Hybrid musculoskeletal model-based 3D asymmetric lifting prediction and comparison with symmetric lifting

被引:0
|
作者
Xiang, Yujiang [1 ,3 ]
Zaman, Rahid [1 ]
Arefeen, Asif [1 ]
Quarnstrom, Joel [1 ]
Rakshit, Ritwik [2 ]
Yang, James [2 ]
机构
[1] Oklahoma State Univ, Sch Mech & Aerosp Engn, Stillwater, OK USA
[2] Texas Tech Univ, Dept Mech Engn, Human Centr Design Res Lab, Lubbock, TX USA
[3] Oklahoma State Univ, Sch Mech & Aerosp Engn, 201 Gen Acad Bldg, Stillwater, OK 74078 USA
基金
美国国家科学基金会;
关键词
Lifting; asymmetric lifting; motion prediction; lower back injuries; hybrid model; musculoskeletal injuries; OpenSim; BIOMECHANICAL SIMULATION; MOTION PREDICTION; OPTIMIZATION; STRENGTH; DESIGN; VELOCITY; SYSTEMS; TORQUE; ANGLE; KNEE;
D O I
10.1177/09544119231172862
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
In this study, a 3D asymmetric lifting motion is predicted by using a hybrid predictive model to prevent potential musculoskeletal lower back injuries for asymmetric lifting tasks. The hybrid model has two modules: a skeletal module and an OpenSim musculoskeletal module. The skeletal module consists of a dynamic joint strength based 40 degrees of freedom spatial skeletal model. The skeletal module can predict the lifting motion, ground reaction forces (GRFs), and center of pressure (COP) trajectory using an inverse dynamics-based motion optimization method. The musculoskeletal module consists of a 324-muscle-actuated full-body lumbar spine model. Based on the predicted kinematics, GRFs and COP data from the skeletal module, the musculoskeletal module estimates muscle activations using static optimization and joint reaction forces through the joint reaction analysis tool in OpenSim. The predicted asymmetric motion and GRFs are validated with experimental data. Muscle activation results between the simulated and experimental EMG are also compared to validate the model. Finally, the shear and compression spine loads are compared to NIOSH recommended limits. The differences between asymmetric and symmetric liftings are also compared.
引用
收藏
页码:770 / 781
页数:12
相关论文
共 50 条
  • [21] Autonomous Model-Based Inspection Planning for 3D Visual Coverage Tasks
    Tuerk, Nehemia
    Strand, Marcus
    Rettig, Oliver
    INTELLIGENT AUTONOMOUS SYSTEMS 18, VOL 1, IAS18-2023, 2024, 795 : 243 - 255
  • [22] ADAPTIVE LIFTING SCHEME-BASED METHOD FOR JOINT CODING 3D-STEREO IMAGES WITH LUMINANCE CORRECTION AND OPTIMIZED PREDICTION
    Darazi, Rony
    Gouze, Annabelle
    Macq, Benoit
    2009 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS 1- 8, PROCEEDINGS, 2009, : 917 - 920
  • [23] An initial prediction and fine-tuning model based on improving GCN for 3D human motion prediction
    He, Zhiquan
    Zhang, Lujun
    Wang, Hengyou
    FRONTIERS IN COMPUTATIONAL NEUROSCIENCE, 2023, 17
  • [24] Prediction of Anticancer Peptides with High Efficacy and Low Toxicity by Hybrid Model Based on 3D Structure of Peptides
    Zhao, Yuhong
    Wang, Shijing
    Fei, Wenyi
    Feng, Yuqi
    Shen, Le
    Yang, Xinyu
    Wang, Min
    Wu, Min
    INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES, 2021, 22 (11)
  • [25] 3D Model-Based 6D Object Pose Tracking on RGB Images
    Majcher, Mateusz
    Kwolek, Bogdan
    INTELLIGENT INFORMATION AND DATABASE SYSTEMS (ACIIDS 2020), PT I, 2020, 12033 : 271 - 282
  • [26] 3D Numerical Investigation of Aluminum 2024-T3 Plate Repaired with Asymmetric and Symmetric Composite Patch
    Makwana, Alpesh
    Shaikh, A. A.
    Bakare, A. K.
    Saikrishna, Chitturi
    MATERIALS TODAY-PROCEEDINGS, 2018, 5 (11) : 23638 - 23647
  • [27] Artificial neural networks to predict 3D spinal posture in reaching and lifting activities; Applications in biomechanical models
    Gholipour, A.
    Arjmand, N.
    JOURNAL OF BIOMECHANICS, 2016, 49 (13) : 2946 - 2952
  • [28] Model-based Adaptive Control of a 3D Printed Permanent Magnet Synchronous Motor
    Velarde-Gomez, Sergio
    Molina-Cabrera, Alexander
    Giraldo, Eduardo
    ENGINEERING LETTERS, 2023, 31 (04) : 1804 - 1812
  • [29] Toward Marker-free 3D Pose Estimation in Lifting: A Deep Multi-view Solution
    Mehrizi, Rahil
    Peng, Xi
    Tang, Zhiqiang
    Xu, Xu
    Metaxas, Dimitris
    Li, Kang
    PROCEEDINGS 2018 13TH IEEE INTERNATIONAL CONFERENCE ON AUTOMATIC FACE & GESTURE RECOGNITION (FG 2018), 2018, : 485 - 491
  • [30] Validation of an OpenSim full-body model with detailed lumbar spine for estimating lower lumbar spine loads during symmetric and asymmetric lifting tasks
    Beaucage-Gauvreau, Erica
    Robertson, William S. P.
    Brandon, Scott C. E.
    Fraser, Robert
    Freeman, Brian J. C.
    Graham, Ryan B.
    Thewlis, Dominic
    Jones, Claire F.
    COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING, 2019, 22 (05) : 451 - 464