ORCID Profile
0000-0002-8840-9235
Current Organisations
Electrical Engineering Technical College, Middle Technical University
,
University of South Australia
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Publisher: MDPI AG
Date: 03-02-2023
DOI: 10.3390/APP13031972
Abstract: Malware is the primary attack vector against the modern enterprise. Therefore, it is crucial for businesses to exclude malware from their computer systems. The most responsive solution to this issue would operate in real time at the edge of the IT system using artificial intelligence. However, a lightweight solution is crucial at the edge because these options are restricted by the lack of available memory and processing power. The best contender to offer such a solution is application programming interface (API) calls. However, creating API call characteristics that offer a high malware detection rate with quick execution is a significant challenge. This work uses visualisation analysis and Jaccard similarity to uncover the hidden patterns produced by different API calls in order to accomplish this goal. This study also compared neural networks which use long sequences of API calls with shallow machine learning classifiers. Three classifiers are used: support vector machine (SVM), k-nearest neighbourhood (KNN), and random forest (RF). The benchmark data set comprises 43,876 ex les of API call sequences, ided into two categories: malware and legitimate. The results showed that RF performed similarly to long short-term memory (LSTM) and deep graph convolutional neural networks (DGCNNs). They also suggest the potential for performing inference on edge devices in a real-time setting.
Publisher: MDPI AG
Date: 09-03-2022
DOI: 10.3390/COMPUTERS11030039
Abstract: Data security is the science of protecting data in information technology, including authentication, data encryption, data decryption, data recovery, and user protection. To protect data from unauthorized disclosure and modification, a secure algorithm should be used. Many techniques have been proposed to encrypt text to an image. Most past studies used RGB layers to encrypt text to an image. In this paper, a Text-to-Image Encryption-Decryption (TTIED) algorithm based on Cyan, Magenta, Yellow, Key/Black (CMYK) mode is proposed to improve security, capacity, and processing time. The results show that the capacity increased from one to four times compared to RGB mode. Security was also improved due to a decrease in the probability of an adversary discovering keys. The processing time ranged between 0.001 ms (668 characters) and 31 s (25 million characters), depending on the length of the text. The compression rate for the encrypted file was decreased compared to WinRAR. In this study, Arabic and English texts were encrypted and decrypted.
Publisher: MDPI AG
Date: 30-04-2020
DOI: 10.3390/S20092549
Abstract: Most wearable intelligent biomedical sensors are battery-powered. The batteries are large and relatively heavy, adding to the volume of wearable sensors, especially when implanted. In addition, the batteries have limited capacity, requiring periodic charging, as well as a limited life, requiring potentially invasive replacement. This paper aims to design and implement a prototype energy harvesting technique based on wireless power transfer/magnetic resonator coupling (WPT/MRC) to overcome the battery power problem by supplying adequate power for a heart rate sensor. We optimized transfer power and efficiency at different distances between transmitter and receiver coils. The proposed MRC consists of three units: power, measurement, and monitoring. The power unit included transmitter and receiver coils. The measurement unit consisted of an Arduino Nano microcontroller, a heart rate sensor, and used the nRF24L01 wireless protocol. The experimental monitoring unit was supported by a laptop to monitor the heart rate measurement in real-time. Three coil topologies: spiral–spiral, spider–spider, and spiral–spider were implemented for testing. These topologies were examined to explore which would be the best for the application by providing the highest transfer power and efficiency. The spiral–spider topology achieved the highest transfer power and efficiency with 10 W at 87%, respectively over a 5 cm air gap between transmitter and receiver coils when a 200 Ω resistive load was considered. Whereas, the spider–spider topology accomplished 7 W and 93% transfer power and efficiency at the same airgap and resistive load. The proposed topologies were superior to previous studies in terms of transfer power, efficiency and distance.
Publisher: Informa UK Limited
Date: 20-08-2023
Publisher: MDPI AG
Date: 05-07-2023
DOI: 10.3390/BIOMEDINFORMATICS3030037
Abstract: Neonatal jaundice is a prevalent condition among newborns, with potentially severe complications that can result in permanent brain damage if left untreated during its early stages. The existing approaches for jaundice detection involve invasive procedures such as blood s le collection, which can inflict pain and distress on the patient, and may give rise to additional complications. Alternatively, a non-invasive method using image-processing techniques and implementing kNN, Random Forest, and XGBoost machine learning algorithms as a classifier can be employed to diagnose jaundice, necessitating a comprehensive database of infant images to achieve a diagnosis with high accuracy. This data article presents the NJN collection, a repository of newborn images encompassing erse birthweights and skin tones, spanning an age range of 2 to 8 days. The dataset is accompanied by an Excel sheet file in CSV format containing the RGB and YCrCb channel values, as well as the status of each s le. The dataset and associated resources are openly accessible at Zenodo website. Moreover, the Python code for data testing utilizing various AI techniques is provided. Consequently, this article offers an unparalleled resource for AI researchers, enabling them to train their AI systems and develop algorithms that can assist neonatal intensive care unit (NICU) healthcare specialists in monitoring neonates while facilitating the fast, real-time, non-invasive, and accurate diagnosis of jaundice.
Publisher: MDPI AG
Date: 26-09-2021
Abstract: Biomedical sensors help patients monitor their health conditions and receive assistance anywhere and at any time. However, the limited battery capacity of medical devices limits their functionality. One advantageous method to tackle this limited-capacity issue is to employ the wireless power transfer (WPT) technique. In this paper, a WPT technique using a magnetic resonance coupling (MRC-WPT)-based wireless heart rate (WHR) monitoring system—which continuously records the heart rate of patients—has been designed, and its efficiency is confirmed through real-time implementation. The MRC-WPT involves three main units: the transmitter, receiver, and observing units. In this research, a new design of spiral-spider coil was designed and implemented for transmitter and receiver units, respectively, to supply the measurement unit, which includes a heart rate sensor, microcontroller, and wireless protocol (nRF24L01) with the operating voltage. The experimental results found that an adequate voltage of 5 V was achieved by the power component to operate the measurement unit at a 20 cm air gap between the receiver and transmitter coils. Further, the measurement accuracy of the WHR was 99.65% comparative to the benchmark (BM) instrument. Moreover, the measurements of the WHR were validated based on statistical analyses. The results of this study are superior to those of leading works in terms of measurement accuracy, power transfer, and Transfer efficiency.
Publisher: Middle Technical University
Date: 31-03-2023
Abstract: Facial palsy (FP) is a disorder that affects the seventh facial nerve, which makes the patient unable to control facial movements and expressions with other vital activities. It affects one side of the face, and it is usually diagnosed by the asymmetry of the two sides of the face through visual inspection by a doctor. However, the visual inspection is human-based, which is prone to errors because the doctor is exposed to omission due to fatigue and work stress. Therefore, it is important to develop a new method for detecting FP through artificial intelligence and use a more accurate computerized system to reduce the effort and cost of patients and increase the accuracy of diagnosis. This work aims to establish a safe, useful and high-accuracy diagnostic system for FP that can be used by the patient and proposes to detect FP using a digital camera and deep learning techniques automatically. The system could be used by the patient himself at home without needing to visit the hospital. The proposed system trained 570 images, including 200 images of FP palsy. The proposed FP system achieved an accuracy of 98%. This confirms the effectiveness of the proposed system and makes it an efficient medical examination tool for detecting FP.
Publisher: IOP Publishing
Date: 06-2021
DOI: 10.1088/1757-899X/1105/1/012005
Abstract: Irrigation consumes 70% of the water quantity used worldwide. In a context of rising food demand and declining in water resources, the development of advanced irrigation technologies based on modern techniques in agriculture is a significant demand to keep this resource safe. To achieve this target, the management of water resources in agriculture needs to be specified and controlled. This study aims to propose an automatic, non-contact and cost-effective soil irrigation system based on analysing the changes in loam soil colour captured by a digital camera at different illumination levels. A graphic user interface (GUI) attached to the Arduino Uno microcontroller was used to drive the water pump and determine whether the loam soil requires irrigation or not. The experimental results illustrate the effectiveness of the proposed irrigation system to determine soil state and provide an accurate decision for soil irrigating, thus making this system a promising approach in future irrigation technologies.
Publisher: MDPI AG
Date: 09-03-2022
DOI: 10.3390/COMPUTERS11030039
Abstract: Data security is the science of protecting data in information technology, including authentication, data encryption, data decryption, data recovery, and user protection. To protect data from unauthorized disclosure and modification, a secure algorithm should be used. Many techniques have been proposed to encrypt text to an image. Most past studies used RGB layers to encrypt text to an image. In this paper, a Text-to-Image Encryption-Decryption (TTIED) algorithm based on Cyan, Magenta, Yellow, Key/Black (CMYK) mode is proposed to improve security, capacity, and processing time. The results show that the capacity increased from one to four times compared to RGB mode. Security was also improved due to a decrease in the probability of an adversary discovering keys. The processing time ranged between 0.001 ms (668 characters) and 31 s (25 million characters), depending on the length of the text. The compression rate for the encrypted file was decreased compared to WinRAR. In this study, Arabic and English texts were encrypted and decrypted.
