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 user's 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

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 estimate 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. Arm rehabilitation device may help the rehab program to whom suffered with arm disability. The device 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. To prevent the muscles from paralysis becomes spasticity the force of movements should minimize the mental efforts. Besides that, in order 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 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 experimental results show that ANN can well represent EMG-torque relationship for arm rehabilitation device control

CLASSIFICATION OF ARM MOVEMENT BASED ON UPPER LIMB MUSCLE SIGNAL FOR REHABILITATION DEVICE

Rehabilitation device is used as an exoskeleton for people who experience limb failure. Arm rehabilitation device may ease the rehabilitation programme for those who suffer arm dysfunctional. The device used to facilitate the tasks of the program should improve the electrical activity in the motor unit by minimising the mental effort of the user. Electromyography (EMG) is the techniques to analyse the presence of electrical activity in musculoskeletal systems. The electrical activity in muscles of disable person are failed to contract the muscle for movements. To prevent the muscles from paralysis becomes spasticity or flaccid the force of movements has to minimise the mental efforts. To minimise the used of cerebral strength, analysis on EMG signals from normal people are conducted before it can be implement in the device. The signals are collect according to procedure of surface electromyography for non-invasive assessment of muscles (SENIAM). The implementation of EMG signals is to set the movements’ pattern of the arm rehabilitation device. The filtered signal further the process by extracting the features as follows; Standard Deviation(STD), Mean Absolute Value(MAV), Root Mean Square(RMS), Zero Crossing(ZCS) and Variance(VAR). The extraction of EMG data is to have the reduced vector in the signal features for minimising the signals error than can be implement in classifier. The classification of features is by SOMToolbox using MATLAB. The features extraction of EMG signals is classified into several degree of arm movement visualize in U- Matrix form

Classification of EMG Signal Based on Human Percentile using SOM

Electromyography (EMG) is a bio signal that is formed by physiological variations in the state of muscle fibre membranes. Pattern recognition is one of the fields in the bio-signal processing which classified the signal into certain desired categories with subject to their area of application. This study described the classification of the EMG signal based on human body percentile using Self Organizing Mapping (SOM) technique. Different human percentile definitively varies the arm circumference size. Variation of arm circumference is due to fatty tissue that lay between active muscle and skin. Generally the fatty tissue would decrease the overall amplitude of the EMG signal. Data collection is conducted randomly with fifteen subjects that have numerous percentiles using non-invasive technique at Biceps Brachii muscle. The signals are then going through filtering process to prepare them for the next stage. Then, five well known time domain feature extraction methods are applied to the signal before the classification process. Self Organizing Map (SOM) technique is used as a classifier to discriminate between the human percentiles. Result shows that SOM is capable in clustering the EMG signal to the desired human percentile categories by optimizing the neurons of the technique

Analysing of a shunt compensator installation

Nowadays, electricity has become an important part of human lives. Electricity is needed to light up houses, buildings and even transports. Electricity comes from generators which produce power that are useful for loads. But, not all power that flow from generators is useful for a power system. Although reactive power is needed on power system, a high amount of it will cause problems such as the reduction of active power generated and poor voltage regulation. The reactive power consumption by loads must be compensated and this could be done by installing shunt compensator on the electrical network. An approach has been done to study about the performance of the system with and without compensator installed. This research presents a comprehensive study on the shunt compensation method. There were three methods used in finishing this research which are collecting data from electricity utility, literature review writing, and simulation using PowerWorld Simulator. The simulated network is evaluated in terms of its performance with and without compensator installed

Output Power Forecasting for 2kW Monocrystalline PV System using Response Surface Methodology

 Photovoltaic (PV) system is a renewable energy source that not only able to reduce the effect of greenhouse gas towards the environment, but also a highly profitable industry nowadays. To determine the Return of Investment (ROI) of a newly installed system, forecasting is crucial. Thus, the purpose of this study is to produce a prediction model for the yearly output power of the PV system using three environmental elements; irradiance, module temperature and ambient temperature by Response Surface Methodology (RSM). To do so, MATLAB RStool which is consisting of four models; multiple linear regression (MLR), interaction, pure quadratic, and full quadratic is used. The 5 minute sampling size of yearly 2014 weather station data the three environmental elements and output power of a 2kW Monocrystalline real PV system are used for training. Whereas, yearly 2015 data of the aforementioned elements are used for validation. The coefficient of determination (R2) method and root mean square error (RMSE) approach were used to determine the most accurate prediction model. Results show that, full quadratic is the most accurate prediction model with R2 value of 0.9995 and RMSE of 8%. It is hoped that the prediction model introduced can be a viable method to be used by the PV system installer.

