Core lifting task assessment using time-frequency distribution of surface electromyogram signal
Manual material handling (MMH) is commonly practised in the majority of industrial working environments. However, prolonged and incorrect MMH can cause fatigue, resulting in musculoskeletal disorders (MSDs). Workers who have suffered and fully recovered from MSDs following treatment and rehabilit...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2019
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/77613/1/FK%202019%2014%20ir.pdf |
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| Summary: | Manual material handling (MMH) is commonly practised in the majority of industrial
working environments. However, prolonged and incorrect MMH can cause fatigue,
resulting in musculoskeletal disorders (MSDs). Workers who have suffered and fully
recovered from MSDs following treatment and rehabilitation, are constantly evaluated
to determine their residual functional abilities. However, the functional capacity
evaluation (FCE) presently in use to measure a person’s physical ability to perform
specific work activities depends on the visual observations of a therapist. A crucial
constraint inherent in the FCE test is the likelihood that information other than visual
observations could influence the therapist's decision. Recent studies indicate that strong
characteristics of surface electromyography (SEMG) on muscle performance exist.
Therefore, this study has aimed to extend these findings by improving the reliability and
validity of the FCE by considering SEMG signals to automatically determine the work
level categories of individuals. Eleven healthy control subjects without a previous history
of MSD and eleven validation subjects with a previous history of MSD participated in
an experiment in performing the FCE’s core-lifting task. Surface EMG signals were
collected from four muscles; right and left biceps brachii (BB), and the right and left
erector spinae (ES). Although given the SEMG signal is a highly complex and non-stationary signal, the timefrequency
distribution (TFD) technique was used to automatically segment and process
the signal. A new auto-segmentation through a spectrogram was utilised to reduce the
computation complexity of processing the long EMG signal recording demonstrating
excellent performance regarding accuracy, compared to conventional segmentation
techniques. For the processing stage, three TFDs; spectrogram, Gabor transform, and Stockwell transform were tested to determine the best TFD for the pattern recognition
system. While Stockwell transform has higher computation complexity, this technique
was the best in terms of accuracy.
Three parameters were extracted from the surface EMG signals and three new features
(muscle strength, muscle power, and muscle endurance) were estimated from the average
RMS voltage (Vrms(avg)) which became input to the classifier. A hybrid combination of
Linear Discriminant Analysis and Support Vector Machine demonstrated a 96%
accuracy of, 100% sensitivity, 92% specificity, 100% precision and 0.0035 crossvalidation
error. In conclusion, this study demonstrated that the new EMG-based FCE
was able to analyse the subject’s performance, work level categories and automatically
classifying these, thereby, lessening the possibility of error caused by the therapist. |
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