Development of Three-Diemnsional Whole-Body Musculoskeletal Model for Various Motion Analyses

Hase, Kazunori; Yamazaki, Nobutoshi

doi:10.1299/jsmec1993.40.25

Cited by 36 publications

(19 citation statements)

References 12 publications

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“…From the joint angles, we can roughly estimate the sensory inputs from the proprioceptors during walking. In addition, activities of aMNs can also be estimated from muscle forces predicted by the model analysis method described in Hase and Yamazaki (1997). By assuming that output of an aMN can be reconstructed by weighted summation of the sensory inputs and CPG signals, we estimate initial values of the neural parameters that possibly lie in the vicinity of the locomotion-generating region in the parameter space.…”

Section: Learning Neural Parametersmentioning

confidence: 99%

“…where D is the distance traveled until the model falls down, n is the number of steps generated, and P is the speci®c power (calculated by the muscle energy consumption model in Hase and Yamazaki 1997). In the early stage of learning, not even one step of walking is generated because it learns to minimize the energy consumption of tumbling.…”

Section: Objective Functionmentioning

confidence: 99%

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Generation of human bipedal locomotion by a bio-mimetic neuro-musculo-skeletal model

2001

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To emulate the actual neuro-control mechanism of human bipedal locomotion, an anatomically and physiologically based neuro-musculo-skeletal model is developed. The human musculo-skeletal system is constructed as seven rigid links in a sagittal plane, with a total of nine principal muscles. The nervous system consists of an alpha motoneuron and proprioceptors such as a muscle spindle and a Golgi tendon organ for each muscle. At the motoneurons, feedback signals from the proprioceptors are integrated with the signal induced by foot-ground contact and input from the rhythm pattern generator; a muscle activation signal is produced accordingly. Weights of connection in the neural network are optimized using a genetic algorithm, thus maximizing walking distance and minimizing energy consumption. The generated walking pattern is in remarkably good agreement with that of actual human walking, indicating that the locomotory pattern could be generated automatically, according to the musculoskeletal structures and the connections of the peripheral nervous system, particularly due to the reciprocal innervation in the muscle spindles. Using the proposed model, the flow of sensory-motor information during locomotion is estimated and a possible neuro-control mechanism is discussed.

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Section: Learning Neural Parametersmentioning

confidence: 99%

Section: Objective Functionmentioning

confidence: 99%

Generation of human bipedal locomotion by a bio-mimetic neuro-musculo-skeletal model

2001

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“…1 and Table I. Ligament stiffness summarised by Wei, et al [25] based on published data is used in this study and passive muscle force was calculated based on muscle length with respect to neutral length [26].…”

Section: A Model Formulationmentioning

confidence: 99%

A robot-driven computational model for estimating passive ankle torque with subject-specific adaptation

Zhang

Meng

Davies

et al. 2015

IEEE Trans. Biomed. Eng.

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Article:Zhang, M, Meng, W, Davies, TC et al. (2 more authors) (2016) A robot-driven computational model for estimating passive ankle torque with subject-specific adaptation. IEEE Transactions on Biomedical Engineering, 63 (4). pp. [814][815][816][817][818][819][820][821] https://doi.org/10.1109/TBME.2015.2475161 © 2015 IEEE. This is an author produced version of a paper published in IEEE Transactions on Biomedical Engineering. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Uploaded in accordance with the publisher's self-archiving policy.eprints@whiterose.ac.uk https://eprints.whiterose.ac.uk/ Reuse Unless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version -refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher's website. TakedownIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. TBME-00615-2015.R1 1  Abstract-Background: Robot-assisted ankle assessment could potentially be conducted using sensor-based and model-based methods. Existing ankle rehabilitation robots usually use t o r q u e m e t e r s a n d m u l t i -a x i s l o a d c e l l s f o r m e a s u r i n g j o i n t dynamics. These measurements are accurate, but the contribution as a result of muscles and ligaments is not taken into account. Some computational ankle models have been developed to evaluate ligament strain and joint torque. These models do not include muscles, and thus are not suitable for an overall ankle assessment in robot-assisted therapy. Methods: This study proposed a computational ankle model for use in robot-assisted therapy with three rotational degrees of freedom (DOFs), 12 muscles and seven ligaments. This model is driven by robotics, uses three independent position variables as inputs, and outputs an overall ankle assessment. Subject-specific adaptations by geometric and strength scaling were also made to allow for a universal model. Results: This model was evaluated using published results and experimental data from 11 participants. Results show a high accuracy in the evaluation of ligament neutral length and passive joint torque. The subject-specific adaptation performance is hi...

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“…Komura has proposed a method to convert a captured motion dynamically and physiologically in order to add the physiological effects to motion for creating human motion animation [3]. Hase has proposed a three-dimensional musculoskeletal model for the analysis of motions such as walking and rowing [6], [7], [8]. Nakamura has proposed a somatosensory computation model based on motion capture data [4].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Bio-Signal based on Human Motion for Integrated Visualization of Daily-Life

Umetani¹,

Matsukawa²,

Yokoyama³

2007

2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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This paper describes a method for the estimation of bio-signals based on human motion in daily life for an integrated visualization system. The recent advancement of computers and measurement technology has facilitated the integrated visualization of bio-signals and human motion data. It is desirable to obtain a method to understand the activities of muscles based on human motion data and evaluate the change in physiological parameters according to human motion for visualization applications. We suppose that human motion is generated by the activities of muscles reflected from the brain to bio-signals such as electromyograms. This paper introduces a method for the estimation of bio-signals based on neural networks. This method can estimate the other physiological parameters based on the same procedure. The experimental results show the feasibility of the proposed method.

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Development of Three-Diemnsional Whole-Body Musculoskeletal Model for Various Motion Analyses

Cited by 36 publications

References 12 publications

Generation of human bipedal locomotion by a bio-mimetic neuro-musculo-skeletal model

Generation of human bipedal locomotion by a bio-mimetic neuro-musculo-skeletal model

A robot-driven computational model for estimating passive ankle torque with subject-specific adaptation

Estimation of Bio-Signal based on Human Motion for Integrated Visualization of Daily-Life

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