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A novel memory-based motion simulation (MBMS) model was developed as a general framework for simulating natural human motions for computer-aided ergonomic design. The MBMS model utilizes real human motion samples recorded in motion capture experiments as templates for simulating novel motions. Such human motion samples are stored in a motion database. When a user submits an input simulation scenario to the model, a motion search engine termed the ldquoroot motion finderrdquo in the model searches the motion database and retrieves the motion samples that closely match the given scenario. The retrieved motions, referred to as root motions, may significantly differ from one another in the underlying movement technique. Such variability within the root motion set is analyzed and graphically summarized by a model component termed the motion variability analyzer. This analysis helps users rapidly identify alternative movement techniques for the given input simulation scenario and simulate human motions based on alternative movement techniques. Since root motions do not exactly satisfy but only closely match the input simulation scenario, a motion modification (MoM) algorithm adapts them to fit the scenario by systematically deforming them in the joint angle-time domain. The MoM algorithm retains the root motions' fundamental spatial-temporal structure and minimizes deviations from the root motions during such deformations. The MBMS model overcomes limitations of existing simulation models and achieves the following: 1) simulation of categorically different motions based on a single unified model; 2) simple and efficient learning of new motion behaviors; and 3) representation and simulation of human motion variability.