Earticle

초록

영어

Musculoskeletal modeling nowadays is becoming the most common tool for studying and analyzing human motion. Besides its potential in predicting muscle activity and muscle force during active motion, musculoskeletal modeling can also calculate many important kinetic data that are difficult to measure in vivo, such as joint force or ligament force. This paper will validate muscle activity predicted by the model during a static motion like knee flexion motion (squat motion). In this experiment, knee flexion motion was performed by 5 healthy subjects and modeled by using Gait Lower Extremity model from AnyBody Modeling System (AMS). Eight lower limb muscle activity prediction from the model will be validated by 8 EMG electrodes that measured the same muscles during squat motion. Muscle activity pattern and the position of onset would be used as a key factor in this validation study. Pearson correlation coefficient will be used to compare the pattern of both graphs. Knee joint force prediction from the model will also be compared with the literature studies. The result showed that 3 muscles showed high correlation coefficient, while the other 4 muscles showed slightly medium and one showed low correlation. Time delay of muscle activation between the model and EMG was recorded from Vastus Medialis muscle (18.38 ms) and Vastus Lateralis (22.8 ms), with muscle activation from the model was late compared to EMG. In conclusion, this statistical study has shown some detail differences between EMG and muscle activity prediction from the model. Knee flexion motion can be used as a trap motion when validating muscle activity of a musculoskeletal model, because the model will activate muscle activity based on motion data of markers, while in knee-flexed position, there was no marker’s movement, but the EMG was highly active due to the posture of the subjects in maintaining the knee-flexed position. However, the knee compressive force prediction from the model has showed positive confirmation from the literatures.

목차

Abstract
 1. Introduction
 2. Method
  2.1 Subjects
  2.2 Protocol and Modeling the Motion
  2.3. Data Analysis
  2.4. EMG Comparison
 3. Result
 4. Discussion and Conclusion
 References

키워드

저자

• Wibawa, A. D [ Department of Multimedia and Network Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia ]
• Verdonschot, N [ Orthopaedics Research Laboratory, Radboud University Medical Centre, Nijmegen, the Netherlands / Department of Biomechanical Engineering, University of Twente, Enschede, the Netherlands ]
• Halbertsma, J. P. K [ Rehabilitation Medicine, University of Groningen, University Medical Center Groningen, the Netherlands ]
• Burgerhof, J. G. M [ Epidemiology, University of Groningen, University Medical Center Groningen, the Netherlands ]
• Diercks, R. L [ Orthopaedics, University of Groningen, University Medical Center Groningen, the Netherlands ]
• Verkerke, G. J [ Rehabilitation Medicine, University of Groningen, University Medical Center Groningen, the Netherlands / Department of Biomechanical Engineering, University of Twente, Enschede, the Netherlands ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Bio-Science and Bio-Technology

• 간기

  격월간

• pISSN

  2233-7849

• 수록기간

  2009~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605