Cost function evaluation for optimizing design and actuation of an active exoskeleton to ergonomically assist lifting motions

Harant, Monika; Millard, Matthew; Šarabon, Nejc; Mombaur, Katja

doi:10.1109/humanoids43949.2019.9035028

Cited by 6 publications

(6 citation statements)

References 19 publications

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“…The forward dynamics model simulates human posture based on the reference joint angle 𝑞𝑞 r 𝑗𝑗 . For simulating human posture, reference kinematics are typically set based on experimental data (Nasr et al, 2022;Harant et al, 2019). However, this method is unsuitable for developing a passive lower-limb assistive device because it cannot optimize the wearer's physical posture and is also unsuitable for improving existing devices (e.g., eliminating a forward-leaning posture that increases physical load).…”

Section: Optimizationmentioning

confidence: 99%

“…However, an experimental analysis is unsuitable for the trial stage, in which a range of conditions are examined, due to the high costs associated with prototyping and experimentation. Model-based analysis methods have thus been developed for the design of wearable assistive devices (Nasr et al, 2022;Harant et al, 2019). These methods reduce the cost of development by enabling a computational analysis of the interactions between a human model and a device model, which facilitates the design of optimal device structures.…”

Section: Introductionmentioning

confidence: 99%

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Forward dynamics simulation for analyzing interactions with passive lower-limb assistive devices via optimization of physical posture

HARAGUCHI,

HASE

2024

JBSE

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Forward dynamics simulations can significantly reduce the development costs associated with new wearable assistive devices by minimizing the need for prototyping and experimentation. However, current model-based methods rely on experimental data to define the model posture for the simulation, making it challenging to design the wearer's posture suitable for reducing physical load with passive lower-limb assistive devices. To address this challenge, this study proposes a forward dynamics simulation method that focuses on optimizing physical posture. This simulation approach calculates human-device interactions, including reaction forces, physical posture, and physical load, based on the physical posture optimized by a cost function that evaluates both the physical load and the suitability of the posture for work, which contributes to estimating the impact of the device on the physical load of the wearer. To investigate the validation and advantage of the proposed simulation, this study compared the simulation and experimental results for existing and new passive lower-limb assistive devices. The results show that although the proposed method did not accurately simulate the individual humandevice interactions for a given participant using the passive lower-limb assistive device, it accurately estimated the average reaction forces and lower limb posture across all participants. Additionally, the proposed method correctly represented the differences in reaction forces and lower limb posture resulting from changes in device structure. Consequently, the proposed simulation has advantages in computationally evaluating the device's performance in reducing the lower limb loads and contributes to the development of effective assistive devices for preventing musculoskeletal disorders.

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Section: Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Forward dynamics simulation for analyzing interactions with passive lower-limb assistive devices via optimization of physical posture

HARAGUCHI,

HASE

2024

JBSE

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“…Lifting motion plays a paramount role in our daily lives and has been found to be one key incentive to pain and fatigue in the human neck and upper limbs ( Mombaur et al, 2019 ). So far, it has been discovered that human lifting behavior complies with an optimal pattern regulated by the central nervous system (CNS) ( Chang et al, 2001 ; Xiang et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Cost Function Determination for Human Lifting Motion via the Bilevel Optimization Technology

Tang

Peng

Luo

et al. 2022

Front. Bioeng. Biotechnol.

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Investigating the optimal control strategy involved in human lifting motion can provide meritorious insights on designing and controlling wearable robotic devices to release human low-back pain and fatigue. However, determining the latent cost function regarding this motion remains challenging due to the complexities of the human central nervous system. Recently, it has been discovered that the underlying cost function of a biological motion can be identified from an inverse optimization control (IOC) issue, which can be handled via the bilevel optimization technology. Inspired by this discovery, this work is dedicated to studying the underlying cost function of human lifting tasks through the bilevel optimization technology. To this end, a nested bilevel optimization approach is developed by integrating particle swarm optimization (PSO) with the direction collocation (DC) method. The upper level optimizer leverages particle swarm optimization to optimize weighting parameters among different predefined performance criteria in the cost function while minimizing the kinematic error between the experimental data and the result predicted by the lower level optimizer. The lower level optimizer implements the direction collocation method to predict human kinematic and dynamic information based on the human musculoskeletal model inserted into OpenSim. Following after a benchmark study, the developed method is evaluated by experimental tests on different subjects. The experimental results reveal that the proposed method is effective at finding the cost function of human lifting tasks. Thus, the proposed method could be regarded as a paramount alternative in the predictive simulation of human lifting motion.

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“…The few optimal control studies that have been done to predict new lifting motions (Xiang and Arefeen, 2020 ) do not combine human and exoskeleton models. The limited amount of work that includes an exoskeleton (Millard et al, 2017 ; Harant et al, 2019 ), however, does not consider the effects of training, nor risk factors related to low-back injury. In addition, no simulation work has been found that examines the effect of lifting technique on the risk of low-back injury.…”

Section: Introductionmentioning

confidence: 99%

Comparing the risk of low-back injury using model-based optimization: Improved technique versus exoskeleton assistance

et al. 2021

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Although wearable robotic systems are designed to reduce the risk of low-back injury, it is unclear how effective assistance is, compared to improvements in lifting technique. We use a two-factor block study design to simulate how effective exoskeleton assistance and technical improvements are at reducing the risk of low-back injury when compared to a typical adult lifting a box. The effects of assistance are examined by simulating two different models: a model of just the human participant, and a model of the human participant wearing the SPEXOR exoskeleton. The effects of lifting technique are investigated by formulating two different types of optimal control problems: a least-squares problem which tracks the human participant’s lifting technique, and a minimization problem where the model is free to use a different movement. Different lifting techniques are considered using three different cost functions related to risk factors for low-back injury: cumulative low-back load (CLBL), peak low-back load (PLBL), and a combination of both CLBL and PLBL (HYB). The results of our simulations indicate that an exoskeleton alone can make modest reductions in both CLBL and PLBL. In contrast, technical improvements alone are effective at reducing CLBL, but not PLBL. The largest reductions in both CLBL and PLBL occur when both an exoskeleton and technical improvements are used. While all three of the lifting technique cost functions reduce both CLBL and PLBL, the HYB cost function offers the most balanced reduction in both CLBL and PLBL.

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Cost function evaluation for optimizing design and actuation of an active exoskeleton to ergonomically assist lifting motions

Cited by 6 publications

References 19 publications

Forward dynamics simulation for analyzing interactions with passive lower-limb assistive devices via optimization of physical posture

Forward dynamics simulation for analyzing interactions with passive lower-limb assistive devices via optimization of physical posture

Cost Function Determination for Human Lifting Motion via the Bilevel Optimization Technology

Comparing the risk of low-back injury using model-based optimization: Improved technique versus exoskeleton assistance

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