Publisher: MDPI AG
Date: 09-02-2020
DOI: 10.3390/RS12030577
Abstract: In search and rescue operations, it is crucial to rapidly identify those people who are alive from those who are not. If this information is known, emergency teams can prioritize their operations to save more lives. However, in some natural disasters the people may be lying on the ground covered with dust, debris, or ashes making them difficult to detect by video analysis that is tuned to human shapes. We present a novel method to estimate the locations of people from aerial video using image and signal processing designed to detect breathing movements. We have shown that this method can successfully detect clearly visible people and people who are fully occluded by debris. First, the aerial videos were stabilized using the key points of adjacent image frames. Next, the stabilized video was decomposed into tile videos and the temporal frequency bands of interest were motion magnified while the other frequencies were suppressed. Image differencing and temporal filtering were performed on each tile video to detect potential breathing signals. Finally, the detected frequencies were remapped to the image frame creating a life signs map that indicates possible human locations. The proposed method was validated with both aerial and ground recorded videos in a controlled environment. Based on the dataset, the results showed good reliability for aerial videos and no errors for ground recorded videos where the average precision measures for aerial videos and ground recorded videos were 0.913 and 1 respectively.
Publisher: MDPI AG
Date: 08-10-2021
Abstract: Jaundice or Hyperbilirubinemia is a very common condition that affects newborns in their first few weeks of life. The main cause of jaundice is the high level of the bilirubin substance in the blood. As bilirubin is toxic to brain cells, acute bilirubin encephalopathy can occur in cases of extreme jaundice. This condition can result in brain trauma and lead to kernicterus, which causes repetitive and uncontrolled movements, a permanent upward look, and hearing loss. Thus, a timely diagnosis and treatment can help in preventing long-term damage. In this paper, a developed system based on a digital camera was proposed to diagnose and treat jaundice in newborns. The system detects jaundice and determines if the neonate needs treatment based on the analysis obtained from the real-time captured images. The treatment was achieved by using an Arduino Uno microcontroller to drive phototherapy lighting, which has proven to be an efficient treatment method for jaundice. In addition, the proposed system has the ability to send the diagnostic results to the mobile phone of the care provider. The obtained results from 20 infants inside the intensive care unit showed that the proposed system was accurate in terms of detecting jaundice, easy to implement, and affordable.
Publisher: IOP Publishing
Date: 26-09-2018
Publisher: MDPI AG
Date: 21-10-2019
DOI: 10.3390/RS11202441
Abstract: In the aftermath of a disaster, such as earthquake, flood, or avalanche, ground search for survivors is usually h ered by unstable surfaces and difficult terrain. Drones now play an important role in these situations, allowing rescuers to locate survivors and allocate resources to saving those who can be helped. The aim of this study was to explore the utility of a drone equipped for human life detection with a novel computer vision system. The proposed system uses image sequences captured by a drone camera to remotely detect the cardiopulmonary motion caused by periodic chest movement of survivors. The results of eight human subjects and one mannequin in different poses shows that motion detection on the body surface of the survivors is likely to be useful to detect life signs without any physical contact. The results presented in this study may lead to a new approach to life detection and remote life sensing assessment of survivors.
Publisher: MDPI AG
Date: 07-05-2021
DOI: 10.3390/APP11094255
Abstract: Patients with the COVID-19 condition require frequent and accurate blood oxygen saturation (SpO2) monitoring. The existing pulse oximeters, however, require contact-based measurement using clips or otherwise fixed sensor units or need dedicated hardware which may cause inconvenience and involve additional appointments with the patient. This study proposes a computer vision-based system using a digital camera to measure SpO2 on the basis of the imaging photoplethysmography (iPPG) signal extracted from the human’s forehead without the need for restricting the subject or physical contact. The proposed camera-based system decomposes the iPPG obtained from the red and green channels into different signals with different frequencies using a signal decomposition technique based on a complete Ensemble Empirical Mode Decomposition (EEMD) technique and Independent Component Analysis (ICA) technique to obtain the optical properties from these wavelengths and frequency channels. The proposed system is convenient, contactless, safe and cost-effective. The preliminary results for 70 videos obtained from 14 subjects of different ages and with different skin tones showed that the red and green wavelengths could be used to estimate SpO2 with good agreement and low error ratio compared to the gold standard of pulse oximetry (SA210) with a fixed measurement position.
Publisher: AIP Publishing
Date: 2023
DOI: 10.1063/5.0154589
Publisher: AIP Publishing
Date: 2023
DOI: 10.1063/5.0154228
Publisher: MDPI AG
Date: 26-12-2020
DOI: 10.3390/COMPUTERS10010005
Abstract: Technological advances have allowed hand gestures to become an important research field especially in applications such as health care and assisting applications for elderly people, providing a natural interaction with the assisting system through a camera by making specific gestures. In this study, we proposed three different scenarios using a Microsoft Kinect V2 depth sensor then evaluated the effectiveness of the outcomes. The first scenario used joint tracking combined with a depth threshold to enhance hand segmentation and efficiently recognise the number of fingers extended. The second scenario utilised the metadata parameters provided by the Kinect V2 depth sensor, which provided 11 parameters related to the tracked body and gave information about three gestures for each hand. The third scenario used a simple convolutional neural network with joint tracking by depth metadata to recognise and classify five hand gesture categories. In this study, deaf-mute elderly people performed five different hand gestures, each related to a specific request, such as needing water, meal, toilet, help and medicine. Next, the request was sent via the global system for mobile communication (GSM) as a text message to the care provider’s smartphone because the elderly subjects could not execute any activity independently.
Publisher: MDPI AG
Date: 26-07-2020
DOI: 10.20944/PREPRINTS202007.0625.V1
Abstract: Hand gestures may play an important role in medical applications for health care of elderly people, where providing a natural interaction for different requests can be executed by making specific gestures. In this study we explored three different scenarios using a Microsoft Kinect V2 depth sensor then evaluated the effectiveness of the outcomes. The first scenario utilized the default system embedded in the Kinect V2 sensor, which depth metadata gives 11 parameters related to the tracked body with five gestures for each hand. The second scenario used joint tracking provided by Kinect depth metadata and depth threshold together to enhance hand segmentation and efficiently recognize the number of fingers extended. The third scenario used a simple convolutional neural network with joint tracking by depth metadata to recognize five categories of gestures. In this study, deaf-mute elderly people execute five different hand gestures to indicate a specific request, such as needing water, meal, toilet, help and medicine. Then, the requests were sent to the care provider& rsquo s smartphone because elderly people could not execute any activity independently. The system transferred these requests as a message through the global system for mobile communication (GSM) using a microcontroller.
Publisher: MDPI AG
Date: 08-10-2021
Abstract: Jaundice or Hyperbilirubinemia is a very common condition that affects newborns in their first few weeks of life. The main cause of jaundice is the high level of the bilirubin substance in the blood. As bilirubin is toxic to brain cells, acute bilirubin encephalopathy can occur in cases of extreme jaundice. This condition can result in brain trauma and lead to kernicterus, which causes repetitive and uncontrolled movements, a permanent upward look, and hearing loss. Thus, a timely diagnosis and treatment can help in preventing long-term damage. In this paper, a developed system based on a digital camera was proposed to diagnose and treat jaundice in newborns. The system detects jaundice and determines if the neonate needs treatment based on the analysis obtained from the real-time captured images. The treatment was achieved by using an Arduino Uno microcontroller to drive phototherapy lighting, which has proven to be an efficient treatment method for jaundice. In addition, the proposed system has the ability to send the diagnostic results to the mobile phone of the care provider. The obtained results from 20 infants inside the intensive care unit showed that the proposed system was accurate in terms of detecting jaundice, easy to implement, and affordable.
Publisher: Springer Science and Business Media LLC
Date: 08-08-2017
Publisher: MDPI AG
Date: 26-12-2020
DOI: 10.3390/COMPUTERS10010005
Abstract: Technological advances have allowed hand gestures to become an important research field especially in applications such as health care and assisting applications for elderly people, providing a natural interaction with the assisting system through a camera by making specific gestures. In this study, we proposed three different scenarios using a Microsoft Kinect V2 depth sensor then evaluated the effectiveness of the outcomes. The first scenario used joint tracking combined with a depth threshold to enhance hand segmentation and efficiently recognise the number of fingers extended. The second scenario utilised the metadata parameters provided by the Kinect V2 depth sensor, which provided 11 parameters related to the tracked body and gave information about three gestures for each hand. The third scenario used a simple convolutional neural network with joint tracking by depth metadata to recognise and classify five hand gesture categories. In this study, deaf-mute elderly people performed five different hand gestures, each related to a specific request, such as needing water, meal, toilet, help and medicine. Next, the request was sent via the global system for mobile communication (GSM) as a text message to the care provider’s smartphone because the elderly subjects could not execute any activity independently.
