Research Papers

Estimation of Muscle Response Using Three-Dimensional Musculoskeletal Models Before Impact Situation: A Simulation Study

[+] Author and Article Information
Tae Soo Bae1

Korea Orthopedics and Rehabilitation Engineering Centerbmebae@korec.re.kr

Peter Loan

Musculographics Division,Motion Analysis Inc.peter@musculographics.com

Kuiwon Choi

Korea Institute of Science and Technologychoi@kist.re.kr

Daehie Hong

Department of Mechanical Engineering,Korea Universitydhhong@korea.ac.kr

Mu Seong Mun

Korea Orthopedics and Rehabilitation Engineering Centermsmun@korec.re.kr


Correponding author.

J Biomech Eng 132(12), 121011 (Nov 16, 2010) (7 pages) doi:10.1115/1.4002795 History: Received February 15, 2010; Revised October 06, 2010; Posted October 15, 2010; Published November 16, 2010; Online November 16, 2010

When car crash experiments are performed using cadavers or dummies, the active muscles’ reaction on crash situations cannot be observed. The aim of this study is to estimate muscles’ response of the major muscle groups using three-dimensional musculoskeletal model by dynamic simulations of low-speed sled-impact. The three-dimensional musculoskeletal models of eight subjects were developed, including 241 degrees of freedom and 86 muscles. The muscle parameters considering limb lengths and the force-generating properties of the muscles were redefined by optimization to fit for each subject. Kinematic data and external forces measured by motion tracking system and dynamometer were then input as boundary conditions. Through a least-squares optimization algorithm, active muscles’ responses were calculated during inverse dynamic analysis tracking the motion of each subject. Electromyography for major muscles at elbow, knee, and ankle joints was measured to validate each model. For low-speed sled-impact crash, experiment and simulation with optimized and unoptimized muscle parameters were performed at 9.4 m/h and 10 m/h and muscle activities were compared among them. The muscle activities with optimized parameters were closer to experimental measurements than the results without optimization. In addition, the extensor muscle activities at knee, ankle, and elbow joint were found considerably at impact time, unlike previous studies using cadaver or dummies. This study demonstrated the need to optimize the muscle parameters to predict impact situation correctly in computational studies using musculoskeletal models. And to improve accuracy of analysis for car crash injury using humanlike dummies, muscle reflex function, major extensor muscles’ response at elbow, knee, and ankle joints, should be considered.

Copyright © 2010 by American Society of Mechanical Engineers
Your Session has timed out. Please sign back in to continue.



Grahic Jump Location
Figure 2

Conceptual drawing of car crash experiments. The sled was controlled by a hydraulic actuator with a length of 15 m and configured to represent the seat in a medium-sized passenger car. We used several sensors to set up boundary condition using loadcell and pressure sensor and validation using electrode ① height adjustable sled (5 m), ② shock absorber, ③ experimental vehicle, ④ preventive guide of secession, ⑤ loadcell for handle and brake, ⑥ electrodes (white square), ⑦ pressure sensor for seat back, and ⑧ brake.

Grahic Jump Location
Figure 3

Comparison of joint torques at elbow (upper), knee (middle), and ankle joint (lower) among simulation with initial muscle parameters (◻), with measured values by experiment (▨) and with optimized muscle parameters (◼). Optimized muscle parameters gave us more reasonable results compared with those of experiments rather than initial or unoptimized ones.

Grahic Jump Location
Figure 4

Comparison between measured activities’ ratio with calculated ones for the height of 0.9 m and 1.0 m (equivalent impact velocities were 9.4 m/h and 10 m/h, respectively) to validate musculoskeletal models using optimized muscle parameters for low-speed crash. Muscle activity ratio was defined by extensor activity divided by flexor activity.

Grahic Jump Location
Figure 5

Maximum muscle force for major individual muscles (flexor and extensor) calculated by simulation using three-dimensional musculoskeletal models. Biceps long head and triceps lateralis for elbow joint, biceps femoris long head and rectus femoris for knee joint, and tibialis anterior and gastrocnemius medial for ankle joint.

Grahic Jump Location
Figure 1

Process flow diagram of dynamic analysis including subject-specific musculoskeletal models optimization of muscle parameters and sled experiments. Fm=muscle force, τjt=joint torque, θjt=joint angle, FBC=external forces as boundary conditions, uppercase i: initial, and uppercase o: optimized




Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In