An EMG-driven musculoskeletal model to predict muscle forces during performing a weight training exercise with a dumbbell

  • سال انتشار: 1391
  • محل انتشار: نوزدهمین کنفرانس مهندسی پزشکی ایران
  • کد COI اختصاصی: ICBME19_031
  • زبان مقاله: انگلیسی
  • تعداد مشاهده: 1385
دانلود فایل این مقاله

نویسندگان

Fatemeh Moosavi

Biomedical Engineering Department Islamic Azad University, Science and Research Branch Tehran, Iran

Arefeh Pasdar

Biomedical Engineering Department Islamic Azad University, Science and Research Branch Tehran, Iran

Hossein Ehsani

Biomedical Engineering Department Amirkabir University of Technology Tehran, Iran

Mostafa Rostami

Biomedical Engineering Department Amirkabir University of Technology Tehran, Iran

چکیده

Musculoskeletal system of human body is a redundant system and as a result, employing only inverse dynamics techniques to obtain muscle forces would lead to a dead end. Using EMG signals in order to obtain muscle forces, has been used extensively. In this study, in order to predict muscle forces of elbow flexors (Biceps brachii, brachioradialis, and brachialis) and extensors (Triceps brachii) during flexion/extension weight training with a dumbbell, a hybrid EMG-driven method has been implemented. 6 subjects (4 women and 2 men) were volunteered for the experiments. During performing the action, using a high speed camera and a muscle tester device, kinematic information and EMG signals were obtained, respectively. Besides, exploiting manual muscle testing method, maximum voluntary contraction of all of the mentioned muscles for each subject has been measured. The EMG-driven method, incorporated a forward and an inverse dynamics approach, and by comparing the joint moments obtained from these two routines, the unknown variables of the model (electromechanical delay, shape factor, excitation filter coefficients) were obtained. Finally, in order to compare the virtue of the muscle forces, these results were compared with the same results obtained from a static optimization method (objective function: sum of squared muscle forces). Conducting a two-way ANOVA for comparing the results, a significant difference between the two results, has been observed (P < 0.005).

کلیدواژه ها

musculoskeletal simulation; EMG-driven method; manual muscle testing; hybrid method; Hill-based muscle model

مقالات مرتبط جدید

اطلاعات بیشتر در مورد COI

COI مخفف عبارت CIVILICA Object Identifier به معنی شناسه سیویلیکا برای اسناد است. COI کدی است که مطابق محل انتشار، به مقالات کنفرانسها و ژورنالهای داخل کشور به هنگام نمایه سازی بر روی پایگاه استنادی سیویلیکا اختصاص می یابد.

کد COI به مفهوم کد ملی اسناد نمایه شده در سیویلیکا است و کدی یکتا و ثابت است و به همین دلیل همواره قابلیت استناد و پیگیری دارد.