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ISSN E 2409-2770
ISSN P 2521-2419

Workers Ergonomics Measures Enhancement Through Surface Electromyography (EMG)

Usman Karim Awan, Salman Hussain

Vol. 10, Issue 02, PP. 01-09, February 2023


Keywords: Ergonomics, Electromyography, Fatigue Analysis, EMG signal classifier

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Poor ergonomics directly affect the performance of workers and its major cause is muscle fatigue. Conventional methods of fatigue assessment are unrealistic and based on the perception of an individual. Fatigue monitoring systems currently available are highly sophisticated and cumbersome to implement. There is a need for a smart real-time fatigue monitoring system. This study aims to propose an EMG-based fatigue monitoring system by targeting the bicep muscle of workers through real-time fatigue monitoring. EMG signal classifier is developed for data acquisition, manipulation, and analysis to assess muscle fatigue. In the end, a case study of gym-goers was investigated by implementing the developed system to differentiate between fatigued and non-fatigued muscles. The participants involved with poor ergonomics experienced muscle fatigue earlier than others. The proposed system can be utilized to design work-rest schedules, prevent musculoskeletal disorders, and increase the performance of employees.

  1. Usman Karim Awan ,, Department of Engineering Management, University of Engineering and Technology, Taxila, Pakistan.
  2. Salman Hussain ,, Department of Engineering Management, University of Engineering and Technology, Taxila, Pakistan.

Usman Karim Awan and Salman Hussain Workers Ergonomics Measures Enhancement Through Surface Electro International Journal of Engineering Works Vol. 10 Issue 01 PP. 01-09 February 2023.

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