Tarmizi Ahmad Izzuddin

JOINT TORQUE ESTIMATION MODEL OF SEMG SIGNAL FOR ARM REHABILITATION DEVICE USING ARTIFICIAL NEURAL NETWORK TECHNIQUES

Rehabilitation device is used as an exoskeleton for peoples who had failure of their limb. Arm rehabilitation device may help the rehab program to who suffered with arm disability. The device is used to facilitate the tasks of the program and minimize the mental effort of the user. Electromyography (EMG) is the techniques to analyze the presence of electrical activity in musculoskeletal systems. The electrical activity in muscles of disable person is failed to contract the muscle for movements. To minimize the used of mental forced for disable patients, the rehabilitation device can be utilize by analyzing the surface EMG signal of normal people that can be implemented to the device. The objective of this work is to model the muscle EMG signal to torque for a motor control of the arm rehabilitation device using Artificial Neural Network (ANN) technique. The EMG signal is collected from Biceps Brachii muscles to estimate the elbow joint torque. A two layer feed-forward network is trained using Back Propagation Neural Network (BPNN) to model the EMG signal to torque value. The performance result of the network is measured based on the Mean Squared Error (MSE) of the training data and Regression (R) between the target outputs and the network outputs. The experimental results show that ANN can well represent EMG-torque relationship for arm rehabilitation device control.

Predicting EMG Based Elbow Joint Torque Model Using Multiple Input ANN Neurons for Arm Rehabilitation

This paper illustrates the Artificial Neural Network (ANN) technique to predict the joint torque estimation model for arm rehabilitation device in a clear manner. This device acts as an exoskeleton for people who had failure of their limb. Electromyography (EMG) is the techniques to analyze the presence of electrical activity in musculoskeletal systems. The electrical activity in muscles of disable person is failed to contract the muscle for movements. In order to prevent the muscles from paralysis becomes spasticity the force of movements should minimize the mental efforts. The objective of this work is to model the muscle EMG signal to torque using ANN technique. The EMG signal is collected from Biceps Brachii muscles to estimate the elbow joint torque. A two layer feed-forward network is trained using Back Propagation Neural Network (BPNN). The experimental results show that ANN can well represent EMG-torque relationship for arm rehabilitation device control.

Pattern recognition of EMG signal during load lifting using Artificial Neural Network (ANN)

This paper describes pattern recognition of electromyography (EMG) signal during load lifting using Artificial Neural Network (ANN). EMG is a method to measure and record the muscle activity when individuals perform certain operation and actions. This research will classify the EMG signal based on force apply to the arm due to the gravity act on it during load lifting. Recognizing pattern based on EMG signal is not an easy task because of the nonlinearities behavior of the signal. It required a good classifier to distinguish each pattern. The motivation of this project is to help the person suffer with hemiparesis to perform daily activities as well as to improve the lifestyle. It is important for patients to realize the hopes of hemiparesis after experiencing their inability to do activity as a normal human. Recognizing EMG pattern is crucially important for rehabilitation control that enables the patients to lift the heavy load despite of their muscle weaknesses. Therefore, a proper analysis of muscle behavior is necessary. The objectives of this research are to extract the important features of EMG signal using time domain analysis and to classify EMG signal based on load lifting using ANN. The experiment was performed to five subjects that were selected mainly based on criteria specified. The EMG signals are acquired at long head biceps brachii. Then, the subjects were asked to lift the loads of 2kg, 5kg, and 7kg. It is expected an accurate classifier which can recognize the pattern precisely and could be further used for arm rehabilitation control.

Movement intention detection using neural network for quadriplegic assistive machine

Biomedical signal lately have been a hot topic for researchers, as many journals and books related to it have been publish. In this paper, the control strategy to help quadriplegic patient using Brain Computer Interface (BCI) on basis of Electroencephalography (EEG) signal was used. BCI is a technology that obtain users thought to control a machine or device. This technology has enabled people with quadriplegia or in other words a person who had lost the capability of his four limbs to move by himself again. Within the past years, many researchers have come out with a new method and investigation to develop a machine that can fulfill the objective for quadriplegic patient to move again. Besides that, due to the development of bio-medical and healthcare application, there are several ways that can be used to extract signal from the brain. One of them is by using EEG signal. This research is carried out in order to detect the brain signal to controlling the movement of the wheelchair by using a single channel EEG headset. A group of 5 healthy people was chosen in order to determine performance of the machine during dynamic focusing activity such as the intention to move a wheelchair and stopping it. A neural network classifier was then used to classify the signal based on major EEG signal ranges. As a conclusion, a good neural network configuration and a decent method of extracting EEG signal will lead to give a command to control robotic wheelchair.

