DocumentCode :
665197
Title :
A validation study on muscle activity prediction of a lower limb musculoskeletal model using EMG during normal walking
Author :
Wibawa, A.D. ; Verdonschot, Nico ; Burgerhof, J.G.M. ; Purnama, I.K.E. ; Andersen, Mads Schaarup ; Halbertsma, J.P.K. ; Diercks, R.L. ; Verkerke, G.J.
Author_Institution :
Dept. of Multimedia & Network Eng., Inst. of Technol. Sepuluh Nopember, Surabaya, Indonesia
fYear :
2013
fDate :
7-8 Nov. 2013
Firstpage :
260
Lastpage :
264
Abstract :
This study focused on validating muscle activities predicted by the a Lower Limb musculoskeletal model of AnyBody Modeling System (AMS) against measured muscle activity (EMG) from ten healthy subjects who performed a normal walking task. The GaitLowerExtremity Model (GLEM) of AMS was used in this study. Eight EMG electrodes measured the activity of eight muscles of the right leg: Vastus Medialis, Vastus Lateralis, Rectus Femoris, Semitendinosus, Biceps Femoris, Gastrocnemius Medialis and Lateralis and Tibialis Anterior. Four different thresholds were applied on both curves (predicted and measured muscle activity): 10%, 25%, 35% and 45% of the mean of the graph threshold (MGT) before they were being compared quantitatively in the same threshold level. Number of onset and offset of muscle activity were used to quantify the level of agreement. Visual inspection showed good agreement between EMG and predicted muscle activity. In general, for all parameters the 45 % MGT showed the best agreement compared to the other MGT. For the number of onset and offset, two muscles showed fair agreement and four muscles showed slight agreement. This first attempt in a quantitative point of view showed that 6 muscles showed a slight positive agreement out of 8 in both variables number of onset and offset. The differences between AMS and EMG patterns can be attributed to the nature of the modeling process such as assumption and simplification, and possible inaccuracy of determining knee kinetic data during motion.
Keywords :
biomedical electrodes; electromyography; gait analysis; medical signal processing; EMG electrodes; EMG patterns; anybody modeling system; biceps femoris; gait-lower extremity model; gastrocnemius medialis; knee kinetic data; lateralis; lower limb musculoskeletal model; mean graph threshold; muscle activity prediction; normal walking task; rectus femoris; right leg; semitendinosus; tibialis anterior; vastus lateralis; vastus medialis; visual inspection; Biomechanics; Electromyography; Foot; Knee; Legged locomotion; Muscles; AnyBody Modeling System; EMG; Inverse dynamics analysis; Muscle activity; Musculoskeletal Model;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Instrumentation, Communications, Information Technology, and Biomedical Engineering (ICICI-BME), 2013 3rd International Conference on
Conference_Location :
Bandung
Print_ISBN :
978-1-4799-1649-8
Type :
conf
DOI :
10.1109/ICICI-BME.2013.6698504
Filename :
6698504
Link To Document :
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