Mohamad Diab - Academia.edu (original) (raw)
Papers by Mohamad Diab
Usage of VGRF in Biometrics: Application on Healthy and Parkinson Gaits
Biometric systems use unique information about or from a person to identify that person. Investme... more Biometric systems use unique information about or from a person to identify that person. Investments and research in this era has increased more than ever before. However, such technology is facing major fraud risks and pitfalls. This paper examines the use of VGRF in biometrics as a tool to be fused with an existing biometric system for reliable person identification. Results ensure that the use of raw data of VGRF is trustworthy if extracted from a normal subject. However, the results deteriorated in the presence of an abnormal gait. For instance, Gait subjects affected with Parkinson decreases the accuracy of classification among subjects ending up in high bias among results.
A mother wavelet selection study for vertical ground reaction force signals
Wavelet transform (WT) is a recent mathematical tool widely used in biomedical field. Its applica... more Wavelet transform (WT) is a recent mathematical tool widely used in biomedical field. Its application enormously spans the field of signal and image processing. The first step in applying this transform is to select the appropriate mother wavelet (MW). However, there is no standard method in the literature that could be used to choose a convenient MW for the analysis of a given data. While few techniques have been proposed, the present work aims to select the optimum MW for Vertical Ground Reaction Force (VGRF) signals through the computation of the maximum compression ratio (CR). In this study, fifty-three orthogonal MWs have been analyzed using the discrete wavelet transform (DWT). Then, sequential steps have been applied on hundred signals extracted from participants walking at their usual speed. The obtained results show that sym1, db1, bior1.1 and rbio1.1 compress maximally the VGRF signals (50.56%). Therefore, the use of these MWs for VGRF signal analysis is recommended.
Artificial Neural Network Based on Ensemble Empirical Mode Decomposition Features for Human Gait Diagnosis and Classification
Human gait analysis has been widely used to assess the stage of disease affecting the walking abi... more Human gait analysis has been widely used to assess the stage of disease affecting the walking ability. The gait signals, namely vertical ground reaction force signals, become more unsteady and non-linear with the progress of the disease. This paper makes use of ground reaction force signals measured from both normal and subjects diagnosed with Parkinson. New features are then extracted from different intrinsic mode functions as a result of the ensemble mode decomposition. The extracted features are divided randomly into a training set of 60%, a validation set of 15% and testing set of 25%. The neural network is then employed which yield an interesting overall classification accuracy of 95.7 %. This paper will pave the way for better rehabilitative programs, understanding of gait biomechanics and fall prevention among the elderly.
Classification of multichannel uterine EMG signals by using a weighted majority voting decision fusion rule
Recording the bioelectrical signals by using multiple sensors has been the subject of considerabl... more Recording the bioelectrical signals by using multiple sensors has been the subject of considerable research effort in the recent years. The multisensor recordings have opened the way to the application of more advanced signal processing techniques and the extraction of new parameters. The focus of this paper is to demonstrate the importance of multisensor recordings for classifying multichannel uterine EMG
Multichannel analysis is an innovative technique used for the analysis of bioelectrical signals. ... more Multichannel analysis is an innovative technique used for the analysis of bioelectrical signals. In this paper, we analyzed uterine Electromyogram (EMG) signals recorded by means of a 4x4 electrode matrix positioned on the woman's abdomen by using a multichannel approach. Relevant features were extracted from each channel and fed to a competitive neural network (CNN). First, we evaluated the classification performance of each channel. Then, we compared these performances to see which channel ranks better than the others. Finally, a decision fusion method based on the weighted sum of the individual decision of each channel was tested. The results showed that data can be grouped into 2 different groups. Furthermore, they showed that the classification performance varies according to the position of the electrode. Therefore, when a decision fusion rule was applied, the network yielded better classification accuracy than any individual channel could provide. These encouraging results prove that multichannel analysis can improve the classification of uterine EMG signals.
