Ways to improve sport performance become exceptional contemporary interest. Nowadays, many studies use human brain as an input signal include eyes blinking, attention and meditation to control the exchange process. Brain–Computer Interface (BCI) requires generating control signals for external device by analysing the internal brain signal. The objective is to identify the signal of brainwave which gives effect to performance of golfer. The analysis involved the meditation (α) and attention (β) state of different golf players. In this project, the brainwave of golfer’s will be analyzed based on the movement before club strike the ball. EEG signal used to find out the features by using Fast Fourier Transform (FFT). The analysis included three categories of player include beginner, intermediate and professional. Two types of game have been considered which are Par Tee Ireland and Driving Range. The project interfaces MATLAB software with an EEG headset. The data has interpreted in time and frequency domain graph that show different level in an attention (β) state for both games. Brainwave signals indicated players’ performance and lead to better performance. This data benefits increasing the performance of golfer to become the professional golfer by using electroencephalography (EEG) headset in future study.

Parameter estimation is a vital part in constructing the best model of a dynamic system. This paper analyzed the performance of toothbrush rig parameter estimation using different model orders. Parameter estimation process of the system is performed through system identification. The approximate mathematical model that resembles the real system is obtained when the output is measured after loading the input signal. The application of real-coded genetic algorithm (RCGA) is proposed as optimization method in estimating the parameters of dynamic system. The best model is obtained by optimizing the objective function of mean squared errors. The performance is analyzed to get the approximate model of the real system using three different model orders with 10 times analysis for each model. A few criteria have been considered which are the optimization result of objective function, time execution and validation process. The estimated parameters are acceptable and possible to be used for controller development later on. Estimated parameter with model order 3 is chosen as the best model or the dynamic system as it has the highest performance compared to others.

System identification is a method to build a model for a dynamic system from the experimental data. In this paper, optimization technique was applied to optimize the objective function that lead to satisfying solution which obtain the dynamic model of the system. Real-coded genetic algorithm (RCGA) as a stochastic global search method was applied for optimization. Hence, the model of the plant was represented by the transfer function from the identified parameters obtained from the optimization process. For performance analysis of toothbrush rig parameter estimation, there were six different model orders have been considered where each of model order has been analyzed for 10 times. The influence of conventional genetic algorithm parameter - generation gap has been investigated too. The statistical analysis was used to evaluate the performance of the model based on the objective function which is the Mean Square Error (MSE). The validation test-through correlation analysis was used to validate the model. The model of model order 2 is chosen as the best model as it has fulfilled the criteria involved in selecting the accurate model. Generation gap used was 0.5 has shorten the algorithm convergence time without affecting the model accuracy.