Pub Date : 2021-12-08DOI: 10.1109/BioSMART54244.2021.9677828
Meldin Bektic, Adam Tischler, Nathaniel Fahey, Kwangtaek Kim, Lisa Onesko
In this study we examine the effectiveness of using AR haptic simulation as a tool for nursing students to learn physical attributes related to diseases, as well as testing with the simulation rather than pen & paper. We utilize edema, a medical condition that causes swelling in the body's tissues, as an example the students can learn and be tested on. The simulation takes advantage of the Magic Leap and Geomagic Touch as the AR headset and haptic device of choice. Students use these technologies to see different examples of legs that have varying degrees of edema in a 3D space and use the Geomagic Touch to feel the virtual leg. When pressing upon the leg, the object has deformation capabilities which allow the user to see and feel the impressions made upon the skin. We tested this under four different conditions, a desktop 2D version with haptics disabled and enabled, and an AR 3D version with haptics disabled and enabled. We tested these conditions on 8 different subjects, with four being non-nursing professionals, and the other four being from Kent State University (KSU) College of Nursing (CoN). The results showed that qualitatively the subjects felt that the desktop haptics version was the best, however quantitatively the results showed that the subjects scored the highest during the desktop no haptics version.
{"title":"Efficacy of AR Haptic Simulation for Nursing Student Education","authors":"Meldin Bektic, Adam Tischler, Nathaniel Fahey, Kwangtaek Kim, Lisa Onesko","doi":"10.1109/BioSMART54244.2021.9677828","DOIUrl":"https://doi.org/10.1109/BioSMART54244.2021.9677828","url":null,"abstract":"In this study we examine the effectiveness of using AR haptic simulation as a tool for nursing students to learn physical attributes related to diseases, as well as testing with the simulation rather than pen & paper. We utilize edema, a medical condition that causes swelling in the body's tissues, as an example the students can learn and be tested on. The simulation takes advantage of the Magic Leap and Geomagic Touch as the AR headset and haptic device of choice. Students use these technologies to see different examples of legs that have varying degrees of edema in a 3D space and use the Geomagic Touch to feel the virtual leg. When pressing upon the leg, the object has deformation capabilities which allow the user to see and feel the impressions made upon the skin. We tested this under four different conditions, a desktop 2D version with haptics disabled and enabled, and an AR 3D version with haptics disabled and enabled. We tested these conditions on 8 different subjects, with four being non-nursing professionals, and the other four being from Kent State University (KSU) College of Nursing (CoN). The results showed that qualitatively the subjects felt that the desktop haptics version was the best, however quantitatively the results showed that the subjects scored the highest during the desktop no haptics version.","PeriodicalId":286026,"journal":{"name":"2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114181334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-08DOI: 10.1109/BioSMART54244.2021.9677719
Ahmad M. El-Hajj, Khaled Chahine
The management of glucose levels in type I diabetes patients is a tedious daily routine that comprises painful glucose measurements and insulin shots. The process is also intractable as insulin is administered in fixed doses, leading sometimes to hypo-glycemia. To address these limitations, this paper conceptualizes an in-vivo glucose control mechanism that relies on subcutaneous glucose measurement to trigger the release of precise doses of insulin into the bloodstream. The signalling process is achieved through a molecular communication system where information is encoded in molecules. The implementation of the proposed concept would eventually revolutionize diabetes management to a painless and more accurate approach.
{"title":"In-Vivo Automated Diabetes Control System Utilizing Molecular Communication","authors":"Ahmad M. El-Hajj, Khaled Chahine","doi":"10.1109/BioSMART54244.2021.9677719","DOIUrl":"https://doi.org/10.1109/BioSMART54244.2021.9677719","url":null,"abstract":"The management of glucose levels in type I diabetes patients is a tedious daily routine that comprises painful glucose measurements and insulin shots. The process is also intractable as insulin is administered in fixed doses, leading sometimes to hypo-glycemia. To address these limitations, this paper conceptualizes an in-vivo glucose control mechanism that relies on subcutaneous glucose measurement to trigger the release of precise doses of insulin into the bloodstream. The signalling process is achieved through a molecular communication system where information is encoded in molecules. The implementation of the proposed concept would eventually revolutionize diabetes management to a painless and more accurate approach.","PeriodicalId":286026,"journal":{"name":"2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126127743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-08DOI: 10.1109/BioSMART54244.2021.9677707
Pimwipa Charuthamrong, P. Israsena, S. Hemrungrojn, S. Pan-Ngum
An oddball paradigm is an experimental design that uses a sequence of one repeating stimulus called the standard stimulus. This sequence is infrequently interrupted by a different stimulus called the deviant or target stimulus. Potentially the oddball paradigm can be employed in an EEG-based speech discrimination assessment protocol. Speech discrimination indicates how well a person can differentiate between different words. Analyzing EEG measurements such as the Event-Related Potentials (ERPs) may help to achieve the goal of automated assessment process. In this work we compare two listening modes in an oddball paradigm in order to find a suitable mode for assessing speech discrimination automatically. The two listening modes include passive and active listening. Passive listening is when the listener does not pay attention to what they hear. Active listening is when the listener actively pays attention to the sound. We tested these two listening modes using two Thai words with consonant contrast. We compared the ERP waveform, classification accuracy, and attention during passive and active listening. We found that passive listening produced clearer ERP waveform. However, active listening achieved higher accuracy and engaged less attention. Therefore, we recommend using active listening for an auditory oddball paradigm when assessing speech discrimination.