Multichannel analysis is an innovative technique used for the analysis of bioelectrical signals. ... more Multichannel analysis is an innovative technique used for the analysis of bioelectrical signals. In this paper, we analyzed uterine Electromyogram (EMG) signals recorded by means of a 4x4 electrode matrix positioned on the woman's abdomen by using a multichannel approach. Relevant features were extracted from each channel and fed to a competitive neural network (CNN). First, we evaluated the classification performance of each channel. Then, we compared these performances to see which channel ranks better than the others. Finally, a decision fusion method based on the weighted sum of the individual decision of each channel was tested. The results showed that data can be grouped into 2 different groups. Furthermore, they showed that the classification performance varies according to the position of the electrode. Therefore, when a decision fusion rule was applied, the network yielded better classification accuracy than any individual channel could provide. These encouraging results prove that multichannel analysis can improve the classification of uterine EMG signals.
Fall Risk Assessment Using Pressure Insole Sensors and Convolutional Neural Networks
2022 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES), Dec 7, 2022
Design of Robotic Manipulator to Hollow Out Zucchini
Volume 6: Design, Systems, and Complexity
Robotics entered the food industry starting from packaging to cooking. Zucchini is an important d... more Robotics entered the food industry starting from packaging to cooking. Zucchini is an important dish in the Middle Eastern kitchen. The eventual challenge of hollowing out Zucchini is to avoid poking its bottom with the corer. This paper introduces a novel robotic mechanism for hollowing out zucchini tasks precisely and efficiently. The mobility of the robot arm ensured a smooth hollowing while the corer is put in motion. Moreover, frames are assigned to the tip of the corer, holder, zucchini and camera to avoid stabbing the zucchini bottom. Accordingly, the special Euclidean group of the homogenous transformation matrices derived between different links are discussed in the context of their properties. The kinematic analysis is based on Mozzi-Chasles’ theorem rather than using the traditional Denavit Hartenberg convention for better task-oriented planning of hollowing out zucchini mechanism. Results indicate a promising mechanism that is well designed, simple and easy to build, mai...
International Journal of Integrated Engineering, 2021
Falls are a prevalent and severe health problem in the elderly community, leading to unfortunate ... more Falls are a prevalent and severe health problem in the elderly community, leading to unfortunate and devastating consequences. Some falls can be prevented through interventions, proper management, and extra care. Therefore, studying and identifying elderly people with risk of falls is essential to minimize the falling risk and to minimize the severity of injuries that can occur from these falls. Besides, identifying at-risk patients can profoundly affect public health in a positive way. In this paper, we use classification techniques to identify at-risk patients using pressure signals of the innersoles of 520 elderly people. These people reported whether they had experienced previous falls or not. Two different types of feature sets were used as inputs to the classification models and were compared: The first feature set includes time-domain, physiological, and cyclostationary features, whereas the second includes a subset of those features chosen by Relief-F as the most important f...
Murine Atherosclerosis Detection Using Machine Learning Under Magnetic Resonance Imaging
2020 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES), 2021
Over the past few decades, the diagnosis of heart disease, namely atherosclerosis, has been of in... more Over the past few decades, the diagnosis of heart disease, namely atherosclerosis, has been of interest for many researchers. The use of electrocardiogram (ECG) triggered Magnetic Resonance Imaging (MRI) sequences, enables exploration of aortic arches with high-resolution images. Lately, implementation of numerous Machine Learning (ML) techniques improved the accuracy of diagnosing diseases especially cardiovascular ones. In this paper, we proposed a model to diagnose atherosclerosis in mice using features extracted from the electrocardiogram (ECG) and respiratory signals. We defined new variables, after adaptation to the altered nature of electrophysiological signals with superimposed MRI noise. Using the 5-fold cross validation test, we introduced the features into the Random Forest (RF) classifier and obtained an accuracy of 96.96%. Feature selection then took place using the Info Gain (IG) attribute evaluator, which increased our model's accuracy to 97.57%. This presents a first step towards a real-time MRI synchronous cardiovascular finding system.