Features extraction of EMG signal using time domain analysis for arm rehabilitation device

Rehabilitation device is used as an exoskeleton for people who had failure of their limb. Arm rehabilitation device may help the rehab program whom suffers from arm disability. The device that is used to facilitate the tasks of the program should improve the electrical activity in the motor unit and minimize the mental effort of the user. Electromyography (EMG) is the techniques to analyze the presence of electrical activity in musculoskeletal systems. The electrical activity in muscles of disable person is failed to contract the muscle for movements. In order to prevent the muscles from paralysis becomes spasticity, the force of movements should minimize the mental efforts. Therefore, the rehabilitation device should analyze the surface EMG signal of normal people that can be implemented to the device. The signal is collected according to procedure of surface electromyography for non-invasive assessment of muscles (SENIAM). The EMG signal is implemented to set the movements’ pattern of the arm rehabilitation device. The filtered EMG signal was extracted for features of Standard Deviation (STD), Mean Absolute Value (MAV) and Root Mean Square (RMS) in time-domain. The extraction of EMG data is important to have the reduced vector in the signal features with less of error. In order to determine the best features for any movements, several trials of extraction methods are used by determining the features with less of errors. The accurate features can be use for future works of rehabilitation control in real-time.Rehabilitation device is used as an exoskeleton for people who had failure of their limb. Arm rehabilitation device may help the rehab program whom suffers from arm disability. The device that is used to facilitate the tasks of the program should improve the electrical activity in the motor unit and minimize the mental effort of the user. Electromyography (EMG) is the techniques to analyze the presence of electrical activity in musculoskeletal systems. The electrical activity in muscles of disable person is failed to contract the muscle for movements. In order to prevent the muscles from paralysis becomes spasticity, the force of movements should minimize the mental efforts. Therefore, the rehabilitation device should analyze the surface EMG signal of normal people that can be implemented to the device. The signal is collected according to procedure of surface electromyography for non-invasive assessment of muscles (SENIAM). The EMG signal is implemented to set the movements’ pattern of the arm rehabilita...

Novel Rehab Devices’ Feature Extraction Analysis Using EMG Signal via Self-Organizing Maps (SOM)

Rehabilitation device is designed to be an exoskeleton for people who had limb failure that proven beneficial toward rehab program. The device used to facilitate the tasks of the program is able to improve the electrical activity in the motor unit and minimize the mental effort of the user. Electromyography (EMG) is the technique to analyze the presence of electrical activity in musculoskeletal systems related to muscle movement. To prevent from the muscle paralyzed, it is becoming spasticity that the force of movements should minimize the mental efforts needed. To achieve this, the rehab device should analyze the surface EMG signal of normal people to be implemented to the rehab device. The EMG signal collected using noninvasive method is implemented to set the movements’ pattern of the arm rehab device. The signal is filtered and extracted for three time-domain features of standard deviation (STD), mean absolute value (MAV), and root mean square (RMS). The features’ combinations are important to produce the best classification result with less error. To determine the best combination features for any movements, several trials of movements are used by determining the right combination using self-organizing maps (SOM) for the classification process.

Development of Novel Fire Alarm Warning System Using Automated Remote Messaging Method

This paper discussed on design and development of fire warning system using automated remote messaging method. This device enables to alert the owner whenever fire occur that need rapid attention towards the building. This is maybe due to carelessness of user or gas leakage. Fire warning system is an existing project but it will be enhanced. This project discussed the design and implementation of a fire alarm system using the microcontroller which controlled the entire system. This system comprised of smoke detector that linked to PIC and GSM Modem. When smoke detected, the fire alarm will triggered and send a signal to the PIC. The PIC will process the data and transmit the signal to the GSM modem. The GSM modem will send message to alert the building owner. The owner can make further action by informing the nearest fire department. This module is applied for transferring of GSM SMS message to the owner mobile number. The devices can be the early and fast prevention system for building owner.

Smart Fan Controller Add-On (SMAFAC) for Home Ceiling Fan for Home Power Optimization

The rising electricaltariff for domestic usage is a well-known issue due to the increase ofpopulation and the scarce of the natural resources to generate the powersupplies. One of the common appliances which can be found in medium householdis the AC fan (table and ceiling) which is manually in operation to control thespeed. It contributes up to 10% of domestic household power consumption.Malaysia, a tropical country with temperature constantly drops from midnight tomorning from 32 °C to 27 °C. This paper presents an innovation of a SmartController Fan (SMAFAC) as a universal external controller as add-on toautomatically controlling the existing ceiling fan speed. It is designed basedon a microcontroller based system which using the temperature sensor as aninput and automatically trigger and control the capacitor fan regulator circuitto control the speed of the fan. The circuit is designed to interface with thecapacitor fan regulator, thus could reduce the power consumption approximatelyat 27% from 12am to 7am for 30 days.

Controller Design for Vector Controlled AC Induction Motor Drive Using State-Space Design Methodologies

Recently, Vector Control also known as Field Oriented Control used in AC induction motor drive provides us of a way to control AC induction motor similar to that of a DC motor. This objective is achieved by transforming the time-varying, difficult to control stator currents into a simple time-invariant system by means of coordinate transformations. This in turn provides us with a systematical way towards designing a controller using classical control or modern state-space design methodologies. Purpose of this research is to use the latter in designing a controller towards regulating current responsible for torque response. A non-linear model of the AC Induction Motor is modeled in the rotating (d,q) reference frame for the control purposes. Then, a state feedback linearization controller was design based on the idea of “exact linearization” to transform the non-linear model into linear state-space model, thus enabling controller design using modern state-space approach. A Linear Quadratic optimal controller and Feedback+Feedforward controller is then designed and applied to the linearized induction motor model. For comparison purposes a classical P/PI controller was also designed. Simulation is then carried out using MATLAB/SIMULINK software and results shows good current regulation by controller design using modern state –space methodologies.