Publisher: MDPI AG
Date: 29-03-2019
DOI: 10.3390/JLPEA9020013
Abstract: In this paper, the performance of coded systems is considered in the presence of Suzuki fading channels, which is a combination of both short-fading and long-fading channels. The problem in manipulating a Suzuki fading model is the complicated integration involved in the evaluation of the Suzuki probability density function (PDF). In this paper, we calculated noise PDF after the zero-forcing equalizer (ZFE) at the receiver end with several approaches. In addition, we used the derived PDF to calculate the log-likelihood ratios (LLRs) for turbo-coded systems, and results were compared to Gaussian distribution-based LLRs. The results showed a 2 dB improvement in performance compared to traditional LLRs at 10 - 6 of the bit error rate (BER) with no added complexity. Simulations were obtained utilizing the Matlab program, and results showed good improvement in the performance of the turbo-coded system with the proposed LLRs compared to Gaussian-based LLRs.
Publisher: MDPI AG
Date: 20-03-2018
DOI: 10.3390/S18030920
Publisher: MDPI AG
Date: 23-10-2018
DOI: 10.3390/EN11112866
Abstract: Falls are the main source of injury for elderly patients with epilepsy and Parkinson’s disease. Elderly people who carry battery powered health monitoring systems can move unhindered from one place to another according to their activities, thus improving their quality of life. This paper aims to detect when an elderly in idual falls and to provide accurate location of the incident while the in idual is moving in indoor environments such as in houses, medical health care centers, and hospitals. Fall detection is accurately determined based on a proposed sensor-based fall detection algorithm, whereas the localization of the elderly person is determined based on an artificial neural network (ANN). In addition, the power consumption of the fall detection system (FDS) is minimized based on a data-driven algorithm. Results show that an elderly fall can be detected with accuracy levels of 100% and 92.5% for line-of-sight (LOS) and non-line-of-sight (NLOS) environments, respectively. In addition, elderly indoor localization error is improved with a mean absolute error of 0.0094 and 0.0454 m for LOS and NLOS, respectively, after the application of the ANN optimization technique. Moreover, the battery life of the FDS is improved relative to conventional implementation due to reduced computational effort. The proposed FDS outperforms existing systems in terms of fall detection accuracy, localization errors, and power consumption.
Publisher: MDPI AG
Date: 24-05-2019
DOI: 10.3390/JSAN8020032
Abstract: Continuous monitoring of breathing activity plays a major role in detecting and classifying a breathing abnormality. This work aims to facilitate detection of abnormal breathing syndromes, including tachypnea, bradypnea, central apnea, and irregular breathing by tracking of thorax movement resulting from respiratory rhythms based on ultrasonic radar detection. This paper proposes a non-contact, non-invasive, low cost, low power consumption, portable, and precise system for simultaneous monitoring of normal and abnormal breathing activity in real-time using an ultrasonic PING sensor and microcontroller PIC18F452. Moreover, the obtained abnormal breathing syndrome is reported to the concerned physician’s mobile telephone through a global system for mobile communication (GSM) modem to handle the case depending on the patient’s emergency condition. In addition, the power consumption of the proposed monitoring system is reduced via a duty cycle using an energy-efficient sleep/wake scheme. Experiments were conducted on 12 participants without any physical contact at different distances of 0.5, 1, 2, and 3 m and the breathing rates measured with the proposed system were then compared with those measured by a piezo respiratory belt transducer. The experimental results illustrate the feasibility of the proposed system to extract breathing rate and detect the related abnormal breathing syndromes with a high degree of agreement, strong correlation coefficient, and low error ratio. The results also showed that the total current consumption of the proposed monitoring system based on the sleep/wake scheme was 6.936 mA compared to 321.75 mA when the traditional operation was used instead. Consequently, this led to a 97.8% of power savings and extended the battery life time from 8 h to approximately 370 h. The proposed monitoring system could be used in both clinical and home settings.
Publisher: MDPI AG
Date: 22-10-2019
DOI: 10.3390/APP9204474
Abstract: Techniques for noncontact measurement of vital signs using camera imaging technologies have been attracting increasing attention. For noncontact physiological assessments, computer vision-based methods appear to be an advantageous approach that could be robust, hygienic, reliable, safe, cost effective and suitable for long distance and long-term monitoring. In addition, video techniques allow measurements from multiple in iduals opportunistically and simultaneously in groups. This paper aims to explore the progress of the technology from controlled clinical scenarios with fixed monitoring installations and controlled lighting, towards uncontrolled environments, crowds and moving sensor platforms. We focus on the ersity of applications and scenarios being studied in this topic. From this review it emerges that automatic multiple regions of interest (ROIs) selection, removal of noise artefacts caused by both illumination variations and motion artefacts, simultaneous multiple person monitoring, long distance detection, multi-camera fusion and accepted publicly available datasets are topics that still require research to enable the technology to mature into many real-world applications.
Publisher: MDPI AG
Date: 23-07-2020
Abstract: Hand gestures are a form of nonverbal communication that can be used in several fields such as communication between deaf-mute people, robot control, human–computer interaction (HCI), home automation and medical applications. Research papers based on hand gestures have adopted many different techniques, including those based on instrumented sensor technology and computer vision. In other words, the hand sign can be classified under many headings, such as posture and gesture, as well as dynamic and static, or a hybrid of the two. This paper focuses on a review of the literature on hand gesture techniques and introduces their merits and limitations under different circumstances. In addition, it tabulates the performance of these methods, focusing on computer vision techniques that deal with the similarity and difference points, technique of hand segmentation used, classification algorithms and drawbacks, number and types of gestures, dataset used, detection range (distance) and type of camera used. This paper is a thorough general overview of hand gesture methods with a brief discussion of some possible applications.
Publisher: Institution of Engineering and Technology (IET)
Date: 2017
Publisher: Informa UK Limited
Date: 28-02-2019
Publisher: AIP Publishing
Date: 2023
DOI: 10.1063/5.0154231
Publisher: Informa UK Limited
Date: 27-04-2017
DOI: 10.1080/03091902.2017.1313326
Abstract: The aim of this work is to remotely measure heart rate (HR) and respiratory rate (RR) using a video camera from long range (> 50 m). The proposed system is based on imperceptible signals produced from blood circulation, including skin colour variations and head motion. As these signals are not visible to the naked eye and to preserve the signal strength in the video, we used an improved video magnification technique to enhance these invisible signals and detect the physiological activity within the subject. The software of the proposed system was built in a graphic user interface (GUI) environment to easily select a magnification system to use (colour or motion magnification) and measure the physiological signs independently. The measurements were performed on a set of 10 healthy subjects equipped with a finger pulse oximeter and respiratory belt transducer that were used as reference methods. The experimental results were statistically analysed by using the Bland-Altman method, Pearson's correlation coefficient, Spearman correlation coefficient, mean absolute error, and root mean squared error. The proposed system achieved high correlation even in the presence of movement artefacts, different skin tones, lighting conditions and distance from the camera. With acceptable performance and low computational complexity, the proposed system is a suitable candidate for homecare applications, security applications and mobile health devices.
Publisher: IEEE
Date: 25-08-2021
Publisher: MDPI AG
Date: 18-09-2022
DOI: 10.3390/INVENTIONS7030084
Abstract: Blood pressure (BP) is one of the most common vital signs related to cardiovascular diseases. BP is traditionally measured by mercury, aneroid, or digital sphygmomanometers however, these approaches are restrictive, inconvenient, and need a pressure cuff to be attached directly to the patient. Therefore, it is clinically important to develop an innovative system that can accurately measure BP without the need for any direct physical contact with the people. This work aims to create a new computer vision system that remotely measures BP using a digital camera without a pressure cuff. The proposed BP system extracts the optical properties of photoplethysmographic signals in two regions in the forehead captured by a digital camera and calculates BP based on specific formulas. The experiments were performed on 25 human participants with different skin tones and repeated at different times under ambient light conditions. Compared to the systolic/diastolic BP readings obtained from a commercial digital sphygmomanometer, the proposed BP system achieves an accuracy of 94.6% with a root mean square error (RMSE) of 9.2 mmHg for systolic BP readings and an accuracy of 95.4% with an RMSE of 7.6 mmHg for diastolic BP readings. Thus, the proposed BP system has the potential of being a promising tool in the upcoming generation of BP monitoring systems.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
Publisher: World Scientific Pub Co Pte Lt
Date: 2017
DOI: 10.1142/S0219467817500012
Abstract: The aim of this study is to remotely measure cardiac activity (heart pulse, total cycle length and pulse width) from videos based on a head motion at different positions of the head (front, back and side). As the head motion resulting from the cardiac cycle of blood from the heart to the head via the carotid arteries is not visible to the naked eye and to preserve the signal strength in the video, we used wavelet decomposition and a Chebychev filter to develop a standard Eulerian video magnification in terms of noise removal and execution time. We used both magnification systems to measure cardiac activity and statistically compare the results using Bland–Altman method. Also, we proposed a new video quality system based on fuzzy interface system to select which magnification system has better magnification quality and gives better results for the heart pulse rate. The experimental results on several videos captured from 10 healthy subjects show that the proposed contactless system of heart pulse has an accuracy of 98.3% when magnified video based on the developing magnification system was used and an accuracy of 97.4% when magnified video based on Eulerian magnification system was used instead. The proposed system has low computational complexity, making it suitable for advancing health care applications, mobile health applications and telemedicine.
