Nasir Rashid

Optimized circuit for EMG signal processing

An optimized circuit for processing of EMG signals has been designed and presented in this paper. This circuit acquires EMG signals from surface of the skin using bipolar electrodes and enables the amputee to control the prosthetic hand in an efficient manner. EMG can be defined as the electrical potential produced due to the contraction of muscle. It can be picked from the residual portion of muscles of an amputee. EMG signal requires the processes of amplification, band limiting and rectification, before it can be fed to an analog to digital converter (ADC) and subsequently to motors driving the prosthetic device.

Development of an On-Board Diagnostic (OBD) kit for troubleshooting of compliant vehicles

This paper presents the development of a generic OBD device and its working with different vehicles based on OBD-TT standards like Land Rover Defender. This device shows the real time vehicle system status, including vehicle speed, engine RPM, throttle position, battery voltage, engine coolant temperature etc. and the diagnostic trouble codes (DTCs) for different vehicles. The software has also been indigenously developed which is used as interface between user and the ECU of the vehicle. This device helps the user to understand the vehicle status and check malfunctions by indicating the Diagnostic Trouble Codes (DTCs). This generic device can prove as an easy to use, battery free device that drives power from the vehicle battery and is easily connected to the PC using USB port.

Analysis of Risks in Re-Engineering Software Systems

ABSTRACT Software re-engineering has become a vital field of computer science and an active research area. The nature of software re-engineering is to improve or transform existing software so it can be understood, controlled and reused as new software. Re-engineering is frequently challenged, because certain risks will threaten the project success. In this article we have described some risks and their classification what we believe to be the most important. From the analysis of risks, some mitigation techniques have been suggested from the existing literature that helps to make the re-engineering projects more beneficial. Keywords Software re-engineering, risks, mitigation, analysis. 1. INTRODUCTION Software Re-engineering is the examination, reorganization, analysis and alteration of an existing software system. It helps to system. The process of restructuring,make them more maintainable and to reconstitute it in a new form and the subsequent implementation of the modified system. This process involves in order to get the target system according to the new the restructuring or recoding of a component or all parts of legacy system without affecting its functionality [1]. Re-engineering is a combination of other processes such as reverse engineering, re-documentation, translation, and forward engineering. The main purpose is to understand the specification, design, implementation of the legacy system and then to implement its modernize form to improve its overall functionality and performance. The difficulty lies in the conceptual understanding of the legacy system. Usually requirements, design and documentation of programming code is no longer available, or is out of date, so it is not clear to the software engineer that what types of functions are to be shifted. Often the software system contains major functions that are not needed anymore, and those should not be re-coded to the new system [2]. The re-engineering process is not risk free and faces various types of risks as software engineering other approaches face. The risk identification is crucial in development and evolution of a legacy system. Risk identification is very important for effective risk assessment, risk analysis, and management and mitigation of risks. In our proposed work, the potential challenges and risks during transformation are analyzed and then categorized on the basis of severity and nature. A well monitoring technique has been described for the categorized risks. It will help a re-engineering system towards successful and easy maintenance and cost benefit with reduced risk.

Evaluation of ANN, LDA and Decision trees for EEG based Brain Computer Interface

Brain Computer Interface (BCI) is a communication system, which avoiding the brains normal output pathways of muscles and peripheral nerves and allows a patient to control its external world only by means of brain signals. For successful implementation of BCI, dimensionality reduction and classification are fundamental task. In this paper, we used a publically available EEG signals data of the Upper Limb Motion. First the dimensionality of the data is being reduced by using Principal Component Analysis (PCA) followed by classification of the reduced dimensioned dataset by well-known classifiers e.g. Artificial Neural Networks (ANN), Linear Discriminant Analysis (LDA) and Decision trees (DT). To identify a classifier which does the classification task more efficiently, we compare their performances on the basis of Confusion Matrices and Percentage Accuracies. The experimental results show that ANN is the best classifier for the classification of brain signals and has the percentage accuracy of 81.6%.

Development of FPGA-based system for control of an Unmanned Ground Vehicle with 5-DOF robotic arm

This paper discusses the development of a customizable FPGA based system for implementing control algorithms on an Unmanned Ground Vehicle (UGV) and its 5 Degree of Freedom (DOF) manipulator. The compact RIO-9012 is used as a controller which is a reconfigurable embedded control and acquisition system using LabVIEW as the programming platform. The developed system enables the control of UGV and its manipulator using a remote joystick controller via Wi-Fi communication. Apart from Joystick, the system can also be controlled optionally using a keyboard. Accuracy of Joystick control has been enhanced by using point to point mapping technique. A user friendly GUI has been developed to view live video feedback obtained from the onboard cameras to control the UGV accordingly. Different features of UGV like path tracker (tracks its path on Google Maps), variable speed modes, battery indicator, camera switch and selector etc. are also managed in the GUI. The system has been developed so that, in future, it can easily be extended to a fully autonomous system.

