Regression models of muscle activity, psychophysical experience and heart rate in manual lifting task
The mismatch between human and work system can influence workers’ efficiency, productivity, and occupational health. Improper design of manual materials handling (MMH) tasks in manufacturing industry is one of the causes of this mismatch, which is one of risk factors leading to various occupational...
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| Format: | Thesis |
| Language: | English English |
| Published: |
2017
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| Online Access: | http://eprints.utem.edu.my/id/eprint/21428/1/Regression%20Models%20Of%20Muscle%20Activity%2C%20Psychophysical%20Experience%20And%20Heart%20Rate%20In%20Manual%20Lifting%20Task.pdf http://eprints.utem.edu.my/id/eprint/21428/2/Regression%20models%20of%20muscle%20activity%2C%20psychophysical%20experience%20and%20heart%20rate%20in%20manual%20lifting%20task.pdf |
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| Summary: | The mismatch between human and work system can influence workers’ efficiency, productivity, and occupational health. Improper design of manual materials handling (MMH) tasks in manufacturing industry is one of the causes of this mismatch, which is one of risk factors leading to various occupational injuries such as back pain, neck pain, and sprain and strain in the muscles. Engineers and managements require a regression model to design work process related to MMH tasks. The regression model must be able to predict the effects of MMH parameters such as load mass, lifting height, and twist angle to workers’ physical charateristics including muscle activity, psychophysical experience, and heart rate. Existing studies, guidelines and tools related to industrial ergonomics have shown a limitation in predicting the effects of MMH on human pysical. Thus, a development of regression model for designing MMH that can meet above requirement is needed. The aim of this study is to develop regression models of muscle activity, psychophysical experience, and heart rate with respect to load mass, lifting height, and twist angle for the purpose of predicting and redesigning the MMH processes. This study performed questionnaire survey at industries, experiments on manual lifting task using full factorial design of experiment, develop and validate regression models through statistical analysis. In experimental task, ten female with equal numbers of male subjects with no musculoskeletal disorders were paid to perform eight lifting tasks with five replications for each task. Results of this study found that the developed regression models able to predict the electromyography (EMG) signals in the erector spinae and biceps brachii, psychophysical experience (Borg Scale), and heart rate (beats per minute) when the load mass in the range of 5 kg to 15 kg, 55 cm to 130 cm of lifting height, and twist angle ranging from 0 degrees to 90 degrees. Based on the developed regression models, this study concluded that the load mass contributed the greatest effect to muscle activity, psychophysical experience, and heart rate. It is expected that the use of developed regression models may facilitate the MMH process design through the optimization of muscle activity, psychophysical experience, and heart rate. Hence, better compatibility of MMH task and worker may be achieved resulting in efficiency, productivity and occupational health improvement. |
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