TY - JOUR
T1 - Multi-objective optimization for two-dimensional maximum weight lifting prediction considering dynamic strength
AU - Xiang, Yujiang
AU - Cruz, Jazmin
AU - Zaman, Rahid
AU - Yang, James
N1 - Publisher Copyright:
© 2020 Informa UK Limited, trading as Taylor & Francis Group.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2021
Y1 - 2021
N2 - Manual material handling is common in daily life and is the main cause of lower back pain. Therefore, it is critical to establish a lifting limit for workers. However, it is difficult to obtain each individual's maximum lifting weight through experiments. This study presents a multi-objective optimization (MOO) for two-dimensional maximum weight lifting prediction. Minimizing the dynamic effort (joint torque square) and maximizing the box weight are the two objective functions. Fourteen human subjects were recruited to collect motion and ground reaction force data in the laboratory. Twelve subjects’ data were used to determine cost function weights. The other two subjects’ data were used to validate the best MOO objective function weights through the root mean square errors and Pearson coefficients between the simulated and experimental data. The results show that the proposed MOO method and the best weighting coefficients could improve the accuracy of the simulation.
AB - Manual material handling is common in daily life and is the main cause of lower back pain. Therefore, it is critical to establish a lifting limit for workers. However, it is difficult to obtain each individual's maximum lifting weight through experiments. This study presents a multi-objective optimization (MOO) for two-dimensional maximum weight lifting prediction. Minimizing the dynamic effort (joint torque square) and maximizing the box weight are the two objective functions. Fourteen human subjects were recruited to collect motion and ground reaction force data in the laboratory. Twelve subjects’ data were used to determine cost function weights. The other two subjects’ data were used to validate the best MOO objective function weights through the root mean square errors and Pearson coefficients between the simulated and experimental data. The results show that the proposed MOO method and the best weighting coefficients could improve the accuracy of the simulation.
KW - Dynamic effort
KW - dynamic joint strength
KW - inverse dynamics optimization
KW - maximum lifting weight
KW - multi-objective optimization
UR - http://www.scopus.com/inward/record.url?scp=85078594100&partnerID=8YFLogxK
U2 - 10.1080/0305215X.2019.1702979
DO - 10.1080/0305215X.2019.1702979
M3 - Article
AN - SCOPUS:85078594100
VL - 53
SP - 206
EP - 220
JO - Engineering Optimization
JF - Engineering Optimization
SN - 0305-215X
IS - 2
ER -