Abstract:
Due to numerous technical and physiological factors, raw EMG signals can be misleading when conducting electromyographic analysis. It is therefore essential to properly normalize EMG signals. Normalization in reference to a Maximum Voluntary Contraction (MVC) has been proven to be the most appropriate, reliable and common normalization technique. However, there are no clear and conclusive recommendations for eliciting MVC from healthy patients. This poses a serious challenge to modern ergonomists, as an erroneous normalization technique would jeopardize the reliability of results. Therefore, the objective of this research is to compare literature-based MVC techniques for six muscles in the neck, shoulder, and low back regions in healthy subjects. The six muscles chosen were: the thoracic erector spinae, the lumbar erector spinae, the latissimus dorsi, the posterior deltoid, the upper trapezius, and the sternocleidomastoid. These muscles were chosen based on the amount of attention they receive in ergonomic research. EMG activities were measured while 15 healthy participants performed specific MVC techniques for each muscle chosen. The results indicated that the lumbar and thoracic subdivisions of the erector spinae muscle can be maximally activated by four similar MVC technique. Furthermore, it was recommended to use the “Prone Extension” test or the “Chest Supported ROW” test to normalize EMG signals from the latissimus dorsi. The “Shoulder Abduction in Slight Extension” test or the “Transvers Abduction” test were recommended as the MVC technique for the posterior deltoid. The results showed that the levels of EMG signal generated by the upper trapezius in the “Abduction 125” test, the ”Elevation and Abduction 90” test, and the “Abduction 90” test were not significantly different. Therefore, either one of the former three tests can be used as a MVC technique for the upper trapezius. Finally, the “Anterolateral Flexion[u201
Description:
Thesis. M.E.M. American University of Beirut. Department of Industrial Engineering and Management, 2016. ET:6383
Advisor : Dr. Saif Al Qaisi, Assistant Professor, Industrial Engineering and Management ; Members of Committee : Dr. Ali Yassine, Professor, Industrial Engineering and Management ; Dr. Ibrahim Alameddine, Assistant Professor, Civil and Environmental Engineering.
Includes bibliographical references (leaves 109-138)