processing with diagnostic proposes. As a result, the slope of the RMS, ARV and MNF indicators were successful to describe the fatigue behavior expected. Whereas that, MDF and AIF indicators failed in the description of fatigue. Similarly, the use of a constant load for sEMG data acquisition was the best strategy in both tasks.
疲劳研究是疾病诊断、体育、人体工程学和机器人技术领域的重要工具。这项工作涉及在等长和等张任务中利用信号处理分析表面肌电图（sEMG）最重要的疲劳肌肉指标，目的是标准化疲劳协议，以便为诊断目的选择数据采集和处理方法。结果表明，均方根（RMS）、平均整流值（ARV）和平均频率（MNF）指标的斜率成功地描述了预期的疲劳行为。然而，中值频率下降率（MDF）和平均功率频率增量（AIF）指标未能描述疲劳情况。同样，在这两项任务中，使用恒定负荷进行sEMG数据采集是最佳策略。
