Papers by Irraivan Elamvazuthi
Applied Sciences
Medical science-related studies have reinforced that the prevalence of coronary heart disease whi... more Medical science-related studies have reinforced that the prevalence of coronary heart disease which is associated with the heart and blood vessels has been the most significant cause of health loss and death globally. Recently, data mining and machine learning have been used to detect diseases based on the unique characteristics of a person. However, these techniques have often posed challenges due to the complexity in understanding the objective of the datasets, the existence of too many factors to analyze as well as lack of performance accuracy. This research work is of two-fold effort: firstly, feature extraction and selection. This entails extraction of the principal components, and consequently, the Correlation-based Feature Selection (CFS) method was applied to select the finest principal components of the combined (Cleveland and Statlog) heart dataset. Secondly, by applying datasets to three single and three ensemble classifiers, the best hyperparameters that reflect the pre-...
IEEE Access
Electroencephalography (EEG) signals have a major impact on how well assistive rehabilitation dev... more Electroencephalography (EEG) signals have a major impact on how well assistive rehabilitation devices work. These signals have become a common technique in recent studies to investigate human motion functions and behaviors. However, incorporating EEG signals to investigate motor planning or movement intention could benefit all patients who can plan motion but are unable to execute it. In this paper, the movement planning of the lower limb was investigated using EEG signal and bilateral movements were employed, including dorsiflexion and plantar flexion of the right and left ankle joint movements. The proposed system uses Continuous Wavelet Transform (CWT) to generate a time-frequency (TF) map of each EEG signal in the motor cortex and then uses the extracted images as input to a deep learning model for classification. Deep Learning (DL) models are created based on vision transformer architecture (ViT) which is the state-of-the-art of image classification and also the proposed models were compared with residual neural network (ResNet). The proposed technique reveals a significant classification performance for the multiclass problem (p < 0.0001) where the classification accuracy was 97.33 ± 1.86 % and the F score, recall and precision were 97.32 ± 1.88 %, 97.30 ± 1.90 % and 97.36 ± 1.81 % respectively. These results show that DL is a promising technique that can be applied to investigate the user's movements intention from EEG signals and highlight the potential of the proposed model for the development of future brain-machine interface (BMI) for neurorehabilitation purposes. INDEX TERMS Continuous wavelet transform, deep learning, electroencephalography, motor-related cortical potentials, vision transformers architecture.
Machines
An empathic collaborative robot (cobot) was realized through the transmission of fear from a huma... more An empathic collaborative robot (cobot) was realized through the transmission of fear from a human agent to a robot agent. Such empathy was induced through an electroencephalographic (EEG) sensor worn by the human agent, thus realizing an empathic safe brain-computer interface (BCI). The empathic safe cobot reacts to the fear and in turn transmits it to the human agent, forming a social circle of empathy and safety. A first randomized, controlled experiment involved two groups of 50 healthy subjects (100 total subjects) to measure the EEG signal in the presence or absence of a frightening event. The second randomized, controlled experiment on two groups of 50 different healthy subjects (100 total subjects) exposed the subjects to comfortable and uncomfortable movements of a collaborative robot (cobot) while the subjects’ EEG signal was acquired. The result was that a spike in the subject’s EEG signal was observed in the presence of uncomfortable movement. The questionnaires were dis...
Abstract—This paper discusses the conversion of surface electromyography signals (s-EMG) to torqu... more Abstract—This paper discusses the conversion of surface electromyography signals (s-EMG) to torque for robotic rehabilitation. Genetic algorithm (GA) has been applied as control algorithm for a number of selected mathematical models. s-EMG signals was treated as input to the mathematical model where pararameters of the mathematicl model were optimized using GA. Hence, the estimated torque is considered as output data of mathematical model. Index Terms — Electromyography, estimation torque,mathematical model,biomechanics human motion,muscle contraction,robot rehabilitation,feature extraction,genetic algorithm.