Robotic Interface Controller for Minimally Invasive Surgery
2018 1st International Conference on Computer Applications & Information Security (ICCAIS), 2018
This study aims to assemble a touch screen controlled surgical robotic arm, with the ability to o... more This study aims to assemble a touch screen controlled surgical robotic arm, with the ability to overcome the lack of haptic feedback encountered during minimally invasive surgery (MIS). The objective is to advance feasibility of solutions for millimeter scale movement in critical surgery areas. We achieved a prototype integrating servo-motors, resistive screen, microcontroller and end-effector measurement sensor. We implemented calibration, command and feed-back processes using Arduino codes. We were able to deliver minimal motion of surgical alike tool in different directions. According to requirements, the detected output values allowed distinction between hard and soft materials that simulate various human tissues. However, the developed exploration will facilitate the completion of haptic feed-back system after implementing communication between sensors on both side of operation.
Synchrosqueezing Characterize Non-stationary Signals: Application on Gait-Vertical Ground Reaction Force
2016 Global Summit on Computer & Information Technology (GSCIT), 2016
Signal is a physical quantity we can measure like gait vertical ground reaction force. However, t... more Signal is a physical quantity we can measure like gait vertical ground reaction force. However, the latter are such non- stationary signals require a deep understanding of their instantaneous amplitude, phase and frequency. From this, one can model its stationary and non- stationary part and approximate the noise. In addition, one can practice such features for inter-subject classification of the vertical ground reaction force signals like between normal and pathological. Not to add, one objective could also concentrate in intra-subject classification like between usual gait and gait associated with cognitive tasks for the same subject as this paper mainly concerns. For that purpose, Synchrosqueezing of time-frequency representation is being used to spot its power in non-stationary signal analysis and classification. This technique also helped in developing an accurate detection of outliers within such time series signal like when subjects encounter turning points during walking. All this would help in a correct assessing treatment effectiveness and précising the stage of disease. In addition, this would be a starting point for having accurate parameters in elderly fall detection.
Bioinspired Approach to Inverse Kinematic Problem
Biomedical Engineering and Computational Intelligence, 2019
In robotics, inverse kinematics is mapping the end-effector location and orientation to joint ang... more In robotics, inverse kinematics is mapping the end-effector location and orientation to joint angles. In this paper, the challenge behind finding a solution in inverse kinematics is tackled through minimizing the energy introduced in joints and the energy required by the mechanism as a whole. Studying integrated energies in the joints found in the human arm can give a new approach in understanding and solving inverse kinematics problem constrained by following the optimized path. Results are based on screw motion theorem, introduced by Charles and Mozzi. Moreover, the proposed technique and the results are exposed through simulation of three-link redundant manipulators that resemble human arm.
Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies, 2019
The success of chemotherapy treatment is achieved based on "Chronotherapy": the concept of admini... more The success of chemotherapy treatment is achieved based on "Chronotherapy": the concept of administering the correct drug at a precise time based on the circadian rhythm study. This paper aims to detect the rest/activity cycle and automatically calculate the dichotomy index (I<O), as both parameters have been proved to be reliable indices of the circadian rhythm. First, the DARC "Détection Automatique du Rythme Circadien" algorithm is used to segment the rest-activity phases automatically. Then, a Graphical User Interface (GUI) is used to calculate easily the I<O across several days of records and smooth the analysis. The outcome of this study provides an easy-to-use GUI that minimizes patients' intervention, facilitates user involvement, and reduces the time required for analysis.
Prediction of Elderly Falls Using the Degree of Cyclostationarity of Walk Pressure Signals
2020 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES), 2021
There is an increasing interest in developing older adult fall-risk prediction models that can be... more There is an increasing interest in developing older adult fall-risk prediction models that can be used as a preventive approach to predict future risk of falling in the elderly community. This study's primary objective is to implement and compare supervised machine-learning methods to classify elderly subjects as fallers or non-fallers. Features used for building the models were extracted from the pressure signals of the innersoles of 520 elderly people who reported whether they had experienced previous falls or not. Two different types of feature sets were used as inputs to the classification models and were compared. The first feature set includes ten time-domain features, while the second set includes a single cyclostationary property, which is the degree of cyclostationarity of the average walk pressure signal. Our study showed that the use of the degree of cyclostationarity as a single feature improved the model prediction accuracy by 6.58%, compared to the use of the time-...
