Literature DB >> 19012998

Incorporating ultrasound-measured musculotendon parameters to subject-specific EMG-driven model to simulate voluntary elbow flexion for persons after stroke.

L Li1, K Y Tong, X L Hu, L K Hung, T K K Koo.   

Abstract

BACKGROUND: This study was to extend previous neuromusculoskeletal modeling efforts through combining the in vivo ultrasound-measured musculotendon parameters on persons after stroke.
METHOD: A subject-specific neuromusculoskeletal model of the elbow was developed to predict the individual muscle force during dynamic movement and then validated by joint trajectory. The model combined a geometrical model and a Hill-type musculotendon model, and used subject-specific musculotendon parameters as inputs. EMG signals and joint angle were recorded from healthy control subjects (n=4) and persons after stroke (n=4) during voluntary elbow flexion in a vertical plane. Ultrasonography was employed to measure the muscle optimal length and pennation angle of each prime elbow flexor (biceps brachii, brachialis, brachioradialis) and extensor (three heads of triceps brachii). Maximum isometric muscle stresses of the flexor and extensor muscle group were calibrated by minimizing the root mean square difference between the predicted and measured maximum isometric torque-angle curves. These parameters were then inputted into the neuromusculoskeletal model to predict the individual muscle force using the input of EMG signals directly without any trajectory fitting procedure involved.
FINDINGS: The results showed that the prediction of voluntary flexion in the hemiparetic group using subject-specific parameters data was better than that using cadaveric data extracted from the literature.
INTERPRETATION: The results demonstrated the feasibility of using EMG-driven neuromusculoskeletal modeling with direct ultrasound measurement for the prediction of voluntary elbow movement for both subjects without impairment and persons after stroke.

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Year:  2008        PMID: 19012998     DOI: 10.1016/j.clinbiomech.2008.08.008

Source DB:  PubMed          Journal:  Clin Biomech (Bristol, Avon)        ISSN: 0268-0033            Impact factor:   2.063


  9 in total

1.  Subject-specific knee joint geometry improves predictions of medial tibiofemoral contact forces.

Authors:  Pauline Gerus; Massimo Sartori; Thor F Besier; Benjamin J Fregly; Scott L Delp; Scott A Banks; Marcus G Pandy; Darryl D D'Lima; David G Lloyd
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2.  Determining the Online Measurable Input Variables in Human Joint Moment Intelligent Prediction Based on the Hill Muscle Model.

Authors:  Baoping Xiong; Nianyin Zeng; Yurong Li; Min Du; Meilan Huang; Wuxiang Shi; Guoju Mao; Yuan Yang
Journal:  Sensors (Basel)       Date:  2020-02-21       Impact factor: 3.576

3.  Modeling muscle function using experimentally determined subject-specific muscle properties.

Authors:  J M Wakeling; C Tijs; N Konow; A A Biewener
Journal:  J Biomech       Date:  2021-01-15       Impact factor: 2.712

4.  Change of muscle architecture following body weight support treadmill training for persons after subacute stroke: evidence from ultrasonography.

Authors:  Peng Liu; Yanjun Wang; Huijing Hu; Yurong Mao; Dongfeng Huang; Le Li
Journal:  Biomed Res Int       Date:  2014-03-24       Impact factor: 3.411

5.  Combined Ultrasound Imaging and Biomechanical Modeling to Estimate Triceps Brachii Musculotendon Changes in Stroke Survivors.

Authors:  Le Li; Raymond Kai-Yu Tong
Journal:  Biomed Res Int       Date:  2016-12-08       Impact factor: 3.411

Review 6.  Electrode Size and Placement for Surface EMG Bipolar Detection from the Brachioradialis Muscle: A Scoping Review.

Authors:  Andrea Merlo; Maria Chiara Bò; Isabella Campanini
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7.  Subject-specific tendon-aponeurosis definition in Hill-type model predicts higher muscle forces in dynamic tasks.

Authors:  Pauline Gerus; Guillaume Rao; Eric Berton
Journal:  PLoS One       Date:  2012-08-29       Impact factor: 3.240

8.  EMGD-FE: an open source graphical user interface for estimating isometric muscle forces in the lower limb using an EMG-driven model.

Authors:  Luciano Luporini Menegaldo; Liliam Fernandes de Oliveira; Kin K Minato
Journal:  Biomed Eng Online       Date:  2014-04-04       Impact factor: 2.819

9.  Functional and Morphological Changes in the Deep Lumbar Multifidus Using Electromyography and Ultrasound.

Authors:  Shanshan Zhang; Yi Xu; Xiulan Han; Wen Wu; Yan Tang; Chuhuai Wang
Journal:  Sci Rep       Date:  2018-04-25       Impact factor: 4.379

  9 in total

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