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  1. Shair, E. F., Ahmad, S. A., Marhaban, M. H., Abdullah, A. R., Mohd Tamrin, S. B.
    MyJurnal
    Musculoskeletal disorders (MSDs) caused by muscle fatigue have been a major problem for industry
    which needs to be resolved to save costs related to human resource development (extra training and
    compensation). Detailed fatigue monitoring researches aimed at finding the best fatigue indices is not
    new although studies on the causes of fatigue can be explored further. Identification analysis is required
    to monitor the factors that influence muscle performance characteristic of surface electromyography
    (sEMG) signal. Periodogram monitoring technique applies a frequency domain signal and represents the
    distribution of the signal power over the frequency. It is a technique that allows the tracing of small
    changes in the behaviour of sEMG signal when external parameters are varied. This technique is used
    in this paper to monitor the sEMG signal changes in muscle performance when the lifting height and
    load mass are varied. The periodogram amplitude, which represents the power, increases with the rise in
    lifting height and load mass. From the frequency representation of the periodogram, the root mean square
    voltage (Vrms) is calculated where the muscle performance characteristic could be further identified. The
    Vrms also shows a similar trend when the lifting height and load mass are varied proving the periodogram
    technique is useful to monitor changes in the muscle performance during manual lifting.
  2. Shair EF, Ahmad SA, Marhaban MH, Mohd Tamrin SB, Abdullah AR
    Biomed Res Int, 2017;2017:3937254.
    PMID: 28303251 DOI: 10.1155/2017/3937254
    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.
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