Design, Fabrication and Evaluation of an Eye Tracker to Help Disabled People to Communicate with Computer
Diako
Mardanbeigi
M.Sc Student, Biomechanics Department, Faculty of Mechanical Engineering, Iran University of Science and Technology
author
Mohammad Reza
Mallakzadeh
Assistant Professor, Biomechanics Department, Faculty of Mechanical Engineering, Iran University of Science and Technology
author
text
article
2011
per
This paper investigates prototyping an online, low-cost, video based and applicable eye tracker, which is called "Dias Eye Tracker". Disabled people can use the proposed system to communicate with computer. What have made the system different from the other low-cost eye trackers, are the accuracy of gaze estimation, the different application parts of the software and the lightweight wireless hardware, which can be mounted on the user’s head. This paper introduces the software/hardware and the methods of the system. In addition, two methods of pupil tracking have been compared together, and an uncertainty analysis on the mapping function of the system has been done. The performance of the designed eye tracker has been evaluated by analyzing the answers to the three questionnaires, which were filled by disabled people after performing three specific tasks. The results show that the system performs well for interaction with computer.
Iranian Journal of Biomedical Engineering
Iranian Society for Biomedical Engineering
5869-2008
4
v.
4
no.
2011
267
278
https://www.ijbme.org/article_13195_9b9d0355a8a9da34b286f74843d15d26.pdf
dx.doi.org/10.22041/ijbme.2011.13195
Patient Independent Heart Beat Classification using Reconstructed Phase Space
Isar
Nejadgholi
Ph.D Student, Faculty of Biomedical Engineering, Amirkabir University of Technology
author
Mohammad Hasan
Moradi
Associate Professor, Faculty of Group, iomedical Engineering, Amirkabir University of Technology
author
Fateme
Abdol Ali
M.Sc Student, Faculty of Biomedical Engineering, Amirkabir University of Technology
author
text
article
2011
per
Many methods for automatic heartbeat classification have been applied and reported in literature, but relatively little number of them concerned with patient independent classification because of the less significant results compared to patient dependent ones. In this work, Reconstructed Phase Space (RPS) theory is used to classify five heartbeat types (Normal, PVC, LBBB, RBBB and PB). In the first and second method, RPS is modeled by the Gaussian mixture model (GMM) and bins, respectively and then classified by classic Bayesian classifier. In the third method, RPS is directly used to train predictor time-delayed neural networks (TDNN) and classified based on minimum prediction error. All three methods highly outperform the results reported before for patient independent heartbeat classification. The best result is achieved using GMM-Bayes method with 92.5% accuracy for patient independent classification.
Iranian Journal of Biomedical Engineering
Iranian Society for Biomedical Engineering
5869-2008
4
v.
4
no.
2011
279
292
https://www.ijbme.org/article_13196_c84f1dba66252840c1155b9d565c1fd1.pdf
dx.doi.org/10.22041/ijbme.2011.13196
Development of P300 detection by Combination of Time, Frequency and Spatial Feature Extraction Methods
Zahra
Amini
M.Sc Student, Electrical and Computer Engineering Department, Yazd University
author
Vahid
Abootalebi
2Assistant Professor, Electrical and Computer Engineering Department, Yazd University
author
Mohammad Taghi
Sadeghi
Assistant Professor, Electrical and Computer Engineering Department, Yazd University
author
text
article
2011
per
The aim of this paper is to design a pattern recognition based system to detect P300 component in multi-channel electroencephalogram (EEG) trials. This system has two main blocks, feature extraction and classification. In feature extraction block, in addition to conventional features namely morphological, frequency and wavelet features, some new features included intelligent segmentation, common spatial pattern (CSP) and combined features (CSP + Segmentation) have also been used. Three criteria were used for evaluation and selection of a feature set by choosing a subset of the original features that contains most of essential information. Firstly, a statistical analysis has been applied for evaluating the fitness of each feature in discriminating between target and non target signals. Secondly, each of these six groups of features was evaluated by a Linear Discriminant Analysis (LDA) classifier. Furthermore by using Stepwise Linear Discriminant Analysis (SWLDA), the best set of features was selected. Among these six feature vectors, intelligent segmentation was seen to be most efficient in classification of these signals. In classification phase, two linear classifiers -LDA and SWLDA- were used. The algorithm was described here has tested with dataset II from the BCI competition 2005. In this research, the best result for P300 detection is 97.05% .This result have proven to be more accurate than the results of previous works carried out in this filed.
Iranian Journal of Biomedical Engineering
Iranian Society for Biomedical Engineering
5869-2008
4
v.
4
no.
2011
293
306
https://www.ijbme.org/article_13198_03b57f39e3a482db1126d8072017d515.pdf
dx.doi.org/10.22041/ijbme.2011.13198
An Experimental Investigation into the use of Ramanspectroscopy for the Diagnosis of Cancer
Zohre
Dehghani Bidgoli
Ph.D Student, Biomedical Engineering Group, Electrical and Computer Engineering Department, Tarbiat Modares University
author
Mohammad Hossein
Miranbaygi
Associate Professor, Biomedical Engineering Group, Electrical and Computer Engineering Department, Tarbiat Modares University
author
Rasoul
Malekfar
Associate Professor, Physics Group, Faculty of Basic Sciences, Tarbiat Modares University
author
Ehsanollah
Kabir
Professor, Electronics Group, Electrical and Computer Engineering Department, Tarbiat Modares University
author
Tahere
Khamechian
Associate Professor, Medical Pathology Group, Kashan University of Medical Sciences
author
text
article
2011
per
In this research, we investigated cancerous tissues from several organs of the human body using Raman spectroscopy. Different specimens with different pathologic labels (normal & cancerous) were borrowed from a pathology laboratory, and were investigated using two different Raman spectroscopy systems. Since one of the goals of this investigation was detection of cancer, independent of type of the system, we introduced some algorithms for removing systemic differences from the spectra. Then we removed noise and fluorescence signals using a new wavelet created with LWT. The best classification result was 83% in differentiating between normal and cancerous specimens using the SVM classifier
Iranian Journal of Biomedical Engineering
Iranian Society for Biomedical Engineering
5869-2008
4
v.
