Hybrid Predictive Model for Lifting by Integrating Skeletal Motion Prediction With an OpenSim Musculoskeletal Model

Zaman, Rahid; Xiang, Yujiang; Rakshit, Ritwik; Yang, James

doi:10.1109/tbme.2021.3114374

Cited by 16 publications

(14 citation statements)

References 56 publications

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“…To reduce the complexity and improve the computational speed, Zaman et al 55,75 integrated the skeletal model prediction with musculoskeletal model analysis. They used a 10-DOF 2D skeletal model to predict symmetric lifting motion, GRFs, and center of pressure (COP).…”

Section: Human Modeling For Liftingmentioning

confidence: 99%

“…It took about 0.62 s of CPU time to predict the symmetric lifting motion for a 12 kg box using a 2D skeletal model with 10 DOFs. 55 In another study, 42 a similar hybrid optimization formulation was utilized to predict asymmetric lifting motion. The 3D skeletal model has 40 DOFs.…”

Section: Human Modeling For Liftingmentioning

confidence: 99%

“…Based on Denavit-Hartenberg method, 49 the recursive Newton-Euler method 48 and the recursive Lagrangian method 21,[50][51][52][53][54][55] were utilized to solve for the EOM of the biomechanical system. For 2D human models, the EOM can be directly given in matrix form, whereas for 3D human models, the EOM should be derived in a systematic way.…”

Section: Equations Of Motion (Eom) and Sensitivity Analysismentioning

confidence: 99%

“…36 The time-consuming process of these musculoskeletal models makes it difficult to implement in real-time. Therefore, recently some researchers developed hybrid models 55,75 to reduce the computational cost. At first, the hybrid models use the skeletal model to predict the motion.…”

Section: Hybrid Modelmentioning

confidence: 99%

See 3 more Smart Citations

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

Zaman

Arefeen

Quarnstrom

et al. 2022

Proc Inst Mech Eng H

Self Cite

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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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Section: Human Modeling For Liftingmentioning

confidence: 99%

Section: Human Modeling For Liftingmentioning

confidence: 99%

Section: Equations Of Motion (Eom) and Sensitivity Analysismentioning

confidence: 99%

Section: Hybrid Modelmentioning

confidence: 99%

See 2 more Smart Citations

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

Zaman

Arefeen

Quarnstrom

et al. 2022

Proc Inst Mech Eng H

Self Cite

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show abstract

“…H UMAN movements encompass complex interactions of the neuromuscular system [1]. As a powerful omputational simulation tool, musculoskeletal model can be applied for detailed biomechanical analysis to understand these interactions, which would be beneficial to various applications ranging from evaluating rehabilitation treatment [2], enhancing performance of athlete [3], [4], optimising robotic design for impaired individuals [5], to surgical planning and intervention [6].…”

Section: Introductionmentioning

confidence: 99%

Physics-informed Deep Learning for Musculoskeletal Modelling: Predicting Muscle Forces and Joint Kinematics from Surface EMG

Zhang¹,

Zhao²,

Shone³

et al. 2022

Preprint

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Musculoskeletal models have been widely used for detailed biomechanical analysis to characterise various functional impairments given their ability to estimate movement variables (i.e., muscle forces and joint moment) which cannot be readily measured in vivo. Physics-based computational neuromusculoskeletal models can interpret the dynamic interaction between neural drive to muscles, muscle dynamics, body and joint kinematics and kinetics. Still, such set of solutions suffers from slowness, especially for the complex models, hindering the utility in real-time applications. In recent years, datadriven methods has emerged as a promising alternative due to the benefits in speedy and simple implementation, but they cannot reflect the underlying neuromechanical processes. This paper proposes a physics-informed deep learning framework for musculoskeletal modelling, where physics-based domain knowledge is brought into the data-driven model as soft constraints to penalise/regularise the data-driven model. We use the synchronous muscle forces and joint kinematics prediction from surface electromyogram (sEMG) as the exemplar to illustrate the proposed framework. Convolutional neural network (CNN) is employed as the deep neural network to implement the proposed framework. At the same time, the physics law between muscle forces and joint kinematics is used the soft constraint. Experimental validations on two groups of data, including one benchmark dataset and one self-collected dataset from six healthy subjects, are performed. The experimental results demonstrate the effectiveness and robustness of the proposed framework.

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3D biomechanical model that can perform dynamic analysis of the upper extremity and L5/S1 joints without the use of force sensor

Yanikören,

Yilmaz,

Gündoğdu

2024

Sādhanā

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Hybrid Predictive Model for Lifting by Integrating Skeletal Motion Prediction With an OpenSim Musculoskeletal Model

Cited by 16 publications

References 56 publications

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

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

Physics-informed Deep Learning for Musculoskeletal Modelling: Predicting Muscle Forces and Joint Kinematics from Surface EMG

3D biomechanical model that can perform dynamic analysis of the upper extremity and L5/S1 joints without the use of force sensor

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