{"title":"Active and Passive Oddball Paradigm for Automatic Speech Discrimination Assessment","authors":"Pimwipa Charuthamrong, P. Israsena, S. Hemrungrojn, S. Pan-Ngum","doi":"10.1109/BioSMART54244.2021.9677707","DOIUrl":"https://doi.org/10.1109/BioSMART54244.2021.9677707","url":null,"abstract":"An oddball paradigm is an experimental design that uses a sequence of one repeating stimulus called the standard stimulus. This sequence is infrequently interrupted by a different stimulus called the deviant or target stimulus. Potentially the oddball paradigm can be employed in an EEG-based speech discrimination assessment protocol. Speech discrimination indicates how well a person can differentiate between different words. Analyzing EEG measurements such as the Event-Related Potentials (ERPs) may help to achieve the goal of automated assessment process. In this work we compare two listening modes in an oddball paradigm in order to find a suitable mode for assessing speech discrimination automatically. The two listening modes include passive and active listening. Passive listening is when the listener does not pay attention to what they hear. Active listening is when the listener actively pays attention to the sound. We tested these two listening modes using two Thai words with consonant contrast. We compared the ERP waveform, classification accuracy, and attention during passive and active listening. We found that passive listening produced clearer ERP waveform. However, active listening achieved higher accuracy and engaged less attention. Therefore, we recommend using active listening for an auditory oddball paradigm when assessing speech discrimination.","PeriodicalId":286026,"journal":{"name":"2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128866604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-08DOI: 10.1109/BioSMART54244.2021.9677694
Jeroen van Houtte, Jan Sijbers, G. Zheng
Optical position tracking is an essential tool in computer-assisted interventions for intra-operative guidance. It allows to register a pre-operative model or surgery plan to the patient, providing additional support to the surgeon. In this paper, we propose a two-step procedure to register pre-operative digital surface models to the surgical scene based on optical marker data. First a paired-point matching is applied, followed by an iterative closest point registration step. Mapping the surface model to the camera system allows to compute properties like the joint space width and motion asymmetry. Our method can be generalised to any joint and has been made available through an open-source graphical user interface, enabling future research on surgical navigation systems.
{"title":"Graphical User Interface for Joint Space Width Assessment by Optical Marker Tracking","authors":"Jeroen van Houtte, Jan Sijbers, G. Zheng","doi":"10.1109/BioSMART54244.2021.9677694","DOIUrl":"https://doi.org/10.1109/BioSMART54244.2021.9677694","url":null,"abstract":"Optical position tracking is an essential tool in computer-assisted interventions for intra-operative guidance. It allows to register a pre-operative model or surgery plan to the patient, providing additional support to the surgeon. In this paper, we propose a two-step procedure to register pre-operative digital surface models to the surgical scene based on optical marker data. First a paired-point matching is applied, followed by an iterative closest point registration step. Mapping the surface model to the camera system allows to compute properties like the joint space width and motion asymmetry. Our method can be generalised to any joint and has been made available through an open-source graphical user interface, enabling future research on surgical navigation systems.","PeriodicalId":286026,"journal":{"name":"2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127924523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-08DOI: 10.1109/BioSMART54244.2021.9677770
A. Rahman, Fahim Faisal, M. M. Nishat, Muntequa Imtiaz Siraji, Lamim Ibtisam Khalid, Md. Rezaul Hoque Khan, Md. Taslim Reza
Epileptic seizure refers to a brief occurrence of signs in the brain caused by abnormally high or synchronized neuronal activity. With the utilization of EEG signal, the epileptic seizure can be identified. However, incorporating machine learning classifiers with this EEG data can significantly contribute in detecting epileptic seizure in an automated manner. In this paper, nine machine learning algorithms have been studied and models have been constructed by utilizing UCI Epileptic Seizure dataset. The performances of the ML models are noted and detailed comparative analysis has been exhibited for both hyperparameter tuning and without hyperparameter tuning. Random search cross validation has been used for tuning the hyperparameters. Satisfactory results have been witnessed in terms of different performance metrics like accuracy, precision, recall, specificity, FI-Score, and ROC. After simulation, Support Vector Machine (SVM) performed the best in terms of accuracy with over 97.86%. However, Random Forest (RF) and Multi-Layer Perceptron (MLP) also depicted promising accuracies of 97.50% and 97.26% respectively. Therefore, with proper implementation of the ML based diagnosis system, the patients having epileptic seizures can be identified and treated at an early stage.
