Development of Models to Predict Optimal Lifting Motion
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1995/08/01
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By Ayoub MM
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Series: Grant Final Reports
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Description:The goal of this study was to continue efforts in the development of a reliable and realistic dynamic lifting simulation model. Specific objectives were to develop a simulation method to predict the sagittal plane lifting motion to validate the simulation method on both male and female subjects, and to perform sensitivity analysis to study the effect of input errors and changes in model parameters on the performance of the simulation method. As developed, the model simulated the dynamic motion of lifting tasks for five body joints: the elbow, shoulder, hip, knee, and ankle. The inputs of the model included initial and final joint postures: sex, weight, and height of the subject; weight load; lifting height; and box dimensions. The angular trajectories of the five joints were predicted on output. The motion of the lift was completely predicted by the model without any video inputs. The simulation model contained three computation units, the dynamics computation, the trajectory formulation and the optimization unit. Study results indicated that the simulation motion followed the actual motion closely. Sensitivity analysis indicated that the optimization constraints had significant effects on the prediction performance of the simulation model. [Description provided by NIOSH]
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Pages in Document:1-268
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NIOSHTIC Number:nn:00235384
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NTIS Accession Number:PB97-156681
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Citation:Institute for Ergonomics Research, Department of Industrial Engineering, Texas Tech University, Lubbock, Texas 1995 Aug; :1-268
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Contact Point Address:Industrial Engineering Texas Tech University P O Box 4130 Lubbock, Tex 79409
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Federal Fiscal Year:1995
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Performing Organization:Texas Tech University, Lubbock, Texas
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Peer Reviewed:False
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Start Date:19880101
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Source Full Name:Institute for Ergonomics Research, Department of Industrial Engineering, Texas Tech University, Lubbock, Texas
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End Date:19950831
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Main Document Checksum:urn:sha-512:29e190851e7c69fb03d32b0679bf8b26e3b02e65177a0e31738e1dcfb4cd4b67106696361f5044168bf14823aeddaaa49d9e97066288a68bf1ef14e377c9f339
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