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使用肌电图幅度和频率计算多肌肉疲劳评分并评估肩部整体疲劳。

Using EMG Amplitude and Frequency to Calculate a Multimuscle Fatigue Score and Evaluate Global Shoulder Fatigue.

机构信息

McMaster University, Hamilton, ON, Canada.

出版信息

Hum Factors. 2019 Jun;61(4):526-536. doi: 10.1177/0018720818794604. Epub 2018 Aug 24.

Abstract

OBJECTIVE

The authors developed a function to quantify fatigue in multiple shoulder muscles by generating a single score using relative changes in EMG amplitude and frequency over time.

BACKGROUND

Evaluating both frequency and amplitude components of the electromyographic signal provides a more complete evaluation of muscle fatigue than either variable alone; however, little effort has been made to combine time and frequency domains for the evaluation of myoelectric fatigue.

METHOD

Surface EMG was measured from 14 shoulder muscles while participants performed simulated, repetitive work tasks until exhaustion. Each 60-s work cycle consisted of four tasks (dynamic push, dynamic pull, static drill, static force target matching task) scaled to participants' anthropometrics and strength. The function was generated to calculate a multimuscle fatigue score (MMFS) based on changes in EMG frequency, amplitude, and the number of muscles showing signs of myoelectric fatigue (increase in EMG amplitude; decrease in EMG frequency).

RESULTS

The function was evaluated through changes in MMFS over time: first (31.8 ± 14.6), middle (47.6 ± 25.3), last (58.6 ± 35.5) reference exertions ( p < .05). The evaluation of the relationships between MMFS and changes in strength ( r = -0.510) and MMFS and perceived fatigue (RPF) ( r = 0.298) showed significant relationships over time ( p < .05). MMFS scores increased over time ( p < .05) with significant relationships between MMFS and strength changes and RPF ( p < .05).

CONCLUSION AND APPLICATION

The MMFS allows for comparisons between workplace tasks, which can aid in workplace design to mitigate the development of fatigue.

摘要

目的

作者开发了一种通过随时间的相对变化来生成单个评分的功能,以量化多个肩部肌肉的疲劳程度。

背景

评估肌电图信号的频率和幅度成分比单独评估任何一个变量都能提供更全面的肌肉疲劳评估;然而,很少有人努力将时间和频率域结合起来评估肌电疲劳。

方法

表面肌电图(EMG)从 14 个肩部肌肉中测量,参与者在完成模拟的重复性工作任务直至疲劳。每个 60 秒的工作周期包括四项任务(动态推、动态拉、静态钻孔、静态力量目标匹配任务),根据参与者的人体测量学和力量进行调整。该功能是为了根据 EMG 频率、幅度和出现肌电疲劳迹象的肌肉数量(EMG 幅度增加、EMG 频率降低)的变化来计算多肌肉疲劳评分(MMFS)而生成的。

结果

通过随时间变化的 MMFS 评估功能:首次(31.8 ± 14.6)、中间(47.6 ± 25.3)、最后(58.6 ± 35.5)参考用力(p <.05)。对 MMFS 与力量变化(r = -0.510)和 MMFS 与感知疲劳(RPF)(r = 0.298)之间关系的评估显示,随时间变化具有显著关系(p <.05)。MMFS 评分随时间增加(p <.05),与力量变化和 RPF 之间的关系显著(p <.05)。

结论与应用

MMFS 允许对工作场所任务进行比较,这有助于工作场所设计以减轻疲劳的发展。

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