Publisher: AIP Publishing
Date: 2023
DOI: 10.1063/5.0154237
Publisher: MDPI AG
Date: 13-06-2023
DOI: 10.3390/BIOMEDINFORMATICS3020031
Abstract: Facial palsy (FP) is a neurological disorder that affects the facial nerve, specifically the seventh nerve, resulting in the patient losing control of the facial muscles on one side of the face. It is an annoying condition that can occur in both children and adults, regardless of gender. Diagnosis by visual examination, based on differences in the sides of the face, can be prone to errors and inaccuracies. The detection of FP using artificial intelligence through computer vision systems has become increasingly important. Deep learning is the best solution for detecting FP in real-time with high accuracy, saving patients time, effort, and cost. Therefore, this work proposes a real-time detection system for FP, and for determining the patient’s gender and age, using a Raspberry Pi device with a digital camera and a deep learning algorithm. The solution facilitates the diagnosis process for both the doctor and the patient, and it could be part of a medical assessment activity. This study used a dataset of 20,600 images, containing 19,000 normal images and 1600 FP images, to achieve an accuracy of 98%. Thus, the proposed system is a highly accurate and capable medical diagnostic tool for detecting FP.
Publisher: AIP Publishing
Date: 2023
DOI: 10.1063/5.0154239
Publisher: MDPI AG
Date: 11-11-2022
DOI: 10.3390/COMPUTERS11110160
Abstract: Malware is used to carry out malicious operations on networks and computer systems. Consequently, malware classification is crucial for preventing malicious attacks. Application programming interfaces (APIs) are ideal candidates for characterizing malware behavior. However, the primary challenge is to produce API call features for classification algorithms to achieve high classification accuracy. To achieve this aim, this work employed the Jaccard similarity and visualization analysis to find the hidden patterns created by various malware API calls. Traditional machine learning classifiers, i.e., random forest (RF), support vector machine (SVM), and k-nearest neighborhood (KNN), were used in this research as alternatives to existing neural networks, which use millions of length API call sequences. The benchmark dataset used in this study contains 7107 s les of API call sequences (labeled to eight different malware families). The results showed that RF with the proposed API call features outperformed the LSTM (long short-term memory) and gated recurrent unit (GRU)-based methods against overall evaluation metrics.
Publisher: MDPI AG
Date: 20-10-2021
DOI: 10.3390/APP11219813
Abstract: Cardiac arrest (CA) in infants is an issue worldwide, which causes significant morbidity and mortality rates. Cardiopulmonary resuscitation (CPR) is a technique performed in case of CA to save victims’ lives. However, CPR is often not performed effectively, even when delivered by qualified rescuers. Therefore, international guidelines have proposed applying a CPR feedback device to achieve high-quality application of CPR to enhance survival rates. Currently, no feedback device is available to guide learners through infant CPR performance in contrast to a number of adult CPR feedback devices. This study presents a real-time feedback system to improve infant CPR performance by medical staff and laypersons using a commercial CPR infant manikin. The proposed system uses an IR sensor to compare CPR performance obtained with no feedback and with a real-time feedback system. Performance was validated by analysis of the CPR parameters actually delivered against the recommended target parameters. Results show that the real-time feedback system significantly improves the quality of chest compression parameters. The two-thumb compression technique is the achievable and appropriate mechanism applied to infant subjects for delivering high-quality CPR. Under the social distancing constraints imposed by the SARS-CoV-2 pandemic, the results from the training device were sent to a CPR training center and provided each participant with CPR proficiency.
Publisher: MDPI AG
Date: 18-09-2022
DOI: 10.3390/INVENTIONS7030084
Abstract: Blood pressure (BP) is one of the most common vital signs related to cardiovascular diseases. BP is traditionally measured by mercury, aneroid, or digital sphygmomanometers however, these approaches are restrictive, inconvenient, and need a pressure cuff to be attached directly to the patient. Therefore, it is clinically important to develop an innovative system that can accurately measure BP without the need for any direct physical contact with the people. This work aims to create a new computer vision system that remotely measures BP using a digital camera without a pressure cuff. The proposed BP system extracts the optical properties of photoplethysmographic signals in two regions in the forehead captured by a digital camera and calculates BP based on specific formulas. The experiments were performed on 25 human participants with different skin tones and repeated at different times under ambient light conditions. Compared to the systolic/diastolic BP readings obtained from a commercial digital sphygmomanometer, the proposed BP system achieves an accuracy of 94.6% with a root mean square error (RMSE) of 9.2 mmHg for systolic BP readings and an accuracy of 95.4% with an RMSE of 7.6 mmHg for diastolic BP readings. Thus, the proposed BP system has the potential of being a promising tool in the upcoming generation of BP monitoring systems.
Publisher: MDPI AG
Date: 05-02-2021
Abstract: The World Health Organization (WHO) has declared COVID-19 a pandemic. We review and reduce the clinical literature on diagnosis of COVID-19 through symptoms that might be remotely detected as of early May 2020. Vital signs associated with respiratory distress and fever, coughing, and visible infections have been reported. Fever screening by temperature monitoring is currently popular. However, improved noncontact detection is sought. Vital signs including heart rate and respiratory rate are affected by the condition. Cough, fatigue, and visible infections are also reported as common symptoms. There are non-contact methods for measuring vital signs remotely that have been shown to have acceptable accuracy, reliability, and practicality in some settings. Each has its pros and cons and may perform well in some challenges but be inadequate in others. Our review shows that visible spectrum and thermal spectrum cameras offer the best options for truly noncontact sensing of those studied to date, thermal cameras due to their potential to measure all likely symptoms on a single camera, especially temperature, and video cameras due to their availability, cost, adaptability, and compatibility. Substantial supply chain disruptions during the pandemic and the widespread nature of the problem means that cost-effectiveness and availability are important considerations.
Publisher: Middle Technical University
Date: 04-2023
DOI: 10.51173/JT.V5I1.896
Abstract: Urine color analysis is one of the most helpful indicators of health status, and any changes in urine color might be a symptom of serious disease, dehydration of the body, or caused by drugs. To get better assistance for urine color detection in the proposed system, a urine color automatic identification has been developed based on computer vision. The proposed system uses a web camera to capture an image in real-time, analyze it, and then classify the color of urine by using the random forest (RF) algorithm and show the result via the Graphical User Interface (GUI). In addition, the proposed system can send the results to the mobile phone of the patient or care provider by using an Arduino microcontroller and GSM module. Moreover, sending a voice message about the color of urine is related to pathological conditions. The results showed that the proposed system has high accuracy (approximately about 97%) in detecting urine color under different light conditions, with low cost, short time, and easy implementation. In the comparison with the current methods the proposed system has maximum accuracy and minimum error rate. This methodology can pave the way for an additional case study in medical applications, particularly in diagnosis, and patient health monitoring.
Publisher: MDPI AG
Date: 07-05-2021
DOI: 10.3390/APP11094255
Abstract: Patients with the COVID-19 condition require frequent and accurate blood oxygen saturation (SpO2) monitoring. The existing pulse oximeters, however, require contact-based measurement using clips or otherwise fixed sensor units or need dedicated hardware which may cause inconvenience and involve additional appointments with the patient. This study proposes a computer vision-based system using a digital camera to measure SpO2 on the basis of the imaging photoplethysmography (iPPG) signal extracted from the human’s forehead without the need for restricting the subject or physical contact. The proposed camera-based system decomposes the iPPG obtained from the red and green channels into different signals with different frequencies using a signal decomposition technique based on a complete Ensemble Empirical Mode Decomposition (EEMD) technique and Independent Component Analysis (ICA) technique to obtain the optical properties from these wavelengths and frequency channels. The proposed system is convenient, contactless, safe and cost-effective. The preliminary results for 70 videos obtained from 14 subjects of different ages and with different skin tones showed that the red and green wavelengths could be used to estimate SpO2 with good agreement and low error ratio compared to the gold standard of pulse oximetry (SA210) with a fixed measurement position.
