Implementation of fuzzy logic controller for wheelchair motion control based on EOG data

The study of this paper is to implementation the fuzzy logic control designed for wheelchair motion based on the eye movement signals using electrooculograhphy (EOG) technique. This technique is to acquire the eye movement data from a person, for example, tetraplegia. The tetraplegia is paralysis caused by illness or injury to a human that result in the partial or total loss of use of all their limbs and torso. The eye movement data which was obtained can be used as a main communication tool between human and machine. The PD-type fuzzy controller was successfully designed and tested on the wheelchair model, for control the linear motion (focused for forward motion). The wheelchair model was developed using MSC.Visual Nastran 4D. The results obtained show that the PD-type fuzzy logic controller designed has successfully managed to track the input reference for linear motion set by the EOG signal

Analysis of different level of EOG signal from eye movement for wheelchair control

This paper is aimed to analyze different levels of eye movement signals strength using Electrooculography (EOG). The eye movement that is known to be a significant communication tool for a tetraplegia, can be defined as a paralysis that is caused by serious injuries or illness to a human that lead to a partial or total loss of their lower limb and torso. A person who has such paralysis is highly dependent on an assistant and a wheelchair for movement. It is not always the case where the helper is with the patient all the time, therefore independence is encouraged among the wheelchair users. The signal from the eye muscles that is called electrooculogram is generated at different eye movements’ directions and levels. The eye movement signals are acquired using g.USBamp from G.TEC Medical Engineering GMBH by using Ag/AgCl electrodes. The data is then passed to MATLAB/SIMULINK software for data analysis. Different directions and strength level of eye movement are fed to a virtual wheelchair model developed in MSC.Visual Nastran 4D software to study the effect of the signals on the distance and rotation travelled by the wheelchair. Simulation exercises has verified that different strength of eye movement signals levels that have been processed could be manipulated for helping tetraplegia in their mobility using the wheelchair

Hubungan antara banyaknya kuman (CFU/ML) dari bakteriuria dengan leukosit dari leukosituria pada pasien pasca sectio caesarea setelah kateterisasi di Bagian Obstetri dan Ginekologi Rumah Sakit Umum Daerah (RSUD) Ulin Banjarmasin

Urinary Tract Infection (UTI) is a pathologic phenomenon indicated by the presence of bacteria with a high number of CFU/ml. One of the causative measures is the constant catheterization of hospitalized patient. UTI happens easily to pregnant women whose urine will promote the growth of bacteria. The laboratory examination of UTI is carried out to observe the occurcace of bacteria and leucocytes. This study was carried out to see increases in number of bacteria in bacteriuria and number of leucocytes in leucocyturia in caesarean section patients following catheterization. The aim of this study was to examine the correlation between the number of bacteria in bacteriuria and number of lecocytes in leucocyturia from urine obtained after catheterization post caesarean section patients in the Department of Obstetrics and Gynecology of RSUD Ulin Banjarmasin during the periods of June 1st – July 31st, 2004. A descriptive method was used and regression as well as non parametric testing procedure of linear correlation were employed. The results showed a strong degree of correlation between the number of bacteria in bacteriuria and the number of leucocyte in leucocyturia after 1x24 hours (r = 0, 51089) and after 2x24 hours (r = 0, 74647). The regression analysis strongly revealed the increase number of leucocytes in leucocyturia along with the increase number of bacteria in bacteriuria

Design and development of eye movement data acquisition kit

There are several researches that have been done to improve the life among tetraplegia. It has become an attractive research field in the rehabilitation engineering because the eye movement have abilities as a communication tool for disabled people. However, the previous research has not much provided an appropriate design for the user among tetraplegia. Motivated from that, a new design of eye movement data acquisition kit has been developed. This paper aims to describe the design of eye movement data acquisition kit for the user among tetraplegia based on the proper electrode positions, the prototype as well as the signal conditioning circuits. Then, this EOG kit was used to acquire the eye signals for eye movement in the left and right direction. The eye movement data was obtained from the kit, which can be used as a significant communication tool among tetraplegia. The results show that the kit equipped with a proper signal conditioning is able to acquire the eye movement signal

Parametric modelling of pedal pressing activities during road traffic delay

Traffic congestion in big cities in Malaysia has become a common scenario among the communities. The journey between homes to working place twice a day at considerable distances is no longer a strange situation. Being in traffic for hours in a sitting position requires recurrent tasks of manual pressing the pedal and brake excessively and if they are done without the correct sitting posture, it may trigger fatigue faster, particularly for the leg and back of the driver. In the long term, it will negatively affect the health of the driver, particularly in the form of physical, psychological, and emotional. Therefore, this paper is trying to investigate the recurrent brake pedal pressings as well as the leg postures while driving in traffic jam. The research is started with the experimental setup and data acquisition on brake pedal pressing as well as leg posture followed by the modelling and analysis of the obtained data using particle swarm optimization (PSO) modelling technique. The validation step was then executed to verify the model derived using open-loop and closed-loop performance analysis. The results show that the pedal pressing force of leg posture can be closely represented using 2nd order transfer function and mimics the actual pedal pressing pattern during road traffic delay

Performance analysis of an electrooculography-based on intelligent wheelchair motion control

The aim of this study is to analyse the performance of fuzzy logic-based control designed for a wheelchair motion control using the eye movement signals. These signals are acquired through electrooculography (EOG) technique. The EOG is a technique to acquire the eye movement signals from a person, i.e tetraplegia, which the data obtained can be used as a main communication tool, for example in wheelchair motion control. In this project, the eye movement signals were classified using the fuzzy classifier (FC). Then, the PD-type fuzzy controller was successfully designed and tested on the wheelchair model, for wheelchair motion control. The wheelchair model system was developed using MSC. Visual Nastran. The eye movement signals that acquired through the EOG technique is acted as a motion input references. The simulation results obtained show that the PD-type fuzzy logic controller designed has successfully managed to track the input reference for linear motion set by the EOG signal. In this paper, the simulation results are focused for backward motion only

Pedoman akademik pascasarjana

x, 283 hlm., ill., 24cm

Fuzzy-based classifier design for determining the eye movement data as an input reference in wheelchair motion control

Fuzzy logic is widely used in many complex and nonlinear systems for control, system identification and pattern recognition problems. The fuzzy logic controller provides an alternative to the PID controller which is a good tool for control of systems that are difficult to model. In this paper, the fuzzy-based classifiers were designed in order to determine the eye movement data. These data were used as an input reference in wheelchair motion control. Then, a set of an appropriate fuzzy classification (FC) was designed based on the numerical data from eye movement data acquisitions that obtained from the electrooculogram (EOG) technique. Each fuzzy rule (FR) for this system is based on the form of IF-THEN rule. Since membership functions (MFs) are generated automatically, the proposed fuzzy learning algorithm can be viewed as a knowledge acquisition tool for classification problems. The experimental results on eye movement data were presented to demonstrate the contribution of the proposed approach for generating MFs using MATLAB simulink for linear motion in forward direction

Simulation analysis of different strength levels of EOG signals

This paper described the signal from the eye muscles that is called electrooculogram which is generated at different eye movement’s directions and levels. The eye movement signal data is captured by using Al/AgCl electrodes and passed through the g.USBamp from G.TEC Medical Engineering GMBH eye movement data collection. The eye movement data is passed to the Matlab/Simulink software for data analysis. The electrooculography (EOG) signals are obtained from four (4) different places around eye (right, left, up, and down) with different level of strengths will produce different strength of EOG signal. The EOG data obtained could be very helpful in many applications such as in the field of rehabilitation for example as a communication tool to operate a wheelchair among tetraplegia