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OPTIMIZATION-BASED THREE-DIMENSIONAL SYMMETRIC TOSSING MOTION PREDICTION AND COMPARISON WITH TWO-DIMENSIONAL MODEL.

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Title: OPTIMIZATION-BASED THREE-DIMENSIONAL SYMMETRIC TOSSING MOTION PREDICTION AND COMPARISON WITH TWO-DIMENSIONAL MODEL.
Authors: LEE, SEUNGHUN1 (AUTHOR), YANG, JAMES1 (AUTHOR) james.yang@ttu.edu
Source: Journal of Mechanics in Medicine & Biology. Aug2025, Vol. 25 Issue 6, p1-17. 17p.
Subjects: Prevention of injury, Two-dimensional models, Human mechanics, Industrial safety, Torque measurements
Abstract: Weight tossing is a combined manual material handling task performed in human workplaces. The combined motion reduces labor time but comes at the cost of increased physical strain. The accumulated muscle fatigue may cause injuries, leading to high healthcare expenses. After the pandemic, workplace injuries have been in the spotlight with the trend of rising e-commerce. It is critical to analyze tossing tasks to suggest a solution to prevent injury risks. Tossing motion prediction provides insights into the human biodynamic system without real experimental data compared to traditional experimental-based methods. In addition, motion prediction can provide predicted joint torques needed for the tossing motion, and this information is useful to prevent potential injuries. This paper introduces an optimization-based 3D symmetric motion prediction and compares it with the 2D results. The optimization formulation of the 3D prediction is introduced. Lab experimental data of tossing a sandbag to a target point are collected from a healthy male subject and used to validate the predicted results. Results of the predicted joint angles, torques, and ground reaction forces (GRFs) from the 3D model are compared with those from the 2D model. The pros and cons of the 3D and 2D prediction models are reported, considering the higher computational cost of 3D prediction than the 2D. Finally, the future research directions of tossing motion are summarized. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Mechanics in Medicine & Biology is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: OPTIMIZATION-BASED THREE-DIMENSIONAL SYMMETRIC TOSSING MOTION PREDICTION AND COMPARISON WITH TWO-DIMENSIONAL MODEL.
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  Data: <searchLink fieldCode="DE" term="%22Prevention+of+injury%22">Prevention of injury</searchLink><br /><searchLink fieldCode="DE" term="%22Two-dimensional+models%22">Two-dimensional models</searchLink><br /><searchLink fieldCode="DE" term="%22Human+mechanics%22">Human mechanics</searchLink><br /><searchLink fieldCode="DE" term="%22Industrial+safety%22">Industrial safety</searchLink><br /><searchLink fieldCode="DE" term="%22Torque+measurements%22">Torque measurements</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Weight tossing is a combined manual material handling task performed in human workplaces. The combined motion reduces labor time but comes at the cost of increased physical strain. The accumulated muscle fatigue may cause injuries, leading to high healthcare expenses. After the pandemic, workplace injuries have been in the spotlight with the trend of rising e-commerce. It is critical to analyze tossing tasks to suggest a solution to prevent injury risks. Tossing motion prediction provides insights into the human biodynamic system without real experimental data compared to traditional experimental-based methods. In addition, motion prediction can provide predicted joint torques needed for the tossing motion, and this information is useful to prevent potential injuries. This paper introduces an optimization-based 3D symmetric motion prediction and compares it with the 2D results. The optimization formulation of the 3D prediction is introduced. Lab experimental data of tossing a sandbag to a target point are collected from a healthy male subject and used to validate the predicted results. Results of the predicted joint angles, torques, and ground reaction forces (GRFs) from the 3D model are compared with those from the 2D model. The pros and cons of the 3D and 2D prediction models are reported, considering the higher computational cost of 3D prediction than the 2D. Finally, the future research directions of tossing motion are summarized. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Mechanics in Medicine & Biology is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1142/S0219519424500532
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      – Code: eng
        Text: English
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        PageCount: 17
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      – SubjectFull: Prevention of injury
        Type: general
      – SubjectFull: Two-dimensional models
        Type: general
      – SubjectFull: Human mechanics
        Type: general
      – SubjectFull: Industrial safety
        Type: general
      – SubjectFull: Torque measurements
        Type: general
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      – TitleFull: OPTIMIZATION-BASED THREE-DIMENSIONAL SYMMETRIC TOSSING MOTION PREDICTION AND COMPARISON WITH TWO-DIMENSIONAL MODEL.
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            NameFull: LEE, SEUNGHUN
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            NameFull: YANG, JAMES
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            – D: 01
              M: 08
              Text: Aug2025
              Type: published
              Y: 2025
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