Optimization-Based Posture Prediction for Analysis of Box Lifting Tasks

Marler, Timothy; Knake, Lindsey A.; Johnson, Ross

doi:10.1007/978-3-642-21799-9_17

Cited by 21 publications

(13 citation statements)

References 16 publications

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“…3D models can overcome the barriers that 2D models have. Ma et al 58 and Marler et al 57 used a 3D model for posture prediction. Ma et al 58 investigated fatigue effects during MMH tasks.…”

Section: Human Modeling For Liftingmentioning

confidence: 99%

Optimization-based biomechanical lifting models for manual material handling: A comprehensive review

Zaman

Arefeen

Quarnstrom

et al. 2022

Proc Inst Mech Eng H

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Lifting is a main task for manual material handling (MMH), and it is also associated with lower back pain. There are many studies in the literature on predicting lifting strategies, optimizing lifting motions, and reducing lower back injury risks. This survey focuses on optimization-based biomechanical lifting models for MMH. The models can be classified as two-dimensional and three-dimensional models, as well as skeletal and musculoskeletal models. The optimization formulations for lifting simulations with various cost functions and constraints are reviewed. The corresponding equations of motion and sensitivity analysis are briefly summarized. Different optimization algorithms are utilized to solve the lifting optimization problem, such as sequential quadratic programming, genetic algorithm, and particle swarm optimization. Finally, the applications of the optimization-based lifting models to digital human modeling which refers to modeling and simulation of humans in a virtual environment, back injury prevention, and ergonomic safety design are summarized.

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“…3D models can overcome the barriers that 2D models have. Ma et al 58 and Marler et al 57 used a 3D model for posture prediction. Ma et al 58 investigated fatigue effects during MMH tasks.…”

Section: Human Modeling For Liftingmentioning

confidence: 99%

Optimization-based biomechanical lifting models for manual material handling: A comprehensive review

Zaman

Arefeen

Quarnstrom

et al. 2022

Proc Inst Mech Eng H

View full text Add to dashboard Cite

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“…and 2) maximum joint torque (Marler et al, 2011;Liu et al, 2009). Generally, joint torque represents the force of a group of muscles acting on or around a joint, and the torque limits represent strength.…”

Section: Joint-based Performance Measures (Pms)mentioning

confidence: 99%

“…There is an external load of 100 N applied on Santos's right hand, which is the hand reaching toward the target point. Two performance measures are used to optimize Santos's posture prediction problem in Equation 5.1: joint displacement and maximum joint torques in weights of 25% and 75%, respectively (Marler et al, 2011).…”

Section: Posture Prediction For Touching a Point Without External Loadmentioning

confidence: 99%

“…Other conditions on this task are: 1) using two performance measures (joint displacement and maximum torques), 2) active balance constraint (zero moment point), and 3) frozen hip and lower body. The zero moment point condition is responsible for providing only postures with balance; any imbalanced postures are infeasible solutions (Marler et al, 2011). Training cases were collected that they cover all reachable points on the front side of the body, which was similar to the space shown in Figure 5.1.…”

Section: Definitionmentioning

confidence: 99%

“…For example, minimizing the joint displacement PM alone was useful for touching points in front of the body, but it provided bad postures for points behind the body; it should combine with other PMs like potential energy. Maximum joint torque PM was used to provide realistic postures in box-lifting studies (Marler et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

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Artificial neural network for studying human performance

Bataineh¹

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The vast majority of products and processes in industry and academia require human interaction. Thus, digital human models (DHMs) are becoming critical for improved designs, injury prevention, and a better understanding of human behavior. Although many capabilities in the DHM field continue to mature, there are still many opportunities for improvement, especially with respect to posture-and motion-prediction. Thus, this thesis investigates the use of artificial neural network (ANN) for improving predictive capabilities and for better understanding how and why human behave the way they do. With respect to motion prediction, one of the most challenging opportunities for improvement concerns computation speed. Especially, when considering dynamic motion prediction, the underlying optimization problems can be large and computationally complex. Even though the current optimization-based tools for predicting human posture are relatively fast and accurate and thus do not require as much improvement, posture prediction in general is a more tractable problem than motion prediction and can provide a test bead that can shed light on potential issues with motion prediction. Thus, I investigate the use of ANN with posture prediction in order to discover potential issues. In addition, directly using ANN with posture prediction provides a preliminary step towards using ANN to predict the most appropriate combination of performance measures (PMs)-what drives human behavior. The PMs, which are the cost functions that are minimized in the posture prediction problem, are typically selected manually depending on the task. This is perhaps the most significant vi impediment when using posture prediction. How does the user know which PMs should be used? Neural networks provide tools for solving this problem. This thesis hypothesizes that the ANN can be trained to predict human motion quickly and accurately, to predict human posture (while considering external forces), and to determine the most appropriate combination of PM(s) for posture prediction. Such capabilities will in turn provide a new tool for studying human behavior. Based on initial experimentation, the general regression neural network (GRNN) was found to be the most effective type of ANN for DHM applications. A semi-automated methodology was developed to ease network construction, training and testing processes, and network parameters. This in turn facilitates use with DHM applications. With regards to motion prediction, use of ANN was successful. The results showed that the calculation time was reduced from 1 to 40 minutes, to a fraction of a second without reducing accuracy. With regards to posture prediction, ANN was again found to be effective. However, potential issues with certain motion-prediction tasks were discovered and shed light on necessary future development with ANNs. Finally, a decision engine was developed using GRNN for automatically selecting four human PMs, and was shown to be very effective. In order to train this new approach, a novel optimization for...

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