Invariant body kinematics. II: reaching and neurogeometry
Neural Networks
A man-machine interface model for ergonomic design
ICC&IE-94 Selected papers from the 16th annual conference on Computers and industrial engineering
Prediction of human reach posture using a neural network for ergonomic man models
ICC&IE-94 Selected papers from the 16th annual conference on Computers and industrial engineering
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Articular human joint modelling
Robotica
ICDHM'11 Proceedings of the Third international conference on Digital human modeling
Hybrid method for driver accommodation using optimization-based digital human models
Computer-Aided Design
Optimization-based posture reconstruction for digital human models
Computers and Industrial Engineering
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A general methodology and associated computational algorithm for predicting postures of the digital human upper body is presented. The basic plot for this effort is an optimization-based approach, where we believe that different human performance measures govern different tasks. The underlying problem is characterized by the calculation (or prediction) of the human performance measure in such a way as to accomplish a specified task. In this work, we have not limited the number of degrees of freedom associated with the model. Each task has been defined by a number of human performance measures that are mathematically represented by cost functions that evaluate to a real number. Cost functions are then optimized, i.e., minimized or maximized, subject to a number of constraints, including joint limits. The formulation is demonstrated and validated. We present this computational formulation as a broadly applicable algorithm for predicting postures using one or more human performance measures.