Electromyography (EMG) Signal Analysis For Manual Lifting In Industrial Application

Tee, Wei Hown (2016) Electromyography (EMG) Signal Analysis For Manual Lifting In Industrial Application. Project Report. UTeM, Melaka, Malaysia. (Submitted)

Text (24 Pages) Electromyography (EMG) Signal Analysis For Manual Lifting In Industrial Application.pdf - Submitted Version Download (425kB)

Abstract

This study discusses about the analysis about Electromyography (EMG) signal from the waist and lower part of the body during manual handling activities. Manual handling is any activity that requires a person to use his own forces to lift, carry, push, pull, move or hold anything. It includes work involving repetitive activities and awkward postures as well. Industry nowadays involves all workers either engineers, technologists or technicians for example handling the machines, loads and things manually. Working out the session with awkward postures or movements through repetitive manual handling may damage to the musculoskeletal system of the body of the workers and may lead to low back pain [1]. In this project, the style of lifting loads such as postures or gestures will be focused in order to obtain the EMG signal accurately. The manual lifting action is basically based on the common lifting involving in the industries. The manipulated variable of this project will be in terms of size and style of subjects in lifting of objects while fixing the gender of the subject as a constant. The subject will be carrying out the experiment for few times by lifting up a load of 5kg, 10kg and 15kg onto a rack of 70cm as well as 140cm, the load will then be lifted back onto the ground by the subject. The EMG signals will be acquired from the waist and lower part of the subjects during the lifting of different weight of the loads [3]. The result of the data will be collected and analyzed in MATLAB by frequency and time domain algorithm in order to get the frequency and time of the data received. The research focuses on the strength applied by the subject by calculating the EMG signal in terms of voltage based on the signal received. The signal collected will be estimated to identify the muscle based on the voltage from the identified phases. The graphical user interface (GUI) for displaying the signal analyzed from the waist and lower part of the body during manual lifting will be built.

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