Materials Forming, Machining and Tribology, 2021
Materials Forming, Machining and Tribology, 2021
The manufacturing industry has been continuously evolving since the seventeenth century to meet m... more The manufacturing industry has been continuously evolving since the seventeenth century to meet mass production, quality, and reliability requirements. The first generation of manufacturing industries used steam and coal-powered machines in substitution for manpower. In the very next phase of evolution, the manufacturing industry adapted the emerging energy solutions like electricity and petrochemicals [1]. Research and developments in integrated circuits and computers constituted industry 3.0. The third generation of industries involved automation and robotics using electronic controllers to enhance the mass production on shop floors. It is interesting to observe that the technological improvements in preceding generations fuelled the upcoming industrial developments. Analogically, developments on integrated circuits and computers at the time of industry 3.0 unleased the required computational capabilities in the era of artificial intelligence and cloud computing. Advancements in technologies like artificial intelligence, cloud-based environments,
2016 2nd IEEE International Symposium on Robotics and Manufacturing Automation (ROMA), 2016
Over the years, numerous researchers have explored the relationship between surface electromyogra... more Over the years, numerous researchers have explored the relationship between surface electromyography (sEMG) signal with joint torque that would be useful to develop a suitable controller for rehabilitation robot. This research focuses on the transformation of sEMG signal by adopting a mathematical model to find the estimated joint torque of knee extension. Swarm techniques such as Particle Swarm Optimization (PSO) and Improved Particle Swarm Optimization (IPSO) were adapted to optimize the mathematical model for estimated joint torque. The correlation between the estimated joint torque and actual joint torque were determined by Coefficient of Determination (R 2) and fitness value of Sum Squared Error (SSE). The outcome of the research shows that both the PSO and IPSO have yielded promising results.
Sensors, 2021
Visual odometry is the process of estimating incremental localization of the camera in 3-dimensio... more Visual odometry is the process of estimating incremental localization of the camera in 3-dimensional space for autonomous driving. There have been new learning-based methods which do not require camera calibration and are robust to external noise. In this work, a new method that do not require camera calibration called the “windowed pose optimization network” is proposed to estimate the 6 degrees of freedom pose of a monocular camera. The architecture of the proposed network is based on supervised learning-based methods with feature encoder and pose regressor that takes multiple consecutive two grayscale image stacks at each step for training and enforces the composite pose constraints. The KITTI dataset is used to evaluate the performance of the proposed method. The proposed method yielded rotational error of 3.12 deg/100 m, and the training time is 41.32 ms, while inference time is 7.87 ms. Experiments demonstrate the competitive performance of the proposed method to other state-o...
Ultrasound imaging is a cost-effective diagnostic tool to imagine the internal organisms of human... more Ultrasound imaging is a cost-effective diagnostic tool to imagine the internal organisms of human beings that used routinely in the diagnosis of a number of diseases related to ligament, tendon, bone, blood flow estimation, obstetrics, etc. However, ultrasound imaging has limitations such as homogenous intensity regions, homogeneous textures, low contrast regions, enhancement artefact, limited view visualization and inaccurate qualitative/quantitative estimation. To overcome all these investigated problems, this research developed a Computer Aided Diagnosis (CAD) system that helps in efficient segmentation and Three Dimensional (3D) reconstruction of calcaneofibular ligament to enhance the diagnosis. The developed CAD system would help in the achievement of enhanced segmentation results, 3D reconstruction results and statistical analysis of the injured calcaneofibular ligament. Moreover, performance of the developed CAD system is analyzed based on the obtained results, which are indic...
The use of DC servomotor in automated systems is common nowadays in various applications. Specifi... more The use of DC servomotor in automated systems is common nowadays in various applications. Specifically, DC servomotors have played a vital role in the development of surface mount technology (SMT) placement machines that have the ability to quickly place components onto the printed circuit boards (PCBs). The design of controllers for DC servomotors has increasingly become an interesting area for researchers from all over the world. Although the Proportional-Integral-Derivative (PID) controller is regarded as the workhorse of the control industry and it is used for DC servomotor, one of its main short-comings is that there is no efficient tuning method. This paper discusses the development of an algorithm that improves the performance of PID tuning based on Metaheuristic Algorithm; particularly, the Inertia Weight Particle Swarm Optimization (iw-PSO) algorithm. The research findings show the effectiveness of the PSO algorithm compared with the conventional PID controller. Overall, th...