Comparing Healthy Subjects and Alzheimer’s Disease Patients using Brain Network Similarity: a Preliminary Study
2021 Sixth International Conference on Advances in Biomedical Engineering (ICABME), 2021
Brain network analysis is an interdisciplinary field linking computational neuroscience with biom... more Brain network analysis is an interdisciplinary field linking computational neuroscience with biomedical data analytics, aiming for instance to map the brain into interconnected regions at different conditions, resting versus inactivity, and normal versus pathological. In our study, brain connectivity modeling and analysis are performed via graph theory. Several studies have revealed alterations in structural/functional brain networks of people diagnosed with several brain disorders. Most of the studies in the literature used graph theoretical approaches to characterize these disorders, however less attention was given for distance-based approaches (or network similarity). Our objective here is to compare the brain networks of normal versus Alzheimer’s disease (AD) patients by performing distance-based graph similarity analysis between their electrophysiological brain networks. The brain networks of a group of 10 healthy control subjects and 10 AD patients were constructed from Electroencephalography (EEG) signals recorded at rest, followed by the computation of intra- and inter-group network similarity via Siminet and DeltaCon algorithms at the EEG alpha and beta frequency bands. Results showed that AD networks have significantly lower similarity scores and tend to be more heterogenous with respect to the healthy networks. This work provides a preliminary foundation for the effective use of graph similarity in the computational assessment of pathological brain networks compared to healthy subjects.
Smart Autonomous Wheelchair
2019 3rd International Conference on Bio-engineering for Smart Technologies (BioSMART), 2019
Smart wheelchairs are an assistive wheeled mobility device. The world health organization reporte... more Smart wheelchairs are an assistive wheeled mobility device. The world health organization reported 10 % of global population (650 million person) have disability and 10% among them need wheelchair. Wheelchair made it easier for many to pursue their life activities including education, work and social life. A smart autonomous wheelchair is developed in this paper to enhance the maneuvering tasks. The wheelchair requires no human intervention during navigation and perception in addition to processing which is based on computer vision techniques.
Journal of Computer and Communications, 2018
The electrocardiogram (ECG) signal used for diagnosis and patient monitoring, has recently emerge... more The electrocardiogram (ECG) signal used for diagnosis and patient monitoring, has recently emerged as a biometric recognition tool. Indeed, ECG signal changes from one person to another according to health status, heart geometry and anatomy among other factors. This paper forms a comparative study between different identification techniques and their performances. Previous works in this field referred to methodologies implementing either set of fiducial or set non-fiducial features. In this study we show a comparison of the same data using a fiducial feature set and a non-fiducial feature set based on statistical calculation of wavelet coefficient. High identification rates were measured in both cases, non-fiducial using Discrete Meyer (dmey) wavelet outperformed the rest at 98.65.
A Review on ECG-Based Biometric Authentication Systems
Series in BioEngineering, 2019
The objectives of this chapter is three-folds: First, it presents an overview of the existing ECG... more The objectives of this chapter is three-folds: First, it presents an overview of the existing ECG benchmarks used for designing ECG-based authentication systems. Second, it presents the literatures of authentication systems that used fiducial and non-fiducial features. Third, it presents a methodology that uses both fiducial and non-fiducial features and several data mining classification techniques for individuals’ authentication. Moreover this chapter investigates the pertinent features using a large database of healthy and unhealthy subjects with different heart diseases.
Haptic Feedback in Surgical Robotic Interface Controller
2019 3rd International Conference on Bio-engineering for Smart Technologies (BioSMART), 2019
Minimally invasive surgery (MIS) reduces trauma and risks of infection but lacks corresponding di... more Minimally invasive surgery (MIS) reduces trauma and risks of infection but lacks corresponding diagnostic information due to lost vision and palpation of tissues and organs. Recent surgical master-slave systems have no haptic feedback. The ultimate goal of this study is to design a robotic system that will be useful in remote surgery. The system should enable the processing and exchange of force and tremor information between the end-effector side, handling tissue, and the hand held interface being manipulated by the surgeon. The system consists of a touch screen and servo motors based robotic arm. The robotic arm is capable of performing distant surgical manipulations on a stable miniaturized scale. The system enables sensing the operator exerted pressure, through the touch screen, simulating the ability to assess the density of the object where the surgery is being performed.
