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17 results on '"Zaman, Rahid"'

13. Multi-objective optimization for two-dimensional maximum weight lifting prediction considering dynamic strength.

Author

Xiang, Yujiang, Cruz, Jazmin, Zaman, Rahid, and Yang, James

Subjects
WEIGHT lifting, LIFTING & carrying (Human mechanics), STANDARD deviations, LUMBAR pain, MATERIALS handling
Abstract

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. [ABSTRACT FROM AUTHOR]

Published
2021
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17. Development of a predictive musculoskeletal hybrid model based on maximum weight lifting task

Author

Zaman, Rahid

Abstract

Manual material handling (MMH), particularly lifting, is one of the main reasons for work-related joint and back injuries. Injuries associated with MMH play a significant role in the economy. Therefore, it is necessary to determine subject-specific maximum lifting weight and explain why these lifting-related injuries occur. However, it is challenging to determine the maximum lifting weight by experiments as it is a time-consuming process and risky for the participants. Computational biomechanical models can reveal the insight of human lifting motion and help us to find the reasons behind lifting-related injuries. Musculoskeletal models are complicated and computationally heavy, making them infeasible for real-time application. Skeletal models are computationally efficient but lack muscle physiology in their formulations. This makes it unable to assess musculoskeletal injuries. A novel hybrid model is introduced in this study to predict maximum lifting weight and lifting motion and to evaluate musculoskeletal injury for that lifting motion. The hybrid predictive model consists of a predictive skeletal module and an Opensim musculoskeletal module to balance the computational speed and physiological accuracy. The skeletal models predict joint torques, joint angle profiles, center of pressure (COP), and ground reaction forces (GRFs) for both symmetric and asymmetric liftings. The predicted joint angles, GRFs and COP are inputted into OpenSim musculoskeletal module to estimate muscle activations and joint reaction forces. The hybrid predictive models are used to analyze joint torques, muscle activations, and lumbar spine joint reaction forces for both symmetric and asymmetric lifting tasks to prevent musculoskeletal injuries. The developed hybrid model is also able to predict maximum lifting weight by using subject-specific dynamic joint strength and assess associated injury risks. The proposed hybrid model is both computationally efficient and generic, and it can be readily applied to other motions as well. The hybrid predictive musculoskeletal model has wide applications for workers’ injury prevention to reduce the risk of musculoskeletal disorders.

Published
2022

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