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Fulltext Impact of different lifting height and load mass on muscle performance using periodogram

Shair, E. F., Ahmad, S. A., Marhaban, M. H., Abdullah, A. R., Mohd Tamrin, S. B.

Pertanika Journal of Science & Technology, 2017;25(101):1-8.
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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.
EMG Processing Based Measures of Fatigue Assessment during Manual Lifting

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.
Quantitative assessment of wound healing using high-frequency ultrasound image analysis

Mohafez H, Ahmad SA, Hadizadeh M, Moghimi S, Roohi SA, Marhaban MH, et al.

Skin Res Technol, 2018 Feb;24(1):45-53.
PMID: 28557064 DOI: 10.1111/srt.12388

PURPOSE: We aimed to develop a method for quantitative assessment of wound healing in ulcerated diabetic feet.
METHODS: High-frequency ultrasound (HFU) images of 30 wounds were acquired in a controlled environment on post-debridement days 7, 14, 21, and 28. Meaningful features portraying changes in structure and intensity of echoes during healing were extracted from the images, their relevance and discriminatory power being verified by analysis of variance. Relative analysis of tissue healing was conducted by developing a features-based healing function, optimised using the pattern-search method. Its performance was investigated through leave-one-out cross-validation technique and reconfirmed using principal component analysis.
RESULTS: The constructed healing function could depict tissue changes during healing with 87.8% accuracy. The first principal component derived from the extracted features demonstrated similar pattern to the constructed healing function, accounting for 86.3% of the data variance.
CONCLUSION: The developed wound analysis technique could be a viable tool in quantitative assessment of diabetic foot ulcers during healing.

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