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The main objective of this work is to study the degree of engagement levels of human subjects in completing a series of tasks based on their physiological signals. The tasks are in the form of tracking a set of trajectories on the computer screen by using a mouse. . The subjects are chosen randomly based on both sexes, aged from 20 to 40 years old. After completing the required tasks in a series of experiments, the subjects are given a set of questions to answer In particular, for the experiment, the subjects are asked to track a set of prescribed paths within the allocated times in order to adhere to different speed constraints. Various shapes of trajectories with various colours are given to the subjects in order to study the degree of engagements while performing the tasks. In estimating the degree of engagement level, the physiological signals; namely the electrooculogram (EOG) are recorded by using the Gtec data acquisition system. By using these signals, information on the endogenous type of eye blinking is extracted. After completing the experiments, a set of questionnaires are given to the subjects to measure the degree of engagement level. The questionnaire works as an important tool to validate the engagement model deduced from the experiment done earlier by the subjects. Preliminary analysis on the questionnaire shows a good match to the deduction made from the experimental results.

Every normal-born human have five fingers connected to each of the hands. These fingers have their own specific role that contributes to different hand functions. Among the five fingers, the thumb plays the most special function as an anchor to many of hand activities such as turning a key, gripping a ball and holding a spoon for eating. As a result, the lost of thumb due to traumatic accidents could be catastrophic as proper hand function will be severely limited. In order to solve this problem, a prosthetic thumb can be worn to complement the function of the rest of the fingers. In this work the relationship between the electromyography (EMG) and thumb tip force is investigated in order to develop a more natural controlled prosthetic thumb. The signals are measured from the thumb intrinsic muscles namely the Adductor Pollicis (AP), Flexor Pollicis Brevis (FPB), Abductor Pollicis Brevis (APB) and First Dorsal Interosseous (FDI). Meanwhile the thumb tip force is recorded by using the force sensor (FSR). The relationship between the EMG signals to the thumb-tip force is established by using Artificial Neural Network (ANN). A series of experiments have been conducted and preliminary results show the efficacy of ANN to capture the relationship model. Keyword: Electromyograpgy (EMG) signal, thumb-tip force, force sensor (FSR)I.

In this paper, new method is proposed for a more robust Data Assimilation (DA) design of the river flow and stage estimation. By using the new sets of data that are derived from the incorporated Multi Imputation Particle Filter (MIPF) in the DA structure, the proposed method is found to have overcome the issue of missing observation data and contributed to a better estimation process. The convergence analysis of the MIPF is discussed and shows that the number of the particles and imputation influence the ability of this method to perform estimation. The simulation results of the MIPF demonstrated the superiority of the proposed approach when being compared to the Extended Kalman Filter (EKF) and Particle Filter (PF).