Developing a method based on EMG to assess muscle fatigue during manual lifting in a simulated laboratory environment

Abstract

Muscle fatigue has been recognized and considered as a high-risk issue for back pain injuries resulting in MSDs and chronic lower back pain. The aim of current research was to investigate differences in surface electromyography (EMG) data of ten back muscles performing a manual lifting task inducing muscle fatigue with respect to four different heights. Four healthy subjects were asked to lift and deposit twenty-four 10-kg boxes for almost 16 minutes in a simulated laboratory environment in order to assess muscle fatigue. EMG data for ten muscles was collected and cut off for each cycle (lift plus deposit) by the magnetometer and evaluated using a time-frequency analysis procedure (STFT) for extracting the mean and median frequency calculation. Median and mean frequency (MDF/MNF), and root mean square (RMS) were measured and used to analyze the changes in the EMG signal of all the tested muscles for four different heights. The results showed that MDF/MNF decreases and RMS increases as a sign of muscle fatigue factor. The results obtained from this experiment revealed that the greatest rate of muscle fatigue was demonstrated by the posterior deltoid muscles compared to the other muscles. Based on the results, during the entire manual lifting task, height 2 and height 1 (at ground level) presented the highest number of fatigue repetitions compared to all other heights. It is evident from the findings that various muscles had their own unique fatigue pattern between the four different heights, and it can be expected that fatigue occurs from approximately 6 to 14 minutes of entire experiment. This research is also important because it will enable warehouse employees to build a structure for proper box setup at acceptable heights to avoid muscle fatigue and eventually reduce.

Date	2 Mar 2021
Original language	American English
Awarding Institution	École de technologie supérieure
Supervisor	Mustapha Ouhimmou (Supervisor) & Rachid Aissaoui (Co-supervisor)