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We developed a method for use of accelerometers and force sensing resistors (FSRs) within an optimal controller of walking for hemiplegic individuals. The data from four dual-axis accelerometers and four FSRs were inputs, while six muscle activation profiles were outputs. The controller includes two stages: 1) estimating the target gait pattern using artificial neural networks; and 2) optimal control minimizing tracking errors (from the estimated gait pattern) and muscle efforts. The controller was tested using data collected from six healthy subjects walking at five speeds (0.6-1.4 m/s). The average root mean square errors (RMSEs) normalized by the peak-to-peak value of the target signals [normalized RMSE (NRMSE)] were below 6%, 7%, 8%, and 3% for estimation of joint angles, hip acceleration, ground reaction force, and movement of the center of pressure, respectively. Using the estimated data as inputs, the simulation generated the target healthy-like gait patterns and reproducible muscle activation profiles in 90% of 300 tested gait trials. Overall tracking NRMSE was between 2% and 9%. The optimal controller was developed for testing the feasibility of healthy-like gait patterns in hemiplegic individuals, and generating a knowledge base that is required for the synthesis of a sensory-driven control of walking assisted by functional electrical stimulation.