Simulation model of general human and humanoid motion

Vukobratović, Miomir; Potkonjak, Veljko; Babković, Kalman; Borovać, Branislav

doi:10.1007/s11044-006-9034-2

Cited by 53 publications

(23 citation statements)

References 10 publications

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“…A simulation model of general human and humanoid motions has been presented in the literature for dynamical modeling. It has been verified with two sports examples [9]. In [10], an experimental validation was given for controlling humanoid robots using human motion data.…”

Section: Introductionmentioning

confidence: 90%

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An Improved Nonstationary Fuzzy System Approach versus Type-2 Fuzzy System for the Lifting Motion Control with Human-in-the-Loop Simulation

Karaköse¹

2018

IJCIS

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People working in the fields of robotics, animation, computer graphics and computer vision, and biomechanics, find it difficult to conduct human motion simulations, such as lifting, walking and running. This is because it is difficult to predict all of the motion strategies in a variety of situations. The human lifting motion is hard work; at the same time, lifting is the most demanded robotic motion. In this paper, a fuzzy integral based nonstationary fuzzy inference system is proposed to control a five-segment human model for the human lifting motion with human-inthe-loop simulation. This system was designed to reduce the computational complexity of nonstationary fuzzy systems. Hence, this paper contributes to the literature in two ways: as an improved nonstationary fuzzy system and as a human lifting motion simulation framework. A fuzzy integral algorithm between the fuzzification and defuzzification stages has been used to aggregate the inference outputs of the nonstationary fuzzy controller. The fuzzy integral algorithm uses fuzzy values obtained during the fuzzification stage as the attribute values and the fuzzy values obtained by a one-loop quantum particle swarm optimization algorithm as the importance values. The computational complexity in the nonstationary fuzzy systems and type-2 fuzzy systems can be reduced between 25 to 60 percent with the improved nonstationary fuzzy system. An experimental application of the human lifting motion was carried out to demonstrate the effectiveness of the proposed approach. The results illustrated that the proposed algorithm can achieve increased simplicity, improved effectiveness, good robustness, and a higher precision of computation.

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Section: Introductionmentioning

confidence: 90%

“…As such, the control of this motion is an unstable and ill-posed system, and hence, the importance of human lifting motion prediction strategies are a popular topic in the literature [9][10][11][12][13][14]. A simulation model of general human and humanoid motions has been presented in the literature for dynamical modeling.…”

Section: Introductionmentioning

confidence: 99%

An Improved Nonstationary Fuzzy System Approach versus Type-2 Fuzzy System for the Lifting Motion Control with Human-in-the-Loop Simulation

Karaköse¹

2018

IJCIS

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“…We checked also the other cost functions reported in (23) and (24), in each case observing considerable sensitivity of the results to the cost function chosen. Finally, we tested the pseudoinverse scheme reported in (25) together with the post-processing procedure to keep all the muscle stresses within the boundary constrains σ min ≤ σ ≤ σ max . The results were identical to those obtained using the Crowninshied and Brand cost function for p = 2, which is in accordance with the remarks given in [1].…”

Section: Optimization Methodsmentioning

confidence: 99%

“…While the first coordinate type is commonly used in biomechanical system modeling [1][2][3][24][25][26], implementations of the other coordinates in biomechanics are rather rare [5,12,[27][28][29]. By comparing the m7: simulation results obtained using these two mathematical models, one can easily eliminate possible defects in the models.…”

Section: Mathematical Modelingmentioning

confidence: 99%

Influence of selected modeling and computational issues on muscle force estimates

Blajer

Czaplicki²,

Dziewiecki

et al. 2010

Multibody Syst Dyn

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Knowledge of muscle forces and joint reaction forces during human movement can provide insight into the underlying control and tissue loading. Since direct measurement of the internal loads is generally not feasible, non-invasive methods based on musculoskeletal modeling and computer simulations have been extensively developed. By applying observed motion data to the musculoskeletal models, inverse dynamic analysis allow to determine the resultant joint torques, transformed then into estimates of individual muscle forces by means of different optimization procedures. Assessment of the joint reaction forces and other internal loads is further possible. Comparison of the muscle force estimates obtained for different modeling assumptions and parameters in the model can be valuable for the improvement of validity of the model-based estimations. The present study is another contribution to this field. Using a sagittal plane model of an upper limb with a weight carried in hand, and applying the data of recorded flexion and extension movement of the upper limb, the resultant muscular forces are predicted using different modeling assumptions and simulation tools. This study relates to different coordinates (joint and natural coordinates) used to built the mathematical model, muscle path modeling, muscle decomposition (change in number of the modeled muscles), and different optimization methods used to share the joint torques into individual muscles.

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“…Other authors [4][5][6][7][8][9][10] increased the number of elements or expanded mathematical procedures.…”

Section: Measuring Forces At Lower Extremity Joints During Gaitmentioning

confidence: 99%

Inverse Dynamics and Artificial Neural Network Applications in Gait Analysis of the Disabled Subjects

Wit¹,

Czaplicki²

2008

Human Movement

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The aim of this study is to review the achievements of the mathematical modeling of muscles force contribution during walking. In order to determine the contributions of individual muscles to the net force or net muscle torque at a given joint, the external forces acting on lower extremity joints during gait ought to be identified. The solution of this problem, called in biomechanics the inverse dynamics, is now regarded as a classical method of movement modelling. In the hypothesis put forward in this paper, it is considered if the artificial neural network method could be applied to the muscle contraction prediction during gait analysis in normal and disabled subjects. Artificial neural network (ANN) is an artificial intelligence method used in mathematical modelling and its applications in diverse areas, especially in biology and medicine, are steadily progressing. The achievements and possibilities of ANN in biomechanics were presented previously by others authors. For example, Liu and Lockhart [13] attempted at creating a network capable of reproducing muscle forces during gait from EMG signals recorded in working muscles. The objective of our study was to make use of the experience gained in the construction of ANN and to apply advanced mathematical procedures to identical experimental conditions of gaint analysis.

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Simulation model of general human and humanoid motion

Cited by 53 publications

References 10 publications

An Improved Nonstationary Fuzzy System Approach versus Type-2 Fuzzy System for the Lifting Motion Control with Human-in-the-Loop Simulation

An Improved Nonstationary Fuzzy System Approach versus Type-2 Fuzzy System for the Lifting Motion Control with Human-in-the-Loop Simulation

Influence of selected modeling and computational issues on muscle force estimates

Inverse Dynamics and Artificial Neural Network Applications in Gait Analysis of the Disabled Subjects

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