Electromyography (EMG) Signal Analysis For Manual lifting Using Time-Frequency Distribution

• Main Author: Tengku Zawawi, Tengku Nor Shuhada
• Format: Thesis
• Language: English; English
• Published: 2016
• Subjects: T Technology (General); TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.utem.edu.my/id/eprint/18172/1/Electromyography%20%28EMG%29%20Signal%20Analysis%20For%20Manual%20lifting%20Using%20Time-Frequency%20Distribution%2024%20Pages.pdf; http://eprints.utem.edu.my/id/eprint/18172/2/Electromyography%20%28EMG%29%20Signal%20Analysis%20Of%20Manual%20Lifting%20Using%20Time-Frequency%20Distribution.pdf; http://eprints.utem.edu.my/id/eprint/18172/; https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=99998
• Institution: Universiti Teknikal Malaysia Melaka

In manufacturing industries, manual lifting is commonly practiced by workers in their routine to move or transport objects to a desired place. Manual lifting with higher repetition and loading using biceps muscle contribute to the effects of soft tissues and muscle fatigue that affect the performance and efficiency of the worker. Electromyography (EMG) is a device to detect the signal’s muscle that is use to investigate muscular disorder. Fast-Fourier transform is the common technique used in signal processing. However, this technique only present spectral information and have the limitation to provide the time-frequency information. EMG signals is complicated and highly complex which is consists of variable frequency and amplitude. Thus, time-frequency analysis technique is needed to be employed to provide spectral and temporal information of the signal. This research presents the analysis of EMG signal using Fast-Fourier Transform and time-frequency distribution (TFD) which is spectrogram to estimate the parameters. Manual lifting activities is repeated to five times with the different load mass and lifting height are performed until achieve muscle fatigue to collect the data. From experiments, the raw data of EMG signals were collected via Measurement Configuration Data Collection of NORAXON INC. The parameters are extracted from EMG signal such as instantaneous root mean square (RMS) voltage, mean of RMS voltage and instantaneous energy to determine the information of manual lifting behaviour such as muscle fatigue, strength and energy transfer for the subject’s performance evaluation. The results show the relationship between all the parameters involve in manual lifting activities and its behaviour. The higher subjects is easier to handle manual lifting with the higher lifting height, but tough body have advantage to handle higher load mass. The increasing of load masses and lifting height are highly proportional to the strength and energy transfer, however inversely proportional to reach muscle fatigue. The overall results conclude that, the application of spectrogram clearly give the information of the subject’s muscle performance based on the manual lifting activities.