Design of Embedded System for Multivariate Classification of Finger and Thumb Movements Using EEG Signals for Control of Upper Limb Prosthesis

Brain Computer Interface (BCI) determines the intent of the user from a variety of electrophysiological signals. These signals, Slow Cortical Potentials, are recorded from scalp, and cortical neuronal activity is recorded by implanted electrodes. This paper is focused on design of an embedded system that is used to control the finger movements of an upper limb prosthesis using Electroencephalogram (EEG) signals. This is a follow-up of our previous research which explored the best method to classify three movements of fingers (thumb movement, index finger movement, and first movement). Two-stage logistic regression classifier exhibited the highest classification accuracy while Power Spectral Density (PSD) was used as a feature of the filtered signal. The EEG signal data set was recorded using a 14-channel electrode headset (a noninvasive BCI system) from right-handed, neurologically intact volunteers. Mu (commonly known as alpha waves) and Beta Rhythms (8–30 Hz) containing most of the movement data were retained through filtering using “Arduino Uno” microcontroller followed by 2-stage logistic regression to obtain a mean classification accuracy of 70%.

Inverse kinematics control of redundant planar manipulator with joint constraints using numerical method

In this paper, an efficient numerical scheme is associated with the scheming of the inverse kinematics for a four degrees of freedom redundant planar robotic manipulator. The manipulator under study is an RRPR redundant manipulator with joint angle constraints at each joint. Firstly, using the forward kinematics transformation matrix, a plot of the reachable workspace was obtained. This plot was used to determine equations for the possible end-effector positions. Then the analytic approach was used to determine equations for the inverse kinematic scheme. The inverse kinematics scheme initially sets the first joint angle as a constant at the current position in order to determine the inverse solution. However, if the solution calculated is not within the joint constraints then it uses numerical techniques to determine the least displacement new first joint parameter to determine the next possible solution. This results in a faster and more accurate convergence to the desired solution as compared to the traditional approaches.

Comparative Analysis of EMG Signal Features in Time-domain and Frequency-domain using MYO Gesture Control

Feature extraction is a pronounced method to infer the information utility which is concealed in electromyography (EMG) signal to study the characteristic properties and behavior of signal. This study gives a comparative analysis of thirteen complete and most up-to-date EMG feature signals in Time-domain and Frequency-domain. Particularly, the EMG signals are obtained from a device MYO gesture control on an embedded system. For this purpose, four healthy male volunteers are considered to perform four different hand movements based on stationary, double tap, single finger movement and finger spread. To be a successful classification of these EMG features in both domains, we prefer attribute selected classifier as it gives the better performance and higher rate of accuracy i.e. 93.8%. The experimental results prove that features in time-domain are superfluity and redundant while features in frequency-domain (measured by statistical parameters of EMG power spectral density) show the ultimate dominance and signal characterization. The findings of this study are highly beneficial for further use in order to predict the behavior of EMG in pattern recognition and in classification of EMG signals for assistive devices or in powered human arm prosthetics.

Efficient FIR Filter Implementations for Multichannel BCIs Using Xilinx System Generator

Background. Brain computer interface (BCI) is a combination of software and hardware communication protocols that allow brain to control external devices. Main purpose of BCI controlled external devices is to provide communication medium for disabled persons. Now these devices are considered as a new way to rehabilitate patients with impunities. There are certain potentials present in electroencephalogram (EEG) that correspond to specific event. Main issue is to detect such event related potentials online in such a low signal to noise ratio (SNR). In this paper we propose a method that will facilitate the concept of online processing by providing an efficient filtering implementation in a hardware friendly environment by switching to finite impulse response (FIR). Main focus of this research is to minimize latency and computational delay of preprocessing related to any BCI application. Four different finite impulse response (FIR) implementations along with large Laplacian filter are implemented in Xilinx System Generator. Efficiency of 25% is achieved in terms of reduced number of coefficients and multiplications which in turn reduce computational delays accordingly.

Development of a low-cost anthropomorphic manipulator for commercial usage

In recent times there is a prevalence in amputations in the developing world due to lack of proper medical treatment of diseases & injuries sustained in natural disasters or war; creating a need for an optimal solution that is technically sound and is affordable. This paper discusses the available solutions in the market and then proposes a new design for prosthetic hands, incorporating the morphological features of the actual human hand. Each finger, actuated by a slider mechanism, has a single degree of freedom. While the thumb design utilizes worm gear driven mechanisms to feature a double degree of freedom. The morphology of the developed device is that of the adult male human right hand. The motors for actuation are installed inside the palm of the hand while the overall weight of the prosthetic device is 453 g. The control is generated by the forward kinematics model assessing the relation between the slider position and the rotation angles of the metacarpophalangeal (MCP) joints of the individual fingers. A feedback control loop provides the adaptive grip. The developed prosthesis design can be used to interface with either an EMG or EEG-based control scheme.