Usage of VGRF in Biometrics: Application on Healthy and Parkinson Gaits
Biometric systems use unique information about or from a person to identify that person. Investme... more Biometric systems use unique information about or from a person to identify that person. Investments and research in this era has increased more than ever before. However, such technology is facing major fraud risks and pitfalls. This paper examines the use of VGRF in biometrics as a tool to be fused with an existing biometric system for reliable person identification. Results ensure that the use of raw data of VGRF is trustworthy if extracted from a normal subject. However, the results deteriorated in the presence of an abnormal gait. For instance, Gait subjects affected with Parkinson decreases the accuracy of classification among subjects ending up in high bias among results.
A mother wavelet selection study for vertical ground reaction force signals
Wavelet transform (WT) is a recent mathematical tool widely used in biomedical field. Its applica... more Wavelet transform (WT) is a recent mathematical tool widely used in biomedical field. Its application enormously spans the field of signal and image processing. The first step in applying this transform is to select the appropriate mother wavelet (MW). However, there is no standard method in the literature that could be used to choose a convenient MW for the analysis of a given data. While few techniques have been proposed, the present work aims to select the optimum MW for Vertical Ground Reaction Force (VGRF) signals through the computation of the maximum compression ratio (CR). In this study, fifty-three orthogonal MWs have been analyzed using the discrete wavelet transform (DWT). Then, sequential steps have been applied on hundred signals extracted from participants walking at their usual speed. The obtained results show that sym1, db1, bior1.1 and rbio1.1 compress maximally the VGRF signals (50.56%). Therefore, the use of these MWs for VGRF signal analysis is recommended.
Artificial Neural Network Based on Ensemble Empirical Mode Decomposition Features for Human Gait Diagnosis and Classification
Human gait analysis has been widely used to assess the stage of disease affecting the walking abi... more Human gait analysis has been widely used to assess the stage of disease affecting the walking ability. The gait signals, namely vertical ground reaction force signals, become more unsteady and non-linear with the progress of the disease. This paper makes use of ground reaction force signals measured from both normal and subjects diagnosed with Parkinson. New features are then extracted from different intrinsic mode functions as a result of the ensemble mode decomposition. The extracted features are divided randomly into a training set of 60%, a validation set of 15% and testing set of 25%. The neural network is then employed which yield an interesting overall classification accuracy of 95.7 %. This paper will pave the way for better rehabilitative programs, understanding of gait biomechanics and fall prevention among the elderly.
Classification of multichannel uterine EMG signals by using a weighted majority voting decision fusion rule
Recording the bioelectrical signals by using multiple sensors has been the subject of considerabl... more Recording the bioelectrical signals by using multiple sensors has been the subject of considerable research effort in the recent years. The multisensor recordings have opened the way to the application of more advanced signal processing techniques and the extraction of new parameters. The focus of this paper is to demonstrate the importance of multisensor recordings for classifying multichannel uterine EMG
Multichannel analysis is an innovative technique used for the analysis of bioelectrical signals. ... more Multichannel analysis is an innovative technique used for the analysis of bioelectrical signals. In this paper, we analyzed uterine Electromyogram (EMG) signals recorded by means of a 4x4 electrode matrix positioned on the woman's abdomen by using a multichannel approach. Relevant features were extracted from each channel and fed to a competitive neural network (CNN). First, we evaluated the classification performance of each channel. Then, we compared these performances to see which channel ranks better than the others. Finally, a decision fusion method based on the weighted sum of the individual decision of each channel was tested. The results showed that data can be grouped into 2 different groups. Furthermore, they showed that the classification performance varies according to the position of the electrode. Therefore, when a decision fusion rule was applied, the network yielded better classification accuracy than any individual channel could provide. These encouraging results prove that multichannel analysis can improve the classification of uterine EMG signals.
