Rubita Sudirman

The use of graphic rules in grade one to help identify children at risk of handwriting difficulties.

Previous researches on elementary grade handwriting revealed that pupils employ certain strategy when writing or drawing. The relationship between this strategy and the use of graphic rules has been documented but very little research has been devoted to the connection between the use of graphic rules and handwriting proficiency. Thus, this study was conducted to investigate the relative contribution of the use of graphic rules to the writing ability. A sample of 105 first graders who were average printers and 65 first graders who might experience handwriting difficulty, as judged by their teachers, of a normal primary school were individually tested on their use of graphic rules. It has been found that pupils who are below average printers use more non-analytic strategy than average printers to reproduce the figures. The results also reveal that below average printers do not acquire the graphic principles that foster an analytic approach to production skills. Although the findings are not sufficient to allow definitive conclusions about handwriting ability, it can be considered as one of the screening measures in identifying pupils who are at risk of handwriting difficulties.

Wheelchair Motion Control Guide Using Eye Gaze and Blinks Based on PointBug Algorithm

In this paper, we propose new method beside the classic method, to control the motorized wheelchair using EOG signals. The new method allows the user to look around freely while the wheelchair navigates automatically to the desired goal point. Only EOG signals are used to control the wheelchair; eye gazing and blinking. The user can still choose to control the wheelchair using the classic manual method in case the environment and obstacles structure does not help with the auto navigation method. In the new auto navigation method the microcontroller can know the goal point direction and distance by calculating the gaze angle that the user is gazing at. PoingBug algorithm is used to navigate the wheelchair in Auto controlling method. Simulated results are similar to Tangent Bug algorithm results, but experimental tests are slightly improved in some cases where the surroundings have sharp edges.

A wavelet approach on energy distribution of eye movement potential towards direction

This study presents an investigation in classification of Electrooculography (EOG) signals of eye movement potentials. In recent years, the classification of EOG signals by using the FFT is computationally efficient in means and has been a good considerable in research effort. In the quest to improve the accuracy of the EOG signals classification, a method of time-frequency based analysis is proposed. The EOG signals are captured using electrodes placed on the forehead around the eyes to record the eye movements. The wavelet features are used to determine the characteristic of eye movement waveform. EOG signals are captured using the Neurofax EEG-9200. The recorded data is composed of an eye movement toward four directions (upward, downward, left and right). The proposed analysis for each eyes signal is analyzed by using Wavelet Transform (WT) by comparing the energy distribution with the change of time and frequency of a signal. A wavelet scalogram is plotted to display the different percentages of energy for each wavelet coefficient towards different movement. From the result, it is proved that the different EOG signals exhibit differences in signals energy with their corresponding level such as left with level 6 (8–16Hz), right with level 8 (2–4Hz), downward with level 7 (4–8Hz) and upward with level 9 (1–2Hz).

Gaze at Desired Destination, and Wheelchair Will Navigate towards It. New Technique to Guide Wheelchair Motion Based on EOG Signals

In this work, we propose new method beside the classic method, to control the motorized wheelchair using EOG signals. The new method allows the user to look around freely while the wheelchair navigates automatically to the desired goal point. Only EOG signals are used to control the wheelchair, eye gazing and blinking. The user can still choose to control the wheelchair using the classic manual method in case the environment and obstacles structure does not help with the auto navigation method. In the new auto navigation method the micro controller can know the goal point direction and distance by calculating the gaze angle that the user is gazing at. Tangent Bug algorithm is used to navigate the wheelchair in Auto controlling method. Experimental results are similar to simulated with minimum error, due to minimal positioning and sensing errors.

EEG different frequency sound response identification using neural network and fuzzy techniques

Electroencephalographic (EEG) technology has enabled effective measurement of human brain activity, as functional and physiological changes within the brain may be registered by EEG signals. In this paper, electrical activity of human brain due to sound waves of different frequency, i.e. 40 Hz, 500 Hz, 5000 Hz and 15000 Hz, is studied based on EEG signals. Several signal processing techniques, i.e. Principle Component algorithm, Discrete Wavelet Transform and Fast Fourier Transform, are applied onto the raw EEG signal to extract useful information and specific characteristics from the EEG signals. This research has shown that the characteristics of EEG signals differ with respect to different frequency of sound waves, and hence the EEG signal can be identified with suitable characterization algorithm using artificial intelligent techniques, such as Artificial neural network, fuzzy logic and adaptive neuro-fuzzy system.

