EMG Processing Based Measures of Fatigue Assessment during Manual Lifting.
EMG Processing Based Measures of Fatigue Assessment during Manual Lifting.
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DOI：
10.1155/2017/3937254
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发表时间：
2017
影响因子：
--
通讯作者：
Abdullah AR
中科院分区：
文献类型：
--
作者：
Shair EF;Ahmad SA;Marhaban MH;Mohd Tamrin SB;Abdullah AR
手动提升是行业中将物体运输或移动到所需位置的常见做法之一。如今，尽管机械化设备广泛使用，但手动起重仍然被认为是执行物料搬运任务的重要方式。不正确的举重策略可能会导致肌肉骨骼疾病 (MSD)，其中过度用力是最重要的因素。为了克服这个问题，使用肌电图（EMG）信号来监测工人的肌肉状况，并找出工人在出现疲劳之前能够承受的最大提升负载、提升高度和重复次数，以避免过度劳累。过去的研究人员介绍了几种肌电图处理技术和不同的肌电图特征，这些特征代表时间、频率和时频域中的疲劳指数。本文回顾了基于肌电图处理的措施在手动举升过程中疲劳评估中的影响。我们相信，这篇论文将极大地有益于那些需要鸟瞰当前可用的生物信号处理的研究人员，从而确定提升应用的最佳技术。
Manual lifting is one of the common practices used in the industries to transport or move objects to a desired place. Nowadays, even though mechanized equipment is widely available, manual lifting is still considered as an essential way to perform material handling task. Improper lifting strategies may contribute to musculoskeletal disorders (MSDs), where overexertion contributes as the highest factor. To overcome this problem, electromyography (EMG) signal is used to monitor the workers' muscle condition and to find maximum lifting load, lifting height and number of repetitions that the workers are able to handle before experiencing fatigue to avoid overexertion. Past researchers have introduced several EMG processing techniques and different EMG features that represent fatigue indices in time, frequency, and time-frequency domain. The impact of EMG processing based measures in fatigue assessment during manual lifting are reviewed in this paper. It is believed that this paper will greatly benefit researchers who need a bird's eye view of the biosignal processing which are currently available, thus determining the best possible techniques for lifting applications.