Open AccessMulti-Objective Optimization Method for Posture Prediction of Symmetric Static Lifting Using a Three-Dimensional Human Model
Author(s) -
Sirous Azizi,
Afsaneh Dadarkhah,
Alireza Asgharpour Masouleh
Publication year - 2020
Publication title -
annals of military and health sciences research
Language(s) - English
Resource type - Journals
eISSN - 2383-1979
pISSN - 2383-1960
DOI - 10.5812/amh.104283
Subject(s) - squat , sagittal plane , task (project management) , computer science , process (computing) , function (biology) , engineering design process , human body model , simulation , artificial intelligence , engineering , physical medicine and rehabilitation , medicine , mechanical engineering , systems engineering , evolutionary biology , biology , radiology , operating system
Background: The development of virtual human models has recently gained considerable attention in biomechanical studies intending to design for ergonomics. The computer-based simulations of virtual human models can reduce the time and cost of the design cycle. There is an increasing interest in finding the realistic posture of the human body with applications in prototype design and reduction of injuries in the workplace. Objectives: This paper presents a generic method based on a multi-objective optimization (MOO) for posture prediction of a sagittal-plane lifting task. Methods: Improved biomechanical models are used to formulate the predicted posture as a MOO problem. The lifting task has been defined by seven performance measures that are mathematically represented by the weighted sum of cost functions. Specific weights are assigned for each cost function to predict both stoop and squat type postures. Some inequality constraints have been used to ensure that the virtual human does not assume a completely unrealistic configuration. Results: The method can predict the hand configuration effectively. Simulations reveal that predicting a squat posture requires the minimization of certain objective functions, while these measures are less significant for the prediction of a stooped posture. Conclusions: In this study, a MOO-based posture prediction model with a validation process is presented. We employed a three-dimensional model to evaluate the applicability of using a combination of seven performance measures to the posture prediction of symmetric lifting tasks. Results have been compared with the available empirical data to validate the simulated postures. Furthermore, the assigned weights are obtained for a range of percentiles from 50% male to 90% female according to the postures obtained by 3D SSPPTM software.