Browsing by Author "Akmeliawati, Rini"
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Item Analysis of the ECG signal using SVD-based parametric modelling technique(IEEE, 2011-01-17) Baali, Hamza; Salami, Momoh-Jimoh E.; Akmeliawati, Rini; Aibinu, Abiodun M.A new parametric modeling technique for the analysis of the ECG signal is presented in this paper. This approach involves the projection of the excitation signal on the right eigenvectors of the impulse response matrix of the LPC filter. Each projected value is then weighted by the corresponding singular value, leading to an approximated sum of exponentially damped sinusoids (EDS). A two-stage procedure is then used to estimate the EDS model parameters. Prony's algorithm is first used to obtain initial estimates of the model, while the Gauss-Newton method is applied to solve the non-linear least-square optimisation problem. The performance of the proposed model is evaluated on abnormal clinical ECG data selected from the MIT-BIH database using objective measures of distortion. A good compression ratio per beat has been obtained using the proposed algorithm which is quite satisfactory when compared to other techniques.Item Artificial intelligent based friction modelling and compensation in motion control system(INTECH Open Access Publisher, 2011) Ismaila, Tijani B.; Salami, Momoh-Jimoh E.; Akmeliawati, Rini; Alfaro, Horacio M.The interest in the study of friction in control engineering has been driven by the need for 10 precise motion control in most of industrial applications such as machine tools, robot 11 systems, semiconductor manufacturing systems and Mechatronics systems. Friction has 12 been experimentally shown to be a major factor in performance degradation in various 13 control tasks. Among the prominent effects of friction in motion control are: steady state 14 error to a reference command, slow response, periodic process of sticking and sliding (stick-15 slip) motion, as well as periodic oscillations about a reference point known as hunting when 16 an integral control is employed in the control scheme. Table 1 shows the effects and type of 17 friction as highlighted by Armstrong et. al.(1994). It is observed that, each of task is 18 dominated by at least one friction effect ranging from stiction, or/and kinetic to negative 19 friction (Stribeck). Hence, the need for accurate compensation of friction has become 20 important in high precision motion control. Several techniques to alleviate the effects of 21 friction have been reported in the literature (Dupont and Armstrong, 1993; Wahyudi, 2003; 22 Tjahjowidodo, 2004; Canudas, et. al., 1986). 23 One of the successful methods is the well-known model-based friction compensation 24 (Armstrong et al., 1994; Canudas de Wit et al., 1995 and Wen-Fang, 2007). In this method, 25 the effect of the friction is cancelled by applying additional control signal which generates a 26 torque/force. The generated torque/force has the same value (or approximately the same) 27 with the friction torque/force but in opposite direction.Item Development of Brain-Controlled Assistive Feeding System(UNSYS digital, 2015) Khorshidtalab, Aida; Salami, Momoh-Jimoh E.; Akmeliawati, RiniFeeding difficulties and malnutrition are common phenomena in amyotrophic lateral sclerosis patients, locked in patients and people with upper limb disability. Feeding is often time consuming, unpleasant, and may result in choking or asphyxiation. Nowadays, robotic aids are applied to assist these people for eating. However, assistive robots that require movements from the user are not suitable for people with critical disabilities, including sensory losses, and/or difficulty in basic physical mobility. In this regard, a robotic system that can be controlled merely by brain signals is quite a remarkable aid. Therefore, based on the requirements for real-time assistive robot a prototype of an EEG-based feeding robot is proposed. The proposed feeding system enables the target group to eat independently. Experimental results show that the developed system is able to perform the required tasks, in real-time, with tolerable errors of around 17% in average. This amount of error can be further supervised to be reduced or in some cases even eliminated.Item Dynamic approach for real-time skin detection(Springer Berlin Heidelberg, 2015-06-01) Bilal, Sara; Akmeliawati, Rini; Salami, Momoh-Jimoh E.; Shafie, Amir A.Human face and hand detection, recognition and tracking are important research areas for many computer interaction applications. Face and hand are considered as human skin blobs, which fall in a compact region of colour spaces. Limitations arise from the fact that human skin has common properties and can be defined in various colour spaces after applying colour normalization. The model therefore, has to accept a wide range of colours, making it more susceptible to noise. We have addressed this problem and propose that the skin colour could be defined separately for every person. This is expected to reduce the errors. To detect human skin colour pixels and to decrease the number of false alarms, a prior face or hand detection model has been developed using Haar-like and AdaBoost technique. To decrease the cost of computational time, a fast search algorithm for skin detection is proposed. The level of performance reached in terms of detection accuracy and processing time allows this approach to be an adequate choice for real-time skin blob tracking.Item ECG parametric modeling based on signal dependent orthogonal transform(IEEE, 