Multi population evolutionary programming approach for distributed generation installation

This paper describes the impact of development distribution in order to identify optimum location and size for distribution generation (DG) in power system network. High demand on the load will cause unstable control power distributed through power loss via power transmition. Therefore smallscale electricity generation is required to ensure large power generated can be used for particular location to minimize power losses. In addition, the implementation of distribution generation will help to reduce the capital cost compared to the existing power plant due to space, speed and power requirement. Thus proper DG location will significantly improve the impact of the power flow analysis by considering the source of energy which is easily obtained. This study will be conducted by using Matlab and the proposed algorithm (MPEP) will be applied on IEEE 30 buses radial distribution system network. As a result, the DG can be located at optimal location and size depending on the losses consume in various type of DG technology systems used in the network. On the other hand, the condition and location DG itself will generate optimal power contribution depending on design strategies that have been implemented

A smart partial discharge classification SOM with optimized statistical transformation feature

Condition-based monitoring (CBM) has been a vital engineering method to assess high voltage (HV) equipment and power cables conditions or health levels. One of the effective CBM methods is partial discharge (PD) measurement or detection. PD event is the phenomenon that always associated with insulation healthiness. PD has been measured and evaluated in this paper to discriminate PD signals from a good signal. A mixed-signal being fed at an AI technique with statistical modified input data to do fast classification (less than five seconds) with nearly zero error. In this paper, an unsupervised neural network is applied for PD classification. The methods combine the self-organizing maps (SOMs) and feature statistical transformation. By the combination of these methods, the ‘range’ normalization method produced the best classification outcomes. This development decided that PD information was effectively correlated and grouped by means of MATLAB’s SOM Toolbox and transformation device to discriminate the normal signal from the PD signal

Bus Stand Lamp Using Piezoelectric Energy

The development of piezoelectric energy harvester has rapidly becoming an attractive research in these few years mainly due to the largely use of semiconductor in power supply technology. Harvesting ambient vibration energy using piezoelectric element has become an interesting topic since the technology is using the free energy source, which can reduce a significant value in cost development. The prototype of bus stand lamp using piezoelectric energy was developed to show the concept of the harvester system. The objective of this product is to overcome the lacking lighting system problem for bus stand at rural place. By using the free energy source, which is gain from the people walking across this area, the piezoelectric disc was placed on the floor whereby the location of the piezoelectric disc is determined based on the maximum harvesting output power when people step on it. Power that harvested will then be stored in the super capacitor or battery before delivered to the load. The light emitting diode (LED) is used as the supply of the light to the bus stand since the energy efficiency of LED is higher compared to the traditional lighting. Two experiments were conducted to determine the maximum output of piezoelectric harvesting base which from the experimental results, it shows that a significant output voltage and current of 0.372A and 0.421A respectively is successfully generated which then can be use to charge the battery

A 33kV Distribution Network Feeder Reconfiguration by Using REPSO for Voltage Profile Improvement

The complexity of modern power system has contributed to the high power losses and over load in the distribution network. Due to that reason, Feeder Reconfiguration (FR) is required to identify the best topology network in order to fulfill the power demand with reduced power losses while stabilizing the magnitude of voltage. This paper addresses a new optimization method which is called as Rank Evolutionary Particle Swarm Optimization (REPSO). It has been produced by a hybridization of the conventional Particle Swarm Optimization (PSO) and the traditional Evolutionary Programming (EP) algorithm. The main objective of this paper is to improve the voltage profile while solves the overload problem by reducing the power losses respectively. The proposed method has been implemented and the real power losses in the 33kVdistribution system has been investigated and analyzed accordingly. The results are compared to the conventional Genetic Algorithm (GA), EP and PSO techniques and it is hoped to help the power system engineer in securing the network in the future