{"title":"Detection of Epileptic Seizure from EEG Signal Data by Employing Machine Learning Algorithms with Hyperparameter Optimization","authors":"A. Rahman, Fahim Faisal, M. M. Nishat, Muntequa Imtiaz Siraji, Lamim Ibtisam Khalid, Md. Rezaul Hoque Khan, Md. Taslim Reza","doi":"10.1109/BioSMART54244.2021.9677770","DOIUrl":"https://doi.org/10.1109/BioSMART54244.2021.9677770","url":null,"abstract":"Epileptic seizure refers to a brief occurrence of signs in the brain caused by abnormally high or synchronized neuronal activity. With the utilization of EEG signal, the epileptic seizure can be identified. However, incorporating machine learning classifiers with this EEG data can significantly contribute in detecting epileptic seizure in an automated manner. In this paper, nine machine learning algorithms have been studied and models have been constructed by utilizing UCI Epileptic Seizure dataset. The performances of the ML models are noted and detailed comparative analysis has been exhibited for both hyperparameter tuning and without hyperparameter tuning. Random search cross validation has been used for tuning the hyperparameters. Satisfactory results have been witnessed in terms of different performance metrics like accuracy, precision, recall, specificity, FI-Score, and ROC. After simulation, Support Vector Machine (SVM) performed the best in terms of accuracy with over 97.86%. However, Random Forest (RF) and Multi-Layer Perceptron (MLP) also depicted promising accuracies of 97.50% and 97.26% respectively. Therefore, with proper implementation of the ML based diagnosis system, the patients having epileptic seizures can be identified and treated at an early stage.","PeriodicalId":286026,"journal":{"name":"2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132504999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-08DOI: 10.1109/BioSMART54244.2021.9677656
Hind Ali. Khudair, Hassanain Ali Lafta
The most prevalent form of sleep-related respiratory disorders, obstructive sleep apnea (OSA), is very common. It's marked by frequent cessations of breathing during sleep; these are caused by a collapsing of the top respiratory airway. Because of the complicated Polysomnography (PSG) test technique at sleep laboratories, OSA is largely undetected. The database for this research was constructed from 83 individuals (20 of them are control and 63 of them are OSA patients) from an all-night sleep study polysomnography device. The 63 OSA patients are divided into three groups according to the degree of severity to mild, moderate, and severe. Tukey multiple comparisons test was used to do multiple comparisons between different patients' groups and these comparisons will be in three directions. The first direction of comparison is the comparison between control (healthy) and severe OSA patients, the second direction of comparison is the comparison between mild and severe OSA patients, and the third direction of comparison is the comparison between moderate and severe OSA patients. Astatistical correlation analysis was performed for a respiratory event with the other events. The obtained findings [indicate the paramount importance of respiratory events analysis in classifying the severity of OSA patient's in to various degrees.
{"title":"Evaluation of Obstructive Sleep Apnea based on a Statistical Analysis of the Respiratory Events in Iraqi Individuals","authors":"Hind Ali. Khudair, Hassanain Ali Lafta","doi":"10.1109/BioSMART54244.2021.9677656","DOIUrl":"https://doi.org/10.1109/BioSMART54244.2021.9677656","url":null,"abstract":"The most prevalent form of sleep-related respiratory disorders, obstructive sleep apnea (OSA), is very common. It's marked by frequent cessations of breathing during sleep; these are caused by a collapsing of the top respiratory airway. Because of the complicated Polysomnography (PSG) test technique at sleep laboratories, OSA is largely undetected. The database for this research was constructed from 83 individuals (20 of them are control and 63 of them are OSA patients) from an all-night sleep study polysomnography device. The 63 OSA patients are divided into three groups according to the degree of severity to mild, moderate, and severe. Tukey multiple comparisons test was used to do multiple comparisons between different patients' groups and these comparisons will be in three directions. The first direction of comparison is the comparison between control (healthy) and severe OSA patients, the second direction of comparison is the comparison between mild and severe OSA patients, and the third direction of comparison is the comparison between moderate and severe OSA patients. Astatistical correlation analysis was performed for a respiratory event with the other events. The obtained findings [indicate the paramount importance of respiratory events analysis in classifying the severity of OSA patient's in to various degrees.","PeriodicalId":286026,"journal":{"name":"2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133390432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-08DOI: 10.1109/BioSMART54244.2021.9677799
Said Si Kaddoun, Yassir Aberni, L. Boubchir, Mohammed Raddadi, B. Daachi
Palm vein pattern recognition is one of the among biometric recognition techniques that uses blood vessel traits for person's identity identification and/or verification. This paper presents a preliminary study on palm vein recognition based on the application of Convolutional Neural Network (CNN) using a deep learning architecture called ZFNet. ZFNet was adapted and implemented in the proposed method by proposing an improved architecture based on optimal parameters. The proposed method was assessed on the near-infrared palmprint images from MS-PolyU database. The experimental results carried out have shown the high recognition performance of the proposed method compared with other CNN architectures considered in the proposed study such as LeNet, AlexNet and ResNet.