Publisher: MDPI AG
Date: 05-07-2023
DOI: 10.3390/BIOMEDINFORMATICS3030037
Abstract: Neonatal jaundice is a prevalent condition among newborns, with potentially severe complications that can result in permanent brain damage if left untreated during its early stages. The existing approaches for jaundice detection involve invasive procedures such as blood s le collection, which can inflict pain and distress on the patient, and may give rise to additional complications. Alternatively, a non-invasive method using image-processing techniques and implementing kNN, Random Forest, and XGBoost machine learning algorithms as a classifier can be employed to diagnose jaundice, necessitating a comprehensive database of infant images to achieve a diagnosis with high accuracy. This data article presents the NJN collection, a repository of newborn images encompassing erse birthweights and skin tones, spanning an age range of 2 to 8 days. The dataset is accompanied by an Excel sheet file in CSV format containing the RGB and YCrCb channel values, as well as the status of each s le. The dataset and associated resources are openly accessible at Zenodo website. Moreover, the Python code for data testing utilizing various AI techniques is provided. Consequently, this article offers an unparalleled resource for AI researchers, enabling them to train their AI systems and develop algorithms that can assist neonatal intensive care unit (NICU) healthcare specialists in monitoring neonates while facilitating the fast, real-time, non-invasive, and accurate diagnosis of jaundice.
Publisher: MDPI AG
Date: 30-04-2020
DOI: 10.3390/S20092549
Abstract: Most wearable intelligent biomedical sensors are battery-powered. The batteries are large and relatively heavy, adding to the volume of wearable sensors, especially when implanted. In addition, the batteries have limited capacity, requiring periodic charging, as well as a limited life, requiring potentially invasive replacement. This paper aims to design and implement a prototype energy harvesting technique based on wireless power transfer/magnetic resonator coupling (WPT/MRC) to overcome the battery power problem by supplying adequate power for a heart rate sensor. We optimized transfer power and efficiency at different distances between transmitter and receiver coils. The proposed MRC consists of three units: power, measurement, and monitoring. The power unit included transmitter and receiver coils. The measurement unit consisted of an Arduino Nano microcontroller, a heart rate sensor, and used the nRF24L01 wireless protocol. The experimental monitoring unit was supported by a laptop to monitor the heart rate measurement in real-time. Three coil topologies: spiral–spiral, spider–spider, and spiral–spider were implemented for testing. These topologies were examined to explore which would be the best for the application by providing the highest transfer power and efficiency. The spiral–spider topology achieved the highest transfer power and efficiency with 10 W at 87%, respectively over a 5 cm air gap between transmitter and receiver coils when a 200 Ω resistive load was considered. Whereas, the spider–spider topology accomplished 7 W and 93% transfer power and efficiency at the same airgap and resistive load. The proposed topologies were superior to previous studies in terms of transfer power, efficiency and distance.
Publisher: Springer Science and Business Media LLC
Date: 30-05-2023
DOI: 10.3758/S13428-023-02136-Y
Abstract: Cardiac measures such as heart rate measurements are important indicators of both physiological and psychological states. However, despite their extraordinary potential, their use is restricted in comparative psychology because traditionally cardiac measures involved the attachment of sensors to the participant’s body, which, in the case of undomesticated animals such as nonhuman primates, is usually only possible during anesthesia or after extensive training. Here, we validate and apply a camera-based system that enables contact-free detection of animals’ heart rates. The system automatically detects and estimates the cardiac signals from cyclic change in the hue of the facial area of a chimpanzee. In Study 1, we recorded the heart rate of chimpanzees using the new technology, while simultaneously measuring heart rate using classic PPG (photoplethysmography) finger sensors. We found that both methods were in good agreement. In Study 2, we applied our new method to measure chimpanzees’ heart rate in response to seeing different types of video scenes (groupmates in an agonistic interaction, conspecific strangers feeding, nature videos, etc.). Heart rates changed during video presentation, depending on the video content: Agonistic interactions and conspecific strangers feeding lead to accelerated heart rate relative to baseline, indicating increased emotional arousal. Nature videos lead to decelerated heart rate relative to baseline, indicating a relaxing effect or heightened attention caused by these stimuli. Our results show that the new contact-free technology can reliably assess the heart rate of unrestrained chimpanzees, and most likely other primates. Furthermore, our technique opens up new avenues of research within comparative psychology and facilitates the health management of captive in iduals.
Publisher: JMIR Publications Inc.
Date: 29-08-2019
DOI: 10.2196/13400
Abstract: Biomedical research in the application of noncontact methods to measure heart rate (HR) and respiratory rate (RR) in the neonatal population has produced mixed results. This paper describes and discusses a protocol for conducting a method comparison study, which aims to determine the accuracy of a proposed noncontact computer vision system to detect HR and RR relative to the HR and RR obtained by 3-lead electrocardiogram (ECG) in preterm infants in the neonatal unit. The aim of this preliminary study is to determine the accuracy of a proposed noncontact computer vision system to detect HR and RR relative to the HR and RR obtained by 3-lead ECG in preterm infants in the neonatal unit. A single-center cross-sectional study was planned to be conducted in the neonatal unit at Flinders Medical Centre, South Australia, in May 2018. A total of 10 neonates and their ECG monitors will be filmed concurrently for 10 min using digital cameras. Advanced image processing techniques are to be applied later to determine their physiological data at 3 intervals. These data will then be compared with the ECG readings at the same points in time. Study enrolment began in May 2018. Results of this study were published in July 2019. The study will analyze the data obtained by the noncontact system in comparison to data obtained by ECG, identify factors that may influence data extraction and accuracy when filming infants, and provide recommendations for how this noncontact system may be implemented into clinical applications. RR1-10.2196/13400
Publisher: IOP Publishing
Date: 06-2021
DOI: 10.1088/1757-899X/1105/1/012070
Abstract: Computer vision has wide application in medical sciences such as health care and home automation. This study on computer vision for elderly care is based on a Microsoft Kinect sensor considers an inexpensive, three dimensional, non-contact technique, that is comfortable for patients while being highly reliable and suitable for long term monitoring. This paper proposes a hand gesture system for elderly health care based on deep learning convolutional neural network (CNN) that is used to extract features and to classify five gestures according to five categories using a support vector machine (SVM). The proposed system is beneficial for elderly patients who are voiceless or deaf-mute and unable to communicate with others. Each gesture indicates a specific request such as “Water”, “Meal”, “Toilet”, “Help” and “Medicine” and translates as a command sending to a Microcontroller circuit that sends the request to the caregiver’s mobile phone via the global system for mobile communication (GSM). The system was tested in an indoor environment and provides reliable outcomes and a useful interface for older people with disabilities in their limbs to communicate with their families and caregivers.
Publisher: MDPI AG
Date: 04-07-2019
DOI: 10.3390/S19132955
Abstract: For elderly persons, a fall can cause serious injuries such as a hip fracture or head injury. Here, an advanced first aid system is proposed for monitoring elderly patients with heart conditions that puts them at risk of falling and for providing first aid supplies using an unmanned aerial vehicle. A hybridized fall detection algorithm (FDB-HRT) is proposed based on a combination of acceleration and a heart rate threshold. Five volunteers were invited to evaluate the performance of the heartbeat sensor relative to a benchmark device, and the extracted data was validated using statistical analysis. In addition, the accuracy of fall detections and the recorded locations of fall incidents were validated. The proposed FDB-HRT algorithm was 99.16% and 99.2% accurate with regard to heart rate measurement and fall detection, respectively. In addition, the geolocation error of patient fall incidents based on a GPS module was evaluated by mean absolute error analysis for 17 different locations in three cities in Iraq. Mean absolute error was 1.08 × 10−5° and 2.01 × 10−5° for latitude and longitude data relative to data from the GPS Benchmark system. In addition, the results revealed that in urban areas, the UAV succeeded in all missions and arrived at the patient’s locations before the ambulance, with an average time savings of 105 s. Moreover, a time saving of 31.81% was achieved when using the UAV to transport a first aid kit to the patient compared to an ambulance. As a result, we can conclude that when compared to delivering first aid via ambulance, our design greatly reduces delivery time. The proposed advanced first aid system outperformed previous systems presented in the literature in terms of accuracy of heart rate measurement, fall detection, and information messages and UAV arrival time.