2014-07-02) Baali, Hamza; Akmeliawati, Rini; Salami, Momoh-Jimoh E.; Khorshidtalab, Aida; Lim, E.In this letter, we propose a parametric modeling technique for the electrocardiogram (ECG) signal based on signal dependent orthogonal transform. The technique involves the mapping of the ECG heartbeats into the singular values (SV) domain using the left singular vectors matrix of the impulse response matrix of the LPC filter. The resulting spectral coefficients vector would be concentrated, leading to an approximation to a sum of exponentially damped sinusoids (EDS). A two-stage procedure is then used to estimate the model parameters. The Prony's method is first employed to obtain initial estimates of the model, while the Levenberg-Marquardt (LM) method is then applied to solve the nonlinear least-square optimization problem. The ECG signal is reconstructed using the EDS parameters and the linear prediction coefficients via the inverse transform. The merit of the proposed modeling technique is illustrated on the clinical data collected from the MITBIH database including all the arrhythmias classes that are recommended by the Association for the Advancement of Medical Instrumentation (AAMI). For all the tested ECG heartbeats, the average values of the percent root mean square difference (PRDs) between the actual and the reconstructed signals were relatively low, varying between a minimum of 3.1545% for Premature Ventricular Contractions (PVC) class and a maximum of 10.8152% for Nodal Escape (NE) class.Item Hidden Markov model for human to computer interaction: a study on human hand gesture recognition(Springer Netherlands, 2013-12-01) Bilal, Sara; Akmeliawati, Rini; Shafie, Amir A.; Salami, Momoh-Jimoh E.Human hand recognition plays an important role in a wide range of applications ranging from sign language translators, gesture recognition, augmented reality, surveillance and medical image processing to various Human Computer Interaction (HCI) domains. Human hand is a complex articulated object consisting of many connected parts and joints. Therefore, for applications that involve HCI one can find many challenges to establish a system with high detection and recognition accuracy for hand posture and/or gesture. Hand posture is defined as a static hand configuration without any movement involved. Meanwhile, hand gesture is a sequence of hand postures connected by continuous motions. During the past decades, many approaches have been presented for hand posture and/or gesture recognition. In this paper, we provide a survey on approaches which are based on Hidden Markov Models (HMM) for hand posture and gesture recognition for HCI applications.Item Hidden Markov model for human to computer interaction: a study on human hand gesture recognition(Springer Netherlands, 2013-12-01) Bilal, Sara; Akmeliawati, Rini; Shafie, Amir A.; Salami, Momoh-Jimoh E.Human hand recognition plays an important role in a wide range of applications ranging from sign language translators, gesture recognition, augmented reality, surveillance and medical image processing to various Human Computer Interaction (HCI) domains. Human hand is a complex articulated object consisting of many connected parts and joints. Therefore, for applications that involve HCI one can find many challenges to establish a system with high detection and recognition accuracy for hand posture and/or gesture. Hand posture is defined as a static hand configuration without anymovement involved. Meanwhile, hand gesture is a sequence of hand postures connected by continuous motions. During the past decades, many approaches have been presented for hand posture and/or gesture recognition. In this paper, we provide a survey on approaches which are based on Hidden Markov Models (HMM) for hand posture and gesture recognition for HCI applications.Item Human Upper Body Pose Region Estimation(Springer, Berlin, Heidelberg, 2013) Bilal, Sara; Akmeliawati, Rini; Shafie, Amir A.; Salami, Momoh-Jimoh E.The objective of this chapter is to estimate 2D human pose for action recognition and especially for sign language recognition systems which require not only the hand motion trajectory to be classified but also facial features, Human Upper Body (HUB) and hand position with respect to other HUB parts. We propose an approach that progressively reduces the search space for body parts and can greatly improve chance to estimate the HUB pose. This involves two contributions: (a) a fast and robust search algorithm for HUB parts based on head size has been introduced for real time implementations. (b) Scaling the extracted parts during body orientation was attained using partial estimation of face size. The outcome of the system makes it applicable for real-time applications such as sign languages recognition systems. The method is fully automatic and self-initializing using a Haar-like face region. The tracking the HUB pose is based on the face detection algorithm. Our evaluation was done mainly using 50 images from INRIA Person Dataset.Item A hybrid method using haar-like and skin-color algorithm for hand posture detection, recognition and tracking(IEEE, 2010-08-04) Bilal, S.; Akmeliawati, Rini; Salami, Momoh-Jimoh E.; Shafie, Amir A.; Bouhabba, El MehdiHuman hand posture detection and recognition is a challenging problem in computer vision. We introduce an algorithm that is capable to recognize hand posture in a sophisticated background. The system combines two algorithms to achieve better detection rate for hand. Recently Viola et al. in have introduced a rapid object detection scheme; we use