Prediction of human reach posture using a neural network for ergonomic man models

For proper evaluation of operator's usability through ergonomic man models, accurate prediction of human reach is one of the essential functions that those models should possess. This study examined the applicability of artificial neural networks to the prediction of human reach posture. The three-dimensional motion trajectories of the joints of upper limb (shoulder, elbow, and wrist) in the right arm from 5 percentile female to 95 percentile male were obtained through a motion analysis system that photographed actual human reach. The data obtained were divided into two data sets — training data set and test data set. The backpropagation method being usually used for a pattern associator was employed as a tool for predicting such human movements. Comparisons between prediction and real measurements were made using a pairwise t-test, and no significant differences were found between the two data sets for all the joints considered. Thus, the neural network approach adopted in this study showed a very promising prediction capability of human reach and it is, therefore, expected that this method be used to accurately simulate human reach better than existing heuristic or analytic methods as well as to improve a human modelling capability in general.