New neural network for real-time human dynamic motion prediction
iii ACKNOWLEDGMENTS I would like to thank my research advisor, Dr. Timothy Marler, for his endless directions and contributions to present my ideas fruitfully and clearly. I am especially grateful to my academic advisor and mentor, Professor Karim Abdel-Malek for his enthusiastic support and directions to work on exciting and appropriate topics. In addition, I would like to thank my other thesis committee members for their time and valuable feedback. I would also like to thank Melanie Laverman
... k Melanie Laverman for the help in editing my thesis chapters, and all the group at the Virtual Soldier Research program for their efforts. Finally, above all, I am grateful to the God (firstly and lastly) and to my family for their endless smiles, confidence, and support though the past five years of my life. iv ABSTRACT Artificial neural networks (ANNs) have been used successfully in various practical problems. Though extensive improvements on different types of ANNs have been made to improve their performance, each ANN design still experiences its own limitations. The existing digital human models are mature enough to provide accurate and useful results for different tasks and scenarios under various conditions. There is, however, a critical need for these models to run in real time, especially those with large-scale problems like motion prediction which can be computationally demanding. For even small changes to the task conditions, the motion simulation needs to run for a relatively long time (minutes to tens of minutes). Thus, there can be a limited number of training cases due to the computational time and cost associated with collecting training data. In addition, the motion problem is relatively large with respect to the number of outputs, where there are hundreds of outputs (between 500-700 outputs) to predict for a single problem. Therefore, the aforementioned necessities in motion problems lead to the use of tools like the ANN in this work. This work introduces new algorithms for the design of the radial-basis network (RBN) for problems with minimal available training data. The new RBN design incorporates new training stages with approaches to facilitate proper setting of necessary network parameters. The use of training algorithms with minimal heuristics allows the new RBN design to produce results with quality that none of the competing methods have achieved. The new RBN design, called Opt_RBN, is tested on experimental and practical problems, and the results outperform those produced from standard regression and ANN models. In general, the Opt_RBN shows stable and robust performance for a given set of training cases. v When the Opt_RBN is applied on the large-scale motion prediction application, the network experiences a CPU memory issue when performing the optimization step in the training process. Therefore, new algorithms are introduced to modify some steps of the new Opt_RBN training process to address the memory issue. The modified steps should only be used for large-scale applications similar to the motion problem. The new RBN design proposes an ANN that is capable of improved learning without needing more training data. Although the new design is driven by its use with motion prediction problems, the consequent ANN design can be used with a broad range of large-scale problems in various engineering and industrial fields that experience delay issues when running computational tools that require a massive number of procedures and a great deal of CPU memory. The results of evaluating the modified Opt_RBN design on two motion problems are promising, with relatively small errors obtained when predicting approximately 500-700 outputs. In addition, new methods for constraint implementation within the new RBN design are introduced. Moreover, the new RBN design and its associated parameters are used as a tool for simulated task analysis. This work initiates the idea that output weights ( ) can be used to determine the most critical basis functions that cause the greatest reduction in the network test error. Then, the critical basis functions can specify the most significant training cases that are responsible for the proper performance achieved by the network. The inputs with the most change in value can be extracted from the basis function centers ( ) in order to determine the dominant inputs. The outputs with the most change in value and their corresponding key body degrees-of-freedom for a motion task can also be specified using the training cases that are used to create the network's basis functions. vi PUBLIC ABSTRACT Research in the field of human simulation has led to significant advancement in quality, time, and cost management for products like military and athletic equipment and vehicles. There is, however, a critical need for human simulation models to run in real time, especially those with large-scale problems like motion prediction (a single motion problem involves prediction of between 500-700 outputs). Hence, this thesis addresses that need by developing a new design of artificial neural network (ANN) that is capable of providing real-time motion results with maximum accuracy and minimal training. The success of the new ANN design is proven for the intended problem of motion simulation and other experimental and real-world problems. In addition, the design creates a new tool for the analysis of the task being simulated. The new implemented ANN algorithms will open a new area of advancement and capability in the digital human modeling field. Although the new ANN design is driven by its use with motion prediction problems, the consequent ANN design can be used with a broad range of large-scale problems. The motion problem is simply a well-studied example problem for the proposed developments. The new ANN design can be populated to be used for applications in various large-scale engineering and industrial fields that experience delay issues when running computational tools that require a massive number of procedures and a great deal of CPU memory. vii 220.127.116.11 Network output(s) as initial guess (NOIG) .