Sustainability, 2021
A compound parabolic concentrator (CPC) is a non-imaging device generally used in PV, thermal, or... more A compound parabolic concentrator (CPC) is a non-imaging device generally used in PV, thermal, or PV/thermal hybrid systems for the concentration of solar radiation on the target surface. This paper presents the geometric design, statistical modeling, parametric analysis, and geometric optimization of a two-dimensional low concentration symmetric compound parabolic concentrator for potential use in building-integrated and rooftop photovoltaic applications. The CPC was initially designed for a concentration ratio of “2×” and an acceptance half-angle of 30°. A MATLAB code was developed in house to provoke the CPC reflector’s profile. The height, aperture width, and concentration ratios were computed for different acceptance half-angles and receiver widths. The interdependence of optical concentration ratio and acceptance half-angle was demonstrated for a wide span of acceptance half-angles. The impact of the truncation ratio on the geometric parameters was investigated to identify the...
Applied Sciences, 2021
Cardiovascular diseases (CVDs) kill about 20.5 million people every year. Early prediction can he... more Cardiovascular diseases (CVDs) kill about 20.5 million people every year. Early prediction can help people to change their lifestyles and to ensure proper medical treatment if necessary. In this research, ten machine learning (ML) classifiers from different categories, such as Bayes, functions, lazy, meta, rules, and trees, were trained for efficient heart disease risk prediction using the full set of attributes of the Cleveland heart dataset and the optimal attribute sets obtained from three attribute evaluators. The performance of the algorithms was appraised using a 10-fold cross-validation testing option. Finally, we performed tuning of the hyperparameter number of nearest neighbors, namely, ‘k’ in the instance-based (IBk) classifier. The sequential minimal optimization (SMO) achieved an accuracy of 85.148% using the full set of attributes and 86.468% was the highest accuracy value using the optimal attribute set obtained from the chi-squared attribute evaluator. Meanwhile, the ...
Sensors, 2019
Rheumatoid arthritis (RA) is an autoimmune illness that impacts the musculoskeletal system by cau... more Rheumatoid arthritis (RA) is an autoimmune illness that impacts the musculoskeletal system by causing chronic, inflammatory, and systemic effects. The disease often becomes progressive and reduces physical function, causes suffering, fatigue, and articular damage. Over a long period of time, RA causes harm to the bone and cartilage of the joints, weakens the joints’ muscles and tendons, eventually causing joint destruction. Sensors such as accelerometer, wearable sensors, and thermal infrared camera sensor are widely used to gather data for RA. In this paper, the classification of medical disorders based on RA and orthopaedics datasets using Ensemble methods are discussed. The RA dataset was gathered from the analysis of white blood cell classification using features extracted from the image of lymphocytes acquired from a digital microscope with an electronic image sensor. The orthopaedic dataset is a benchmark dataset for this study, as it posed a similar classification problem wit...
Research Journal of Applied Sciences, Engineering and Technology, 2013
This study reports the fuzzy inference system controllers. They are employed in order to control ... more This study reports the fuzzy inference system controllers. They are employed in order to control actuator systems for thermal, visual and indoor air quality power consumption. The implementation of the Fuzzy Logic Control (FLC) system, allows an stochastic interval range of comfort index as susceptible to human body, achieving numerous power demand values for the actuator operations. The potential benefit of the automated smart building are high-level comfort, improved device efficiency, environment friendliness and reduced energy consumption as well as cost. The simulation results are shown describing the behavourial relation for each control parameter with the power consumption.
Sensors (Basel, Switzerland), Jan 7, 2018
Electroencephalography (EEG) signals have great impact on the development of assistive rehabilita... more Electroencephalography (EEG) signals have great impact on the development of assistive rehabilitation devices. These signals are used as a popular tool to investigate the functions and the behavior of the human motion in recent research. The study of EEG-based control of assistive devices is still in early stages. Although the EEG-based control of assistive devices has attracted a considerable level of attention over the last few years, few studies have been carried out to systematically review these studies, as a means of offering researchers and experts a comprehensive summary of the present, state-of-the-art EEG-based control techniques used for assistive technology. Therefore, this research has three main goals. The first aim is to systematically gather, summarize, evaluate and synthesize information regarding the accuracy and the value of previous research published in the literature between 2011 and 2018. The second goal is to extensively report on the holistic, experimental o...
