Human lifting simulation using a multi-objective optimization approach

Xiang, Yujiang; Arora, Jasbir S.; Rahmatalla, Salam; Marler, Timothy; Bhatt, R. K.; Abdel‐Malek, Karim

doi:10.1007/s11044-009-9186-y

Cited by 90 publications

(75 citation statements)

References 15 publications

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“…In this approach the Lagrangian of system is calculates, then by extermizing the integral of the lagrangian of the system, the equations which govern the dynamics of motion will be obtained. The kinetic energy of the model which was presented before [28] is defines as (3).…”

Section: Inverse Dynamicsmentioning

confidence: 99%

“…This assumption is implemented in some biomechanical researches to simulate the CNS action to predict the muscle forces and limb motions [1][2][3]19]. The predictive dynamics is a novel approach used for the motion simulation [27,28]; it implements inverse dynamic equations as a major constraint to model the motion's dynamics in the simulation process. The joints torques and angles are the designed variables, so by using this method the joint angles and torques could obtain as outputs, according to the task parameters which are used as inputs.…”

Section: Optimization-based Simulationmentioning

confidence: 99%

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What Strategy Central Nervous System Uses to Perform a Movement Balanced? Biomechatronical Simulation of Human Lifting

Shoushtari

2013

Applied Bionics and Biomechanics

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Abstract. How does the central nervous system control the body posture during various tasks despite a redundancy? It's a well-known question existed in such fields of study as biomechanics and bioengineering. Some techniques based on muscle and torques synergies are presented to study the function which Central Nervous System uses to addresses the kinetic redundancy in musculoskeletal system. The human body with its whole numerous joints considered as a hyper redundant structure which caused to be seemed that it is impossible for CNS to control and signal such system. To solve the kinematic redundancy in previous studies it is hypothesize that CNS functions as an optimizer, such of that are the task-based algorithms which search to find optimal solution for each specific task. In this research a new objective function based on ankle torques during movement is implemented to guarantee the stability of motion. A 2D 5DOF biomechatronical model of human body is subjected to lifting task simulation. The simulation process implements inverse dynamics as major constraint to consider the dynamics of motion for predicted postures. In the previous optimization-based techniques which are used to simulate the human movements, the motion stability was guaranteed by a nonlinear inequality constraint which restricts the total moment arm of the links to an upper and lower boundary. In this method, there is no need to use this constraint. The results show that the simulated postures are normal and the predicted motion is performed completely balanced.

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Section: Inverse Dynamicsmentioning

confidence: 99%

Section: Optimization-based Simulationmentioning

confidence: 99%

What Strategy Central Nervous System Uses to Perform a Movement Balanced? Biomechatronical Simulation of Human Lifting

Shoushtari

2013

Applied Bionics and Biomechanics

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“…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%

“…The control results from this study involved two simulations solved by sequential quadratic programming. [13] and [14] concentrated on the human lifting simulation. In these studies, a multi-objective optimization approach was proposed to predict dynamic lifting for the human lifting simulation using a three dimensional human model [13].…”

Section: Introductionmentioning

confidence: 99%

“…[13] and [14] concentrated on the human lifting simulation. In these studies, a multi-objective optimization approach was proposed to predict dynamic lifting for the human lifting simulation using a three dimensional human model [13]. In [13], the simulation results illustrate that the proposed approach, with two objective functions as the dynamic effort and stability, is good, but less sensitive to the stability criterion.…”

Section: Introductionmentioning

confidence: 99%

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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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Towards Human and Robot Collaborative Ergonomic Handling of Long Parts with a Loose Grip

Hofmann¹,

Ganglbauer²,

Propst³

et al. 2021

IFIP Advances in Information and Communication Technology

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The concept of robots collaborating with humans has gained a lot of interest in the recent years. The pure use of the robot to follow the human’s actions involves several important components, which have to fullfil economic, ergonomic, safety and usability aspects. This paper presents a setup build with recent hardware and taking economic, safety and usability aspects into account. A concept is introduced how the ergonomics of a human worker is actively influenced by the system.

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Human lifting simulation using a multi-objective optimization approach

Cited by 90 publications

References 15 publications

What Strategy Central Nervous System Uses to Perform a Movement Balanced? Biomechatronical Simulation of Human Lifting

What Strategy Central Nervous System Uses to Perform a Movement Balanced? Biomechatronical Simulation of Human Lifting

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

Towards Human and Robot Collaborative Ergonomic Handling of Long Parts with a Loose Grip

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