4
no.
2011
307
316
https://www.ijbme.org/article_13199_725e5bf65a72049378670bd2e899045d.pdf
dx.doi.org/10.22041/ijbme.2011.13199
Image reconstruction in Diffuse Optical Tomography by a High-Order Finite Element Method
Maede
Hadinia
Ph.D Student, Biomedical Engineering Group, Electrical Engineering Department, K.N. Toosi University of Technology
author
Reza
Jafari
Assistant Professor, Biomedical Engineering Group, Electrical Engineering Department, K.N. Toosi University of Technology
author
text
article
2011
per
This paper presents image reconstruction in Diffuse Optical Tomography (DOT) using a high-order finite element method. DOT is a non-invasive imaging modality for visualizing and continuously monitoring tissue and blood oxygenation levels in brain and breast. Image reconstruction in DOT leads to an inverse problem consisting of a forward problem and an iterative algorithm. The inverse problem in DOT systems is ill posed and depends on the accuracy of the forward problem. An accurate model, that describes the light transmission in tissue is required and can increase the spatial resolution. Using first order finite elements in the forward problem, numerical results are converged to the exact solution with increasing the number of elements. However, increasing the number of elements may cause a critical issue in the ill-posed inverse problem. This paper focuses on applying the high-order finite element method without increasing the number of elements, and image reconstruction is accomplished. The forward problem results are compared with analytical solutions. Images of absorbers reconstructed using this method are presented.
Iranian Journal of Biomedical Engineering
Iranian Society for Biomedical Engineering
5869-2008
4
v.
4
no.
2011
317
326
https://www.ijbme.org/article_13200_3830f0785499ea7428707f6e9087b7bf.pdf
dx.doi.org/10.22041/ijbme.2011.13200
Optimization of Stimulation Patterns in Paraplegic Walker-Assisted Walking using Functional Electrical Stimulation
Vahab
Nekoukar
Ph.D Student, Biomedical Engineering Group, Electrical Engineering Department, Iran University of Science and Technology
author
Abbas
Erfanian Omidvar
Associate professor, Biomedical Engineering Group, Electrical Engineering Department, Iran University of Science and Technology
author
text
article
2011
per
One major limitation of walker-supported walking using functional electrical stimulation (FES) in paraplegic subjects is the high energy expenditure and the high upper body effort. Paraplegics should exert high amount of hand force to stabilize the body posture and to compensate lack of the sufficient torques at the lower extremity joints. In this paper, we introduce a 2-D musculoskeletal model of walker-assisted FES-supported walking of paraplegics. Using the developed model and an optimal controller, the stimulation patterns are determined such that the tracking errors of lower joint reference trajectories are minimized and the muscle activations and the handle reaction force (HRF) are reduced. Outputs of the optimal controller are stimulation patterns of the lower body muscles and torque acting on the upper body joints. The results show that the HRF and ground reaction force (GRF) generated by simulation are in agreement with the measured HRF and GRF. Moreover, the results indicate that the simulation-generated stimulation patterns of lower body muscles are in consist with the stimulation patterns reported in the literatures.
Iranian Journal of Biomedical Engineering
Iranian Society for Biomedical Engineering
5869-2008
4
v.
4
no.
2011
327
336
https://www.ijbme.org/article_13201_59bbe8d549c59f0f5f85e616f2f25273.pdf
dx.doi.org/10.22041/ijbme.2011.13201
A survey of Neonatal Brain Atlases based on MR Images
Hamid
Abrishami Moghaddam
Professor, Biomedical Engineering Group, Electrical and Computer Engineering Department, K.N. Toosi University of Technology
author
Maryam
Momeni
Ph.D Student, Biomedical Engineering Group, Electrical and Computer Engineering Department, K.N. Toosi University of Technology
author
Kamran
Kazemi
Assistant Professor, Communication Engineering Group, Electrical Engineering Department, Shiraz University of Technology
author
Reinhard
Grebe
Professor, Biophysics Group, Medical Department, Picardie University
author
Fabrice
Wallois
Associate Professor, Neurophysiology Group, Medical Department, Picardie University
author
text
article
2011
per
Diagnostic follow-up of the brain development during the neonatal period and childhood is an important clinical task. Any disturbance of this process can cause pathological deviations, especially if the baby is born premature. Recent advances in magnetic resonance imaging allow obtaining high-resolution images of the neonatal brain. After segmenting the brains they can be used to reconstruct and model changes occurring during neonatal brain development. In addition such near-realistic model of the head, including the skin, skull and brain can be used to solve the inverse problem of determining the sources of registered signals from electrical brain activity. Although there exist numerous methods and various modeling schemes for adults, these cannot be used directly for neonates due to important differences in morphology. In this review article, neonatal brain atlases are divided into three categories: individual atlases, probabilistic atlases and stochastic atlases. In the following, existing neonatal brain atlases are placed in this classification and their methods of construction are presented. Furthermore, strengths and weaknesses of those neonatal brain atlases are analyzed and finally future research trends in this area are explained.
Iranian Journal of Biomedical Engineering
Iranian Society for Biomedical Engineering
5869-2008
4
v.
4
no.
2011
337
360
https://www.ijbme.org/article_13202_72d5e19331cc6da3dec68273c14f5ac7.pdf
dx.doi.org/10.22041/ijbme.2011.13202