EVENT-RELATED POTENTIALS EXTRACTION OF WORKING MEMORY USING WAVELET ALGORITHM

This study was designed to classify and determine t he Event-Related Potentials (ERPs) signal pattern o f normal children on visual response. Thirty-eight ch ildren aged between 10 to 12 years old were subject ed to a two-phase computer-based assessment while their working memory activity was recorded using a NeurofaxEEG 9200 machine. For children, it is anticipated t hat some information can be lost when there is too much information given at any one time due to limited me mory capacity and this is a type of memory impairment. Based on the visual stimulus responses, EEG signal were recorded and captured from channel location at Fz. This paper explains the extraction of raw EEG signa ls into grand mean ERPs signal which to determine t he pattern of signal developed. The ERPs concerning la tency and amplitude variability of the P300 component was evaluated. The analysis was based on Discrete Wavelet Transform (DWT) algorithm and focused on alpha rhythm. Results indicated that the Daubechies wavel et at a decomposition level of 4 (db4) was the most suitable wavelet for pre-processing raw EEG signal of working memory. A significant increase of latenc y was detected in children aged 10 to 12 years old at cha nnel Fz (frontal midline) when the visual stimuli b ecame more difficult. For amplitude variability, the girl s gave higher amplitude at Phase 1. These results s upported the concept of increased cognitive memory in childr en.

Identification of working memory impairments in normal children using wavelet approach

The aim of this study was to explore the working memory of children through three assessments of remembering pictures and on mathematical skills. This was done by identifying whether normal right-handed children use their left brain to stimulate the task and investigating the early stages of working memory problem in children. For children, working memory has its limits where the child will lose some information when there is too much information. If that is the case, then the child is known as having impairment in his or her working memory. In this study, the acquisition of working memory data was recorded using Neurofax EEG 9200. The EEG signals were captured using Neuroband electrodes placed at the pre-frontal cortex area of the head. The three assessments were analyzed using Wavelet Transform. Parameter extraction for mean and standard deviation value at the four channels F3, F4, F7 and F8 were averaged and the result showed different increment and decrement for the entire practical assessments. The values of mean at the consequent assessment for Phase I were male=2.25 and female=2.56; Phase II were male=5.23 and female=2.17; and for assessment on mathematical skills, the values were male=2.32 and female=2.54. The result shows that normal children have a good working memory.

Characterization of Ventricular Arrhythmias in Electrocardiogram Signal Using Semantic Mining Algorithm

Ventricular arrhythmias, especially ventricular fibrillation, is a type of arrhythmias that can cause sudden death. The paper applies semantic mining approach to electrocardiograph (ECG) signals in order to extract its significant characteristics (frequency, damping coefficient and input signal) to be used for classification purpose. Real data from an arrhythmia database are used after noise filtration. After features extraction they are statistically classified into three groups, i.e. normal (N), normal patients (PN) and patients with ventricular arrhythmia (V). We found that the V, PN, and N types of ECG signals can be identified by the extracted parameters. It is estimated that the parameters in semantic algorithm can be use to predict the onset of ventricular arrhythmias.

Pre-processing of input features using LPC and warping process

This paper presents pre-processing of input features to artificial neural network (NN). This is for preparation of reliable reference templates for the set of words to be recognized. The first task is to extract pitch features using Pitch Scale Harmonic Filter (PSHF) algorithm. Another task is to align the input frames (test set) to the reference template (training set) using a modified DTW algorithm called DTW fixing frame (DTW-FF) algorithm. This proper time normalization is needed since NN is designed to compare data of the same length; same speech can varies in their duration. By performing frame fixing or time normalization, the test set and the training set is adjusted to a fix number of frames throughout the sets utilizing the local distance score of the matched features. Then those features can be adapted to NN for further recognition tuning.

Wheelchair motion control guide using eye gaze and blinks based on bug 2 algorithm

In this work, we propose a new approach alongside the typical method, to control the motorized wheelchair using EOG signals. The new approach grants the user to look around without restraint, while the wheelchair navigates automatically to the desired goal point. Only EOG signals are used to control the wheelchair; eye gazing and blinking. The user can still appoint to control the wheelchair using the typical manual method in case the surroundings and obstacle structures do not assist with the new developed auto navigation method. In the new auto navigation approach the microcontroller can attain the goal point direction and distance by calculating the gaze angle of the user. Bug2 algorithm is utilized to navigate the wheelchair in the auto controlling method. Experimental tests show slightly different results than theory, because the bug algorithms cannot continuously update the robots position data in experiments.