Publisher: MDPI AG
Date: 23-07-2021
Abstract: Infants with fragile skin are patients who would benefit from non-contact vital sign monitoring due to the avoidance of potentially harmful adhesive electrodes and cables. Non-contact vital signs monitoring has been studied in clinical settings in recent decades. However, studies on infants in the Neonatal Intensive Care Unit (NICU) are still limited. Therefore, we conducted a single-center study to remotely monitor the heart rate (HR) and respiratory rate (RR) of seven infants in NICU using a digital camera. The region of interest (ROI) was automatically selected using a convolutional neural network and signal decomposition was used to minimize the noise artefacts. The experimental results have been validated with the reference data obtained from an ECG monitor. They showed a strong correlation using the Pearson correlation coefficients (PCC) of 0.9864 and 0.9453 for HR and RR, respectively, and a lower error rate with RMSE 2.23 beats/min and 2.69 breaths/min between measured data and reference data. A Bland–Altman analysis of the data also presented a close correlation between measured data and reference data for both HR and RR. Therefore, this technique may be applicable in clinical environments as an economical, non-contact, and easily deployable monitoring system, and it also represents a potential application in home health monitoring.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: IOP Publishing
Date: 06-2021
DOI: 10.1088/1757-899X/1105/1/012077
Abstract: Cardiac arrest (CA) is a significant issue in infants worldwide, which causes disagreeable morbidity and mortality ratios. Thus, cardiopulmonary resuscitation (CPR) is a technique performed in case of cardiac arrest to save victims’ lives. The aim of CPR is to follow the blood flow promoting to the vital organs during the external chest provisional compressions. This technique has been reported to develop CA results. It was reported that CPR was not performed in high quality even when highly qualified rescuers delivered by CPR. Therefore, international guidelines have proposed for applying a CPR feedback device to release high-quality CPR to enhance survival rates. There is currently no feedback device available to guide learners through infant CPR performance compared to the adequate number of the adult in CPR feedback device. This study establishes a background knowledge to understand the CPR technique in infant populations by reviewing the following: the critical role of chest compression and rescue breath during the CPR process, the CPR standards, increasing the cardiac arrest survival rate by performing high-quality CPR, the effect of feedback on CPR performance., outlining the effect of different compression techniques on all the hemodynamic outcomes for delivering high-quality infant CPR.
Publisher: MDPI AG
Date: 23-07-2020
Abstract: Hand gestures are a form of nonverbal communication that can be used in several fields such as communication between deaf-mute people, robot control, human–computer interaction (HCI), home automation and medical applications. Research papers based on hand gestures have adopted many different techniques, including those based on instrumented sensor technology and computer vision. In other words, the hand sign can be classified under many headings, such as posture and gesture, as well as dynamic and static, or a hybrid of the two. This paper focuses on a review of the literature on hand gesture techniques and introduces their merits and limitations under different circumstances. In addition, it tabulates the performance of these methods, focusing on computer vision techniques that deal with the similarity and difference points, technique of hand segmentation used, classification algorithms and drawbacks, number and types of gestures, dataset used, detection range (distance) and type of camera used. This paper is a thorough general overview of hand gesture methods with a brief discussion of some possible applications.
Publisher: IOP Publishing
Date: 06-2021
DOI: 10.1088/1757-899X/1105/1/012076
Abstract: Physiological jaundice occurs in the first week of life in newborns due to the increase in bilirubin level which in turn leads to yellowish discolouration of skin and sclera. Sever jaundice and toxic level of bilirubin can cause brain damage as bilirubin exists in the central nervous systems. Invasive blood s ling is the optimum method to measure bilirubin level however, it is painful and stressful for the neonate, and it may cause blood loss and can lead to anaemia, especially when repeated blood tests are required. In addition, blood tests expose the infant to the risk of infections. Moreover, invasive tests are time-consuming as their results are not immediate. Due to all the problems mentioned earlier, this paper proposes a new system for jaundice detection that is based on skin colour analysis. The proposed system uses a digital camera as a colour based screening tool as it is affordable, objective, ubiquitous, and less painful to infants. Based on the analysis obtained from the captured images, jaundice was detected and estimated, opening the door for further case studies in medical applications, especially in diagnosis, monitoring patient’s health, and supplying active treatment.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Middle Technical University
Date: 31-03-2023
Abstract: At the beginning of their lives, newborns may have a widespread condition known as Jaundice or Hyperbilirubinemia. High levels of bilirubin in the blood are the primary cause of jaundice. Severe cases of jaundice may cause acute bilirubin encephalopathy due to the toxicity of bilirubin to the cells of the brain, which may lead to kernicterus. Kernicterus causes several symptoms, including a permanent upward look, loss of hearing, and repetitive and uncontrolled movements. Therefore, diagnosing this condition at the appropriate time helps to prevent chronic effects. In this study, jaundice or hyperbilirubinemia is diagnosed using a computer vision system based on a random forest algorithm. The system comprises a digital HD camera, a computer device with a Matlab application installed to analyze and detect the skin color changes of the infant, and an Arduino Uno microcontroller to control an LED ultraviolet light. A set of neonate images were collected to train the random forest algorithm, including 374 for normal and 137 for jaundiced infants. |The experimental results using the random forest algorithm for classification reached an accuracy of 98.4375%. The results of this study are promising and open doors for new monitoring applications in various medical diseases detection with a high degree of accuracy.
Publisher: Elsevier BV
Date: 2021
Publisher: World Scientific Pub Co Pte Lt
Date: 12-01-2016
DOI: 10.1142/S0218001417570014
Abstract: Vital parameter monitoring systems based on video camera imagery is a growing interest field in clinical and biomedical applications. Heart rate (HR) is one of the most important vital parameters of interest in a clinical diagnostic and monitoring system. This study proposed a noncontact HR and beat length measurement system based on both motion magnification and motion detection at four different regions of interest (ROIs) (wrist, arm, neck and leg). A motion magnification based on a Chebyshev filter was utilized in order to magnify heart pulses in different ROIs that are difficult to see with the naked eye. A new measuring system based on motion detection was used to measure HR and beat length by detecting rapid motion areas in the video frame sequences that represent the heart pulses and converting video frames into a corresponding logical matrix. Video quality metrics were also used to compare our magnification system with standard Eulerian video magnification to select which one has better magnification results and gives better results for the heart pulse. The 99.3% limits of agreement between the proposed system and reference measurement fall within[Formula: see text] beats/min based on Bland and Altman test. The proposed system is expected to produce new options for further noncontact information extraction.
Publisher: JMIR Publications Inc.
Date: 14-01-2019
Abstract: iomedical research in the application of noncontact methods to measure heart rate (HR) and respiratory rate (RR) in the neonatal population has produced mixed results. This paper describes and discusses a protocol for conducting a method comparison study, which aims to determine the accuracy of a proposed noncontact computer vision system to detect HR and RR relative to the HR and RR obtained by 3-lead electrocardiogram (ECG) in preterm infants in the neonatal unit. he aim of this preliminary study is to determine the accuracy of a proposed noncontact computer vision system to detect HR and RR relative to the HR and RR obtained by 3-lead ECG in preterm infants in the neonatal unit. single-center cross-sectional study was planned to be conducted in the neonatal unit at Flinders Medical Centre, South Australia, in May 2018. A total of 10 neonates and their ECG monitors will be filmed concurrently for 10 min using digital cameras. Advanced image processing techniques are to be applied later to determine their physiological data at 3 intervals. These data will then be compared with the ECG readings at the same points in time. tudy enrolment began in May 2018. Results of this study were published in July 2019. he study will analyze the data obtained by the noncontact system in comparison to data obtained by ECG, identify factors that may influence data extraction and accuracy when filming infants, and provide recommendations for how this noncontact system may be implemented into clinical applications. R1-10.2196/13400
Publisher: Springer Science and Business Media LLC
Date: 27-07-2019
DOI: 10.1038/S41390-019-0506-5
Abstract: Non-contact heart rate (HR) and respiratory rate (RR) monitoring is necessary for preterm infants due to the potential for the adhesive electrodes of conventional electrocardiogram (ECG) to cause damage to the epidermis. This study was performed to evaluate the agreement between HR and RR measurements of preterm infants using a non-contact computer vision system with comparison to measurements obtained by the ECG. A single-centre, cross-sectional observational study was conducted in a Neonatal Unit. Ten infants and their ECG monitors were videoed using two Nikon cameras for 10 min. HR and RR measurements obtained from the non-contact system were extracted using advanced signal processing techniques and later compared to the ECG readings using Bland-Altman analysis. The non-contact system was able to detect an apnoea when the ECG determined movement as respirations. Although the mean bias between both methods was relatively low, the limits of agreement for HR were -8.3 to 17.4 beats per minute (b.p.m.) and for RR, -22 to 23.6 respirations per minute (r.p.m.). This study provides necessary data for improving algorithms to address confounding variables common to the neonatal population. Further studies investigating the robustness of the proposed system for premature infants are therefore required.