Sensors, 2018
Increasing interest in analyzing human gait using various wearable sensors, which is known as Hum... more Increasing interest in analyzing human gait using various wearable sensors, which is known as Human Activity Recognition (HAR), can be found in recent research. Sensors such as accelerometers and gyroscopes are widely used in HAR. Recently, high interest has been shown in the use of wearable sensors in numerous applications such as rehabilitation, computer games, animation, filmmaking, and biomechanics. In this paper, classification of human daily activities using Ensemble Methods based on data acquired from smartphone inertial sensors involving about 30 subjects with six different activities is discussed. The six daily activities are walking, walking upstairs, walking downstairs, sitting, standing and lying. It involved three stages of activity recognition; namely, data signal processing (filtering and segmentation), feature extraction and classification. Five types of ensemble classifiers utilized are Bagging, Adaboost, Rotation forest, Ensembles of nested dichotomies (END) and Ra...
Energies, 2016
This paper proposes an advanced Pareto-front non-dominated sorting multi-objective particle swarm... more This paper proposes an advanced Pareto-front non-dominated sorting multi-objective particle swarm optimization (Advanced-PFNDMOPSO) method for optimal configuration (placement and sizing) of distributed generation (DG) in the radial distribution system. The distributed generation consists of single and multiple numbers of active power DG, reactive power DG and simultaneous placement of active-reactive power DG. The optimization problem considers two multi-objective functions, i.e., power loss reduction and voltage stability improvements with voltage profile and power balance as constraints. First, the numerical output results of objective functions are obtained in the Pareto-optimal set. Later, fuzzy decision model is engendered for final selection of the compromised solution. The proposed method is employed and tested on standard IEEE 33 bus systems. Moreover, the results of proposed method are validated with other optimization algorithms as reported by others in the literature. The overall outcome shows that the proposed method for optimal placement and sizing gives higher capability and effectiveness to the final solution. The study also reveals that simultaneous placement of active-reactive power DG reduces more power losses, increases voltage stability and voltage profile of the system.
Biomedical engineering online, Jan 2, 2016
Anterior talofibular ligament (ATFL) is considered as the weakest ankle ligament that is most pro... more Anterior talofibular ligament (ATFL) is considered as the weakest ankle ligament that is most prone to injuries. Ultrasound imaging with its portable, non-invasive and non-ionizing radiation nature is increasingly being used for ATFL diagnosis. However, diagnosis of ATFL injuries requires its segmentation from ultrasound images that is a challenging task due to the existence of homogeneous intensity regions, homogeneous textures and low contrast regions in ultrasound images. To address these issues, this research has developed an efficient ATFL segmentation framework that would contribute to accurate and efficient diagnosis of ATFL injuries for clinical evaluation. The developed framework comprises of five computational steps to segment the ATFL ligament region. Initially, region of interest is selected from the original image, which is followed by the adaptive histogram equalization to enhance the contrast level of the ultrasound image. The enhanced contrast image is further optimi...
Procedia Computer Science, 2015
Over the years, research into robotics has yielded many applications in several sectors such as m... more Over the years, research into robotics has yielded many applications in several sectors such as manufacturing, domestic and sports industries. This paper discusses the development of an autonomous tennis ball retriever robot for the sports sector. The robot was developed with the aim of saving the time and energy of the tennis player from manually collecting the tennis balls after training sessions. The developed autonomous robot is able to retrieve tennis balls by sweeping around the tennis court. The paper discusses the system configuration in terms of mechanical and controller subsystems, the navigation and system performance. The knowledge and experience gained from the development of various subsystems of the robot is useful for educating the undergraduate students.
MATEC Web of Conferences, 2016
Distributed generation (DG) has increased ever attention in the distribution system from last few... more Distributed generation (DG) has increased ever attention in the distribution system from last few years. The main reason for DG in distribution system is increasing electric demand, deregulated power system and congested transmission network, which eventually declines the system performance. There is also increasing pressure of greenhouse gas emissions. For proper utilization of DG, the optimal placement and sizing is of main concern. Because improper DG location and size will increase the losses and decrease the system performance than existing. On the contrary, proper placement will maintain voltage profile, reduce power loss, and increase voltage stability in the distribution system. This paper presents overview of DG, the advances in DG technology and different optimization methods used for optimal placement and sizing problem. The key issues and challenges offered in the development of DG is also presented in this paper.
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Papers by Irraivan Elamvazuthi