Multichannel analysis is an innovative technique used for the analysis of bioelectrical signals. ... more Multichannel analysis is an innovative technique used for the analysis of bioelectrical signals. In this paper, we analyzed uterine Electromyogram (EMG) signals recorded by means of a 4x4 electrode matrix positioned on the woman's abdomen by using a multichannel approach. Relevant features were extracted from each channel and fed to a competitive neural network (CNN). First, we evaluated the classification performance of each channel. Then, we compared these performances to see which channel ranks better than the others. Finally, a decision fusion method based on the weighted sum of the individual decision of each channel was tested. The results showed that data can be grouped into 2 different groups. Furthermore, they showed that the classification performance varies according to the position of the electrode. Therefore, when a decision fusion rule was applied, the network yielded better classification accuracy than any individual channel could provide. These encouraging results prove that multichannel analysis can improve the classification of uterine EMG signals.
Fall Risk Assessment Using Pressure Insole Sensors and Convolutional Neural Networks
2022 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES), Dec 7, 2022
Design of Robotic Manipulator to Hollow Out Zucchini
Volume 6: Design, Systems, and Complexity
Robotics entered the food industry starting from packaging to cooking. Zucchini is an important d... more Robotics entered the food industry starting from packaging to cooking. Zucchini is an important dish in the Middle Eastern kitchen. The eventual challenge of hollowing out Zucchini is to avoid poking its bottom with the corer. This paper introduces a novel robotic mechanism for hollowing out zucchini tasks precisely and efficiently. The mobility of the robot arm ensured a smooth hollowing while the corer is put in motion. Moreover, frames are assigned to the tip of the corer, holder, zucchini and camera to avoid stabbing the zucchini bottom. Accordingly, the special Euclidean group of the homogenous transformation matrices derived between different links are discussed in the context of their properties. The kinematic analysis is based on Mozzi-Chasles’ theorem rather than using the traditional Denavit Hartenberg convention for better task-oriented planning of hollowing out zucchini mechanism. Results indicate a promising mechanism that is well designed, simple and easy to build, mai...
International Journal of Integrated Engineering, 2021
Falls are a prevalent and severe health problem in the elderly community, leading to unfortunate ... more Falls are a prevalent and severe health problem in the elderly community, leading to unfortunate and devastating consequences. Some falls can be prevented through interventions, proper management, and extra care. Therefore, studying and identifying elderly people with risk of falls is essential to minimize the falling risk and to minimize the severity of injuries that can occur from these falls. Besides, identifying at-risk patients can profoundly affect public health in a positive way. In this paper, we use classification techniques to identify at-risk patients using pressure signals of the innersoles of 520 elderly people. These people reported whether they had experienced previous falls or not. Two different types of feature sets were used as inputs to the classification models and were compared: The first feature set includes time-domain, physiological, and cyclostationary features, whereas the second includes a subset of those features chosen by Relief-F as the most important f...
Murine Atherosclerosis Detection Using Machine Learning Under Magnetic Resonance Imaging
2020 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES), 2021
Over the past few decades, the diagnosis of heart disease, namely atherosclerosis, has been of in... more Over the past few decades, the diagnosis of heart disease, namely atherosclerosis, has been of interest for many researchers. The use of electrocardiogram (ECG) triggered Magnetic Resonance Imaging (MRI) sequences, enables exploration of aortic arches with high-resolution images. Lately, implementation of numerous Machine Learning (ML) techniques improved the accuracy of diagnosing diseases especially cardiovascular ones. In this paper, we proposed a model to diagnose atherosclerosis in mice using features extracted from the electrocardiogram (ECG) and respiratory signals. We defined new variables, after adaptation to the altered nature of electrophysiological signals with superimposed MRI noise. Using the 5-fold cross validation test, we introduced the features into the Random Forest (RF) classifier and obtained an accuracy of 96.96%. Feature selection then took place using the Info Gain (IG) attribute evaluator, which increased our model's accuracy to 97.57%. This presents a first step towards a real-time MRI synchronous cardiovascular finding system.