{"title":"Convolutional Neural Algorithm for Palm Vein Recognition using ZFNet Architecture","authors":"Said Si Kaddoun, Yassir Aberni, L. Boubchir, Mohammed Raddadi, B. Daachi","doi":"10.1109/BioSMART54244.2021.9677799","DOIUrl":"https://doi.org/10.1109/BioSMART54244.2021.9677799","url":null,"abstract":"Palm vein pattern recognition is one of the among biometric recognition techniques that uses blood vessel traits for person's identity identification and/or verification. This paper presents a preliminary study on palm vein recognition based on the application of Convolutional Neural Network (CNN) using a deep learning architecture called ZFNet. ZFNet was adapted and implemented in the proposed method by proposing an improved architecture based on optimal parameters. The proposed method was assessed on the near-infrared palmprint images from MS-PolyU database. The experimental results carried out have shown the high recognition performance of the proposed method compared with other CNN architectures considered in the proposed study such as LeNet, AlexNet and ResNet.","PeriodicalId":286026,"journal":{"name":"2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129521753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-08DOI: 10.1109/BioSMART54244.2021.9677886
Zhor Benhafid, S. Selouani, M. S. Yakoub, A. Amrouche
Current speaker recognition systems are based ei-ther on time-delay neural network (TDNN) x-vectors or ResNet embedding speaker representations. Both architectures have their advantages and this paper aims to benefit from their prominent and complementary features. In contrast to what has been already proposed in the literature, we investigate the impact of using only one residual neural network block named ResBlock on x-vectors instead of the several blocks used in conventional sys-tems. Four ResBlock variants are integrated at the TDNN frame-level layer of x-vectors. The obtained hybrid One-ResBlock-TDNN architectures are evaluated using Speaker In The Wild (SITW) and Voices Obscured in Complex Environmental Settings (VOiCES) evaluation sets. The experimental assessment reveals that compared to conventional x-vectors' encoder, a noticeable accuracy improvement of all proposed hybrid One-ResBlock-TDNN variants has been achieved on both SITW and VOiCES standards' datasets.
{"title":"Hybrid Residual Block Time-Delay Neural Network Embeddings for Speaker Recognition","authors":"Zhor Benhafid, S. Selouani, M. S. Yakoub, A. Amrouche","doi":"10.1109/BioSMART54244.2021.9677886","DOIUrl":"https://doi.org/10.1109/BioSMART54244.2021.9677886","url":null,"abstract":"Current speaker recognition systems are based ei-ther on time-delay neural network (TDNN) x-vectors or ResNet embedding speaker representations. Both architectures have their advantages and this paper aims to benefit from their prominent and complementary features. In contrast to what has been already proposed in the literature, we investigate the impact of using only one residual neural network block named ResBlock on x-vectors instead of the several blocks used in conventional sys-tems. Four ResBlock variants are integrated at the TDNN frame-level layer of x-vectors. The obtained hybrid One-ResBlock-TDNN architectures are evaluated using Speaker In The Wild (SITW) and Voices Obscured in Complex Environmental Settings (VOiCES) evaluation sets. The experimental assessment reveals that compared to conventional x-vectors' encoder, a noticeable accuracy improvement of all proposed hybrid One-ResBlock-TDNN variants has been achieved on both SITW and VOiCES standards' datasets.","PeriodicalId":286026,"journal":{"name":"2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130885662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-08DOI: 10.1109/BioSMART54244.2021.9677698
Alaa Daher, Sally Yassin, Hadi Alsamra, Hassan Ali
Parkinson's disease, as a definition, is a neurological condition that affects the brain and causes tremors, stiffness, and difficulties walking, balancing, and coordinating. Symptoms of Parkinson's disease normally appear gradually and