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Recognizing the importance of both the torque-angle and torque-velocity relations, three-dimensional (3D) human strength capabilities (i.e., peak torque as a function of both joint angle and movement velocity) have been increasingly reported. It is not clear, however, the degree to which these surfaces vary between joints, particularly between joints with similar biomechanical configurations. Thus, our goal was to compare 3D strength surfaces between the muscles about the elbow and knee hinge joints in men and women. Peak isometric and isokinetic strength was assessed in 54 participants (30 men) using the Biodex System 3 isokinetic dynamometer. Normalized peak torque surfaces varied significantly between flexion and extension (within each joint) and between joints; however, the normalized 3D torque surfaces did not differ between men and women. These findings suggest the underlying joint biomechanics are the primary influences on these strength surface profiles. Therefore, in applications such as digital human modeling, torque-velocity-angle relationships for each joint and torque direction must be uniquely represented to most accurately estimate human strength capability.

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SUMMARYPosture prediction plays an important role in product design and manufacturing. There is a need to develop a more efficient method for predicting realistic human posture. This paper presents a method based on multi-objective optimization (MOO) for kinematic posture prediction and experimental validation. The predicted posture is formulated as a multi-objective optimization problem. The hypothesis is that human performance measures (cost functions) govern how humans move. Twelve subjects, divided into four groups according to different percentiles, participated in the experiment. Four realistic in-vehicle tasks requiring both simple and complex functionality of the human simulations were chosen. The subjects were asked to reach the four target points, and the joint centers for the wrist, elbow, and shoulder and the joint angle of the elbow were recorded using a motion capture system. We used these data to validate our model. The validation criteria comprise R-square and confidence intervals. Various physics factors were included in human performance measures. The weighted sum of different human performance measures was used as the objective function for posture prediction. A two-domain approach was also investigated to validate the simulated postures. The coefficients of determinant for both within-percentiles and cross-percentiles are larger than 0.70. The MOO-based approach can predict realistic upper body postures in real time and can easily incorporate different scenarios in the formulation. This validated method can be deployed in the digital human package as a design tool.

Although they are powerful and successful in many applications, artificial neural networks (ANNs) typically do not perform well with complex problems that have a limited number of training cases. Often, collecting additional training data may not be feasible or may be costly. Thus, this work presents a new radial-basis network (RBN) design that overcomes the limitations of using ANNs to accurately model regression problems with minimal training data. This new design involves a multi-stage training process that couples an orthogonal least squares (OLS) technique with gradient-based optimization. New termination criteria are also introduced to improve accuracy. In addition, the algorithms are designed to require minimal heuristic parameters, thus improving ease of use and consistency in performance. The proposed approach is tested with experimental and practical regression problems, and the results are compared with those from typical network models. The results show that the new design demonstrates improved accuracy with reduced dependence on the amount of training data. As demonstrated, this new ANN provides a platform for approximating potentially slow but high-fidelity computational models, and thus fostering inter-model connectivity and multi-scale modeling.

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