Robotic Interface Controller for Minimally Invasive Surgery
2018 1st International Conference on Computer Applications & Information Security (ICCAIS), 2018
This study aims to assemble a touch screen controlled surgical robotic arm, with the ability to o... more This study aims to assemble a touch screen controlled surgical robotic arm, with the ability to overcome the lack of haptic feedback encountered during minimally invasive surgery (MIS). The objective is to advance feasibility of solutions for millimeter scale movement in critical surgery areas. We achieved a prototype integrating servo-motors, resistive screen, microcontroller and end-effector measurement sensor. We implemented calibration, command and feed-back processes using Arduino codes. We were able to deliver minimal motion of surgical alike tool in different directions. According to requirements, the detected output values allowed distinction between hard and soft materials that simulate various human tissues. However, the developed exploration will facilitate the completion of haptic feed-back system after implementing communication between sensors on both side of operation.
Synchrosqueezing Characterize Non-stationary Signals: Application on Gait-Vertical Ground Reaction Force
2016 Global Summit on Computer & Information Technology (GSCIT), 2016
Signal is a physical quantity we can measure like gait vertical ground reaction force. However, t... more Signal is a physical quantity we can measure like gait vertical ground reaction force. However, the latter are such non- stationary signals require a deep understanding of their instantaneous amplitude, phase and frequency. From this, one can model its stationary and non- stationary part and approximate the noise. In addition, one can practice such features for inter-subject classification of the vertical ground reaction force signals like between normal and pathological. Not to add, one objective could also concentrate in intra-subject classification like between usual gait and gait associated with cognitive tasks for the same subject as this paper mainly concerns. For that purpose, Synchrosqueezing of time-frequency representation is being used to spot its power in non-stationary signal analysis and classification. This technique also helped in developing an accurate detection of outliers within such time series signal like when subjects encounter turning points during walking. All this would help in a correct assessing treatment effectiveness and précising the stage of disease. In addition, this would be a starting point for having accurate parameters in elderly fall detection.
Bioinspired Approach to Inverse Kinematic Problem
Biomedical Engineering and Computational Intelligence, 2019
In robotics, inverse kinematics is mapping the end-effector location and orientation to joint ang... more In robotics, inverse kinematics is mapping the end-effector location and orientation to joint angles. In this paper, the challenge behind finding a solution in inverse kinematics is tackled through minimizing the energy introduced in joints and the energy required by the mechanism as a whole. Studying integrated energies in the joints found in the human arm can give a new approach in understanding and solving inverse kinematics problem constrained by following the optimized path. Results are based on screw motion theorem, introduced by Charles and Mozzi. Moreover, the proposed technique and the results are exposed through simulation of three-link redundant manipulators that resemble human arm.
Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies, 2019
The success of chemotherapy treatment is achieved based on "Chronotherapy": the concept of admini... more The success of chemotherapy treatment is achieved based on "Chronotherapy": the concept of administering the correct drug at a precise time based on the circadian rhythm study. This paper aims to detect the rest/activity cycle and automatically calculate the dichotomy index (I<O), as both parameters have been proved to be reliable indices of the circadian rhythm. First, the DARC "Détection Automatique du Rythme Circadien" algorithm is used to segment the rest-activity phases automatically. Then, a Graphical User Interface (GUI) is used to calculate easily the I<O across several days of records and smooth the analysis. The outcome of this study provides an easy-to-use GUI that minimizes patients' intervention, facilitates user involvement, and reduces the time required for analysis.
Prediction of Elderly Falls Using the Degree of Cyclostationarity of Walk Pressure Signals
2020 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES), 2021
There is an increasing interest in developing older adult fall-risk prediction models that can be... more There is an increasing interest in developing older adult fall-risk prediction models that can be used as a preventive approach to predict future risk of falling in the elderly community. This study's primary objective is to implement and compare supervised machine-learning methods to classify elderly subjects as fallers or non-fallers. Features used for building the models were extracted from the pressure signals of the innersoles of 520 elderly people who reported whether they had experienced previous falls or not. Two different types of feature sets were used as inputs to the classification models and were compared. The first feature set includes ten time-domain features, while the second set includes a single cyclostationary property, which is the degree of cyclostationarity of the average walk pressure signal. Our study showed that the use of the degree of cyclostationarity as a single feature improved the model prediction accuracy by 6.58%, compared to the use of the time-...