this approach to detect the hand posture in the first set of consecutive frames. The chromatic color distribution of skin can be found within this cluster. As the shape of hand posture keep changing in the subsequent frames, the skin regions updated dynamically. The classification of hand posture makes use of static feature for locating and counting hand fingers. Kalman Filter is used to track the face and hand blobs based on their position. In the experiments, we have tested our system in various environments, and results showed effectiveness of the approach.Item Hybrid technique using singular value decomposition (SVD) and support vector machine (SVM) approach for earthquake prediction(IEEE, 2014-06-06) Astuti, Winda; Akmeliawati, Rini; Sediono, Wahju; Salami, Momoh-Jimoh E.Most of the existing earthquake (EQ) prediction techniques involve a combination of signal processing and geophysics techniques which are relatively complex in computation for analysis of the Earth's electric field data. This paper proposes a relatively simpler and faster method that involves only signal processing procedures. The prediction of the EQ occurrence estimation using a combination of singular value decomposition (SVD)-based technique for feature extraction and support vector machine (SVM) classifier is presented in this paper. Using the proposed method, the Earth's electric field signal is transformed into a new domain using SVD-based approach. In this approach, the time domain signal is projected on the left eigenvectors of impulse response matrix of the linear prediction coefficient (LPC) filter. Several features have been extracted from the transformed signal. These features are used as input for the SVM classifier in order to predict the location of the forthcoming EQ. Once the location is determined, a similar approach is used to estimate its magnitude. Finally, the time estimation of the forthcoming EQ is estimated based on the statistical observation. The occurred EQs during 2008 in Greece are used to train the classifiers, whereas those occurred from 2003 to 2010 in the same region are used to evaluate the performance of the proposed system. In predicting the location of the future EQs, the proposed system could achieve 77% accuracy. As for the magnitude prediction, the proposed system provides an accuracy of 66.67%. Moreover, the predicted time for the EQ with magnitude greater than Ms = 5 is 2 days ahead, whereas for magnitude greater than Ms = 6 is up to 7 days ahead.Item Modeling of Human Upper Body for Sign Language Recognition(IEEE, 2011-12-06) Bilal, Sara; Akmeliawati, Rini; Shafie, Amir A.; Salami, Momoh-Jimoh ESign Language Recognition systems require not only the hand motion trajectory to be classified but also facial features, Human Upper Body (HUB) and hand position with respect to other HUB parts. Head, face, forehead, shoulders and chest are very crucial parts that can carry a lot of positioning information of hand gestures in gesture classification. In this paper as the main contribution, a fast and robust search algorithm for HUB parts based on head size has been introduced for real time implementations. Scaling the extracted parts during body orientation was attained using partial estimation of face size. Tracking the extracted parts for front and side view was achieved using CAMSHIFT [24]. The outcome of the system makes it applicable for real-time applications such as Sign Languages Recognition (SLR) systems.Item Motor imagery task classification using transformation based features(Elsevier, 2017-03-01) Khorshidtalab, A.; Salami, Momoh-Jimoh E.; Akmeliawati, RiniThis paper proposes a feature extraction method named as LP QR, based on the decomposition of the LPC filter impulse response matrix of the signal of interest. This feature extraction method is inspired by LP SVD and is tested in the context of motor imagery electroencephalogram. The extracted features are classified and benchmarked against extracted features of LP SVD method. The two applied methods are also compared regarding the required execution time, which further highlights their respective merits and demerits. This paper closely examines the contribution of EEG channels of these two information extraction algorithms too. Consequently, a detailed analysis of the role of EEG channels concerning the nature of the extracted information is presented. This study is conducted on the BCI IIIa competition database of four motor imagery movements. The obtained results indicate that the proposed method is the better choice if simplicity is demanded. The investigation into the role of EEG channels reveals that level of contribution each channel can be quite dissimilar for different feature extraction algorithms.Item Vision-based hand posture detection and recognition for Sign Language—A study(IEEE, 2011-05-17) Bilal, Sara; Akmeliawati, Rini; Salami, Momoh-Jimoh E.; Shafie, Amir A.Unlike general gestures, Sign Languages (SLs) are highly structured so that it provides an appealing test bed for understanding more general principles for hand shape, location and motion trajectory. Hand posture shape in other words static gestures detection and recognition is crucial in SLs and plays an important role within the duration of the motion trajectory. Vision-based hand shape recognition can be accomplished using three approaches 3D hand modelling, appearance-based methods and hand shape analysis. In this survey paper, we show that extracting features from hand shape is so essential during recognition stage for applications such as SL translators.