TY - JOUR
T1 - Hybrid Predictive Model for Lifting by Integrating Skeletal Motion Prediction with an OpenSim Musculoskeletal Model
AU - Zaman, Rahid
AU - Xiang, Yujiang
AU - Rakshit, Ritwik
AU - Yang, James
N1 - Funding Information:
Manuscript received March 10, 2021; revised August 25, 2021; accepted September 13, 2021. Date of publication September 22, 2021; date of current version February 21, 2022. This research was supported in part by projects from NSF (Award # 1849279 and 1703093). Invaluable assistance and critical input were provided during the design of the experiment by Jazmin Cruz, and during its performance by both Jazmin Cruz and Shadman Tahmid at Texas Tech University. (Corresponding author: Yujiang Xiang.) Rahid Zaman is with the Mechanical and Aerospace Engineering Department, Oklahoma State University, USA.
Funding Information:
This research was supported in part by projects from NSF (Award # 1849279 and 1703093). Invaluable assistance and critical input were provided during the design of the experiment by Jazmin Cruz, and during its performance by both Jazmin Cruz and Shadman Tahmid at Texas Tech University.
Publisher Copyright:
© 1964-2012 IEEE.
PY - 2022/3/1
Y1 - 2022/3/1
N2 - Objective: In this study, a novel hybrid predictive musculoskeletal model is proposed which has both motion prediction and muscular dynamics assessment capabilities. Methods: First, a two-dimensional (2D) skeletal model with 10 degrees of freedom is used to predict a symmetric lifting motion, outputting joint angle profiles, ground reaction forces (GRFs), and center of pressure (COP). These intermediate outputs are input to the scaled musculoskeletal model in OpenSim for muscle activation and joint reaction load analysis. Finally, the experimental validation is carried out. Results: Static Optimization tool is used to estimate the muscle activation data in OpenSim for the predicted lifting motion. Joint reaction forces of the lumbosacral joint (L5-S1) are generated using the OpenSim Joint Reaction analysis tool. The predicted joint angles, muscle activations, and peak joint reaction forces are compared with experimental data and data from literature to validate the hybrid model. Conclusion: The proposed hybrid model combines the skeletal model's rapid motion prediction with OpenSim's complex muscular dynamics assessment, and it can serve as a new generic tool for motion prediction and injury analysis in ergonomics and biomechanics.
AB - Objective: In this study, a novel hybrid predictive musculoskeletal model is proposed which has both motion prediction and muscular dynamics assessment capabilities. Methods: First, a two-dimensional (2D) skeletal model with 10 degrees of freedom is used to predict a symmetric lifting motion, outputting joint angle profiles, ground reaction forces (GRFs), and center of pressure (COP). These intermediate outputs are input to the scaled musculoskeletal model in OpenSim for muscle activation and joint reaction load analysis. Finally, the experimental validation is carried out. Results: Static Optimization tool is used to estimate the muscle activation data in OpenSim for the predicted lifting motion. Joint reaction forces of the lumbosacral joint (L5-S1) are generated using the OpenSim Joint Reaction analysis tool. The predicted joint angles, muscle activations, and peak joint reaction forces are compared with experimental data and data from literature to validate the hybrid model. Conclusion: The proposed hybrid model combines the skeletal model's rapid motion prediction with OpenSim's complex muscular dynamics assessment, and it can serve as a new generic tool for motion prediction and injury analysis in ergonomics and biomechanics.
KW - Hybrid model
KW - Lifting
KW - Motion prediction
KW - Musculoskeletal model
KW - OpenSim
KW - Skeletal model
UR - http://www.scopus.com/inward/record.url?scp=85115717814&partnerID=8YFLogxK
U2 - 10.1109/TBME.2021.3114374
DO - 10.1109/TBME.2021.3114374
M3 - Article
C2 - 34550877
AN - SCOPUS:85115717814
SN - 0018-9294
VL - 69
SP - 1111
EP - 1122
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
IS - 3
ER -