Comparing Healthy Subjects and Alzheimer’s Disease Patients using Brain Network Similarity: a Preliminary Study
2021 Sixth International Conference on Advances in Biomedical Engineering (ICABME), 2021
Brain network analysis is an interdisciplinary field linking computational neuroscience with biom... more Brain network analysis is an interdisciplinary field linking computational neuroscience with biomedical data analytics, aiming for instance to map the brain into interconnected regions at different conditions, resting versus inactivity, and normal versus pathological. In our study, brain connectivity modeling and analysis are performed via graph theory. Several studies have revealed alterations in structural/functional brain networks of people diagnosed with several brain disorders. Most of the studies in the literature used graph theoretical approaches to characterize these disorders, however less attention was given for distance-based approaches (or network similarity). Our objective here is to compare the brain networks of normal versus Alzheimer’s disease (AD) patients by performing distance-based graph similarity analysis between their electrophysiological brain networks. The brain networks of a group of 10 healthy control subjects and 10 AD patients were constructed from Electroencephalography (EEG) signals recorded at rest, followed by the computation of intra- and inter-group network similarity via Siminet and DeltaCon algorithms at the EEG alpha and beta frequency bands. Results showed that AD networks have significantly lower similarity scores and tend to be more heterogenous with respect to the healthy networks. This work provides a preliminary foundation for the effective use of graph similarity in the computational assessment of pathological brain networks compared to healthy subjects.
Smart Autonomous Wheelchair
2019 3rd International Conference on Bio-engineering for Smart Technologies (BioSMART), 2019
Smart wheelchairs are an assistive wheeled mobility device. The world health organization reporte... more Smart wheelchairs are an assistive wheeled mobility device. The world health organization reported 10 % of global population (650 million person) have disability and 10% among them need wheelchair. Wheelchair made it easier for many to pursue their life activities including education, work and social life. A smart autonomous wheelchair is developed in this paper to enhance the maneuvering tasks. The wheelchair requires no human intervention during navigation and perception in addition to processing which is based on computer vision techniques.
Journal of Computer and Communications, 2018
The electrocardiogram (ECG) signal used for diagnosis and patient monitoring, has recently emerge... more The electrocardiogram (ECG) signal used for diagnosis and patient monitoring, has recently emerged as a biometric recognition tool. Indeed, ECG signal changes from one person to another according to health status, heart geometry and anatomy among other factors. This paper forms a comparative study between different identification techniques and their performances. Previous works in this field referred to methodologies implementing either set of fiducial or set non-fiducial features. In this study we show a comparison of the same data using a fiducial feature set and a non-fiducial feature set based on statistical calculation of wavelet coefficient. High identification rates were measured in both cases, non-fiducial using Discrete Meyer (dmey) wavelet outperformed the rest at 98.65.
A Review on ECG-Based Biometric Authentication Systems
Series in BioEngineering, 2019
The objectives of this chapter is three-folds: First, it presents an overview of the existing ECG... more The objectives of this chapter is three-folds: First, it presents an overview of the existing ECG benchmarks used for designing ECG-based authentication systems. Second, it presents the literatures of authentication systems that used fiducial and non-fiducial features. Third, it presents a methodology that uses both fiducial and non-fiducial features and several data mining classification techniques for individuals’ authentication. Moreover this chapter investigates the pertinent features using a large database of healthy and unhealthy subjects with different heart diseases.
Haptic Feedback in Surgical Robotic Interface Controller
2019 3rd International Conference on Bio-engineering for Smart Technologies (BioSMART), 2019
Minimally invasive surgery (MIS) reduces trauma and risks of infection but lacks corresponding di... more Minimally invasive surgery (MIS) reduces trauma and risks of infection but lacks corresponding diagnostic information due to lost vision and palpation of tissues and organs. Recent surgical master-slave systems have no haptic feedback. The ultimate goal of this study is to design a robotic system that will be useful in remote surgery. The system should enable the processing and exchange of force and tremor information between the end-effector side, handling tissue, and the hand held interface being manipulated by the surgeon. The system consists of a touch screen and servo motors based robotic arm. The robotic arm is capable of performing distant surgical manipulations on a stable miniaturized scale. The system enables sensing the operator exerted pressure, through the touch screen, simulating the ability to assess the density of the object where the surgery is being performed.