Publisher: MDPI AG
Date: 10-12-2019
DOI: 10.3390/S19245445
Abstract: Monitoring the cardiopulmonary signal of animals is a challenge for veterinarians in conditions when contact with a conscious animal is inconvenient, difficult, damaging, distressing or dangerous to personnel or the animal subject. In this pilot study, we demonstrate a computer vision-based system and use ex les of exotic, untamed species to demonstrate this means to extract the cardiopulmonary signal. Subject animals included the following species: Giant panda (Ailuropoda melanoleuca), African lions (Panthera leo), Sumatran tiger (Panthera tigris sumatrae), koala (Phascolarctos cinereus), red kangaroo (Macropus rufus), alpaca (Vicugna pacos), little blue penguin (Eudyptula minor), Sumatran orangutan (Pongo abelii) and Hamadryas baboon (Papio hamadryas). The study was done without need for restriction, fixation, contact or disruption of the daily routine of the subjects. The pilot system extracts the signal from the abdominal-thoracic region, where cardiopulmonary activity is most likely to be visible using image sequences captured by a digital camera. The results show motion on the body surface of the subjects that is characteristic of cardiopulmonary activity and is likely to be useful to estimate physiological parameters (pulse rate and breathing rate) of animals without any physical contact. The results of the study suggest that a fully controlled study against conventional physiological monitoring equipment is ethically warranted, which may lead to a novel approach to non-contact physiological monitoring and remotely sensed health assessment of animals. The method shows promise for applications in veterinary practice, conservation and game management, animal welfare and zoological and behavioral studies.
Publisher: Springer Science and Business Media LLC
Date: 13-02-2018
Publisher: MDPI AG
Date: 14-10-2019
DOI: 10.3390/S19204452
Abstract: Elderly fall detection systems based on wireless body area sensor networks (WBSNs) have increased significantly in medical contexts. The power consumption of such systems is a critical issue influencing the overall practicality of the WBSN. Reducing the power consumption of these networks while maintaining acceptable performance poses a challenge. Several power reduction techniques can be employed to tackle this issue. A human vital signs monitoring system (HVSMS) has been proposed here to measure vital parameters of the elderly, including heart rate and fall detection based on heartbeat and accelerometer sensors, respectively. In addition, the location of elderly people can be determined based on Global Positioning System (GPS) and transmitted with their vital parameters to emergency medical centers (EMCs) via the Global System for Mobile Communications (GSM) network. In this paper, the power consumption of the proposed HVSMS was minimized by merging a data-event (DE) algorithm and an energy-harvesting-technique-based wireless power transfer (WPT). The DE algorithm improved HVSMS power consumption, utilizing the duty cycle of the sleep/wake mode. The WPT successfully charged the HVSMS battery. The results demonstrated that the proposed DE algorithm reduced the current consumption of the HVSMS to 9.35 mA compared to traditional operation at 85.85 mA. Thus, an 89% power saving was achieved based on the DE algorithm and the battery life was extended to 30 days instead of 3 days (traditional operation). In addition, the WPT was able to charge the HVSMS batteries once every 30 days for 10 h, thus eliminating existing restrictions involving the use of wire charging methods. The results indicate that the HVSMS current consumption outperformed existing solutions from previous studies.
Publisher: Middle Technical University
Date: 31-03-2023
DOI: 10.51173/JT.V5I1.868
Abstract: The tongue reflects the abnormal condition and behavior of the internal organs of the body, such as problems of the heart, liver, pancreas, stomach, intestines, blood diseases and others, which lead to changes in some of the features and characteristics of the tongue. The most important of these is tongue color, which can be adopted as a biometric that can be used in Computerized Tongue Diagnostic Systems (CTDS). Quantitative diagnosis of the tongue requires some devices, including image acquisition devices such as cameras, light sources, filters, color checkers, image analysis and processing devices through the application of some algorithms or image processing and color correction software, as well as a computer. This study proposes a real-time imaging system to analyze tongue color and diagnose diseases using a webcam under specific conditions. The proposed system was designed in a Matlab GUI environment. After testing the system on a data set of more than 100 images, the preliminary results showed that the proposed system gives a disease diagnosis with an accuracy rate of no less than 86.667%. The proposed system contributed to the diagnosis of several diseases in real time, with an accuracy of 95.45%, with ease of use, implementation and low cost. This gives impetus to further studies to apply computerized diagnosis in medical applications, to enhance the medical reality, monitor patient health, and make an accurate diagnosis.
Publisher: MDPI AG
Date: 26-09-2021
Abstract: Biomedical sensors help patients monitor their health conditions and receive assistance anywhere and at any time. However, the limited battery capacity of medical devices limits their functionality. One advantageous method to tackle this limited-capacity issue is to employ the wireless power transfer (WPT) technique. In this paper, a WPT technique using a magnetic resonance coupling (MRC-WPT)-based wireless heart rate (WHR) monitoring system—which continuously records the heart rate of patients—has been designed, and its efficiency is confirmed through real-time implementation. The MRC-WPT involves three main units: the transmitter, receiver, and observing units. In this research, a new design of spiral-spider coil was designed and implemented for transmitter and receiver units, respectively, to supply the measurement unit, which includes a heart rate sensor, microcontroller, and wireless protocol (nRF24L01) with the operating voltage. The experimental results found that an adequate voltage of 5 V was achieved by the power component to operate the measurement unit at a 20 cm air gap between the receiver and transmitter coils. Further, the measurement accuracy of the WHR was 99.65% comparative to the benchmark (BM) instrument. Moreover, the measurements of the WHR were validated based on statistical analyses. The results of this study are superior to those of leading works in terms of measurement accuracy, power transfer, and Transfer efficiency.
Publisher: IOP Publishing
Date: 26-09-2018
Publisher: MDPI AG
Date: 03-02-2017
DOI: 10.3390/S17020286
Publisher: Emerald
Date: 16-04-2018
DOI: 10.1108/IJIUS-10-2017-0012
Abstract: The purpose of this paper is to present a preliminary solution to address the problem of estimating human pose and trajectory by an aerial robot with a monocular camera in near real time. The distinguishing feature of the solution is a dynamic classifier selection architecture. Each video frame is corrected for perspective using projective transformation. Then, a silhouette is extracted as a Histogram of Oriented Gradients (HOG). The HOG is then classified using a dynamic classifier. A class is defined as a pose-viewpoint pair, and a total of 64 classes are defined to represent a forward walking and turning gait sequence. The dynamic classifier consists of a Support Vector Machine (SVM) classifier C64 that recognizes all 64 classes, and 64 SVM classifiers that recognize four classes each – these four classes are chosen based on the temporal relationship between them, dictated by the gait sequence. The solution provides three main advantages: first, classification is efficient due to dynamic selection (4-class vs 64-class classification). Second, classification errors are confined to neighbors of the true viewpoints. This means a wrongly estimated viewpoint is at most an adjacent viewpoint of the true viewpoint, enabling fast recovery from incorrect estimations. Third, the robust temporal relationship between poses is used to resolve the left-right ambiguities of human silhouettes. Experiments conducted on both fronto-parallel videos and aerial videos confirm that the solution can achieve accurate pose and trajectory estimation for these different kinds of videos. For ex le, the “walking on an 8-shaped path” data set (1,652 frames) can achieve the following estimation accuracies: 85 percent for viewpoints and 98.14 percent for poses.
Publisher: IEEE
Date: 21-09-2022
Publisher: MDPI AG
Date: 21-10-2019
DOI: 10.3390/RS11202441
Abstract: In the aftermath of a disaster, such as earthquake, flood, or avalanche, ground search for survivors is usually h ered by unstable surfaces and difficult terrain. Drones now play an important role in these situations, allowing rescuers to locate survivors and allocate resources to saving those who can be helped. The aim of this study was to explore the utility of a drone equipped for human life detection with a novel computer vision system. The proposed system uses image sequences captured by a drone camera to remotely detect the cardiopulmonary motion caused by periodic chest movement of survivors. The results of eight human subjects and one mannequin in different poses shows that motion detection on the body surface of the survivors is likely to be useful to detect life signs without any physical contact. The results presented in this study may lead to a new approach to life detection and remote life sensing assessment of survivors.
Publisher: MDPI AG
Date: 10-12-2019
DOI: 10.3390/S19245445
Abstract: Monitoring the cardiopulmonary signal of animals is a challenge for veterinarians in conditions when contact with a conscious animal is inconvenient, difficult, damaging, distressing or dangerous to personnel or the animal subject. In this pilot study, we demonstrate a computer vision-based system and use ex les of exotic, untamed species to demonstrate this means to extract the cardiopulmonary signal. Subject animals included the following species: Giant panda (Ailuropoda melanoleuca), African lions (Panthera leo), Sumatran tiger (Panthera tigris sumatrae), koala (Phascolarctos cinereus), red kangaroo (Macropus rufus), alpaca (Vicugna pacos), little blue penguin (Eudyptula minor), Sumatran orangutan (Pongo abelii) and Hamadryas baboon (Papio hamadryas). The study was done without need for restriction, fixation, contact or disruption of the daily routine of the subjects. The pilot system extracts the signal from the abdominal-thoracic region, where cardiopulmonary activity is most likely to be visible using image sequences captured by a digital camera. The results show motion on the body surface of the subjects that is characteristic of cardiopulmonary activity and is likely to be useful to estimate physiological parameters (pulse rate and breathing rate) of animals without any physical contact. The results of the study suggest that a fully controlled study against conventional physiological monitoring equipment is ethically warranted, which may lead to a novel approach to non-contact physiological monitoring and remotely sensed health assessment of animals. The method shows promise for applications in veterinary practice, conservation and game management, animal welfare and zoological and behavioral studies.
Publisher: Elsevier BV
Date: 08-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: IEEE
Date: 21-09-2022
Publisher: MDPI AG
Date: 12-08-2023
DOI: 10.3390/TECHNOLOGIES11040111
Abstract: Image denoising is a critical task in computer vision aimed at removing unwanted noise from images, which can degrade image quality and affect visual details. This study proposes a novel approach that combines deep hybrid learning with the Self-Improved Orca Predation Algorithm (SI-OPA) for image denoising. Leveraging Bidirectional Long Short-Term Memory (Bi-LSTM) and optimized Convolutional Neural Networks (CNN), the hybrid model aims to enhance denoising performance. The CNN’s weights are optimized using SI-OPA, resulting in improved denoising accuracy. Extensive comparisons against state-of-the-art denoising methods, including traditional algorithms and deep learning-based techniques, are conducted, focusing on denoising effectiveness, computational efficiency, and preservation of image details. The proposed approach demonstrates superior performance in all aspects, highlighting its potential as a promising solution for image-denoising tasks. Implemented in Python, the hybrid model showcases the benefits of combining Bi-LSTM, optimized CNN, and SI-OPA for advanced image-denoising applications.
Publisher: Informa UK Limited
Date: 03-05-2023
Publisher: IOP Publishing
Date: 05-2019
DOI: 10.1088/1757-899X/518/4/042025
Abstract: In many conditions, the conventional liquid data monitoring based on an ultrasonic sensor provides the unreliable readings due to the dynamically changed water level. In addition, in some conditions, it needs not only measuring water level but also needs to measure the volume and control water surplus or deficiency. To solve these issues, this paper proposes an accurate non-contact water measurement system based on a microcontroller and an ultrasonic sensor to measure the level and volume of liquids in small tanks without any contact. The proposed system also provides automatically controlling the water level with an alarm system to provide early warning of water surplus or deficiency. Microcontroller PIC16F877A is used to drive the sensor circuit and measure the time change of the reflected echoes from the water surface received by the ultrasonic (PING) sensor that correspond to the changes in the water level. The experimental results illustrate the effectiveness of the proposed system to measure the level and volume of water over 30 cm range with small error rates (SSE = 0.033 cm, RMSE = 0.034 cm and MAE = 0.029 cm for level measurement and SSE = 0.025 liter, RMSE = 0.026 liter and MAE = 0.021 liter for volume measurement) and excellent correlation coefficients (SCC = 0.9997 and KCC = 0.9951), thus provide accurate results for continuous measurement of the water level and volume in industrial applications.
Publisher: MDPI AG
Date: 11-08-2023
DOI: 10.3390/COMPUTERS12080160
Abstract: In medical information systems, image data can be considered crucial information. As imaging technology and methods for analyzing medical images advance, there will be a greater wealth of data available for study. Hence, protecting those images is essential. Image encryption methods are crucial in multimedia applications for ensuring the security and authenticity of digital images. Recently, the encryption of medical images has garnered significant attention from academics due to concerns about the safety of medical communication. Advanced approaches, such as e-health, smart health, and telemedicine applications, are employed in the medical profession. This has highlighted the issue that medical images are often produced and shared online, necessitating protection against unauthorized use.
Publisher: MDPI AG
Date: 14-10-2019
DOI: 10.3390/S19204452
Abstract: Elderly fall detection systems based on wireless body area sensor networks (WBSNs) have increased significantly in medical contexts. The power consumption of such systems is a critical issue influencing the overall practicality of the WBSN. Reducing the power consumption of these networks while maintaining acceptable performance poses a challenge. Several power reduction techniques can be employed to tackle this issue. A human vital signs monitoring system (HVSMS) has been proposed here to measure vital parameters of the elderly, including heart rate and fall detection based on heartbeat and accelerometer sensors, respectively. In addition, the location of elderly people can be determined based on Global Positioning System (GPS) and transmitted with their vital parameters to emergency medical centers (EMCs) via the Global System for Mobile Communications (GSM) network. In this paper, the power consumption of the proposed HVSMS was minimized by merging a data-event (DE) algorithm and an energy-harvesting-technique-based wireless power transfer (WPT). The DE algorithm improved HVSMS power consumption, utilizing the duty cycle of the sleep/wake mode. The WPT successfully charged the HVSMS battery. The results demonstrated that the proposed DE algorithm reduced the current consumption of the HVSMS to 9.35 mA compared to traditional operation at 85.85 mA. Thus, an 89% power saving was achieved based on the DE algorithm and the battery life was extended to 30 days instead of 3 days (traditional operation). In addition, the WPT was able to charge the HVSMS batteries once every 30 days for 10 h, thus eliminating existing restrictions involving the use of wire charging methods. The results indicate that the HVSMS current consumption outperformed existing solutions from previous studies.
Publisher: MDPI AG
Date: 21-03-2023
DOI: 10.20944/PREPRINTS202303.0379.V1
Abstract: Jaundice is a common condition for newborns, and its complications can be severe and cause permanent damage to the patient& rsquo s brain if no action is taken at its early stages. Current methods for jaundice detection are invasive, which include collecting blood s les from the patient, which can be painful and stressful and may cause some complications. Alternatively, a non-invasive approach can be used to diagnose jaundice through image-processing and artificial intelligence (AI) techniques, requiring a database of infant images to achieve a high-accuracy diagnosis. This data article provides a collection of newborn images, called NJN, with various birthweight and skin tones, with ages ranging from 2 to 8 days, and an excel sheet file in CSV format for the values of RGB and YCrCb channels and the status for each raw which is freely accessible at (iew/neonataljaundice). It also provides Python code for data testing using different AI techniques. Thus, this article offers a unique resource for all AI researchers to train their AI system and develop algorithms to help neonatal intensive care unit (NICU) healthcare specialists monitor neonates and provide fast, real-time, non-invasive, and accurate jaundice diagnosis.
Publisher: MDPI AG
Date: 20-10-2021
DOI: 10.3390/APP11219813
Abstract: Cardiac arrest (CA) in infants is an issue worldwide, which causes significant morbidity and mortality rates. Cardiopulmonary resuscitation (CPR) is a technique performed in case of CA to save victims’ lives. However, CPR is often not performed effectively, even when delivered by qualified rescuers. Therefore, international guidelines have proposed applying a CPR feedback device to achieve high-quality application of CPR to enhance survival rates. Currently, no feedback device is available to guide learners through infant CPR performance in contrast to a number of adult CPR feedback devices. This study presents a real-time feedback system to improve infant CPR performance by medical staff and laypersons using a commercial CPR infant manikin. The proposed system uses an IR sensor to compare CPR performance obtained with no feedback and with a real-time feedback system. Performance was validated by analysis of the CPR parameters actually delivered against the recommended target parameters. Results show that the real-time feedback system significantly improves the quality of chest compression parameters. The two-thumb compression technique is the achievable and appropriate mechanism applied to infant subjects for delivering high-quality CPR. Under the social distancing constraints imposed by the SARS-CoV-2 pandemic, the results from the training device were sent to a CPR training center and provided each participant with CPR proficiency.
Publisher: MDPI AG
Date: 09-02-2020
DOI: 10.3390/RS12030577
Abstract: In search and rescue operations, it is crucial to rapidly identify those people who are alive from those who are not. If this information is known, emergency teams can prioritize their operations to save more lives. However, in some natural disasters the people may be lying on the ground covered with dust, debris, or ashes making them difficult to detect by video analysis that is tuned to human shapes. We present a novel method to estimate the locations of people from aerial video using image and signal processing designed to detect breathing movements. We have shown that this method can successfully detect clearly visible people and people who are fully occluded by debris. First, the aerial videos were stabilized using the key points of adjacent image frames. Next, the stabilized video was decomposed into tile videos and the temporal frequency bands of interest were motion magnified while the other frequencies were suppressed. Image differencing and temporal filtering were performed on each tile video to detect potential breathing signals. Finally, the detected frequencies were remapped to the image frame creating a life signs map that indicates possible human locations. The proposed method was validated with both aerial and ground recorded videos in a controlled environment. Based on the dataset, the results showed good reliability for aerial videos and no errors for ground recorded videos where the average precision measures for aerial videos and ground recorded videos were 0.913 and 1 respectively.
Location: Iraq
No related grants have been discovered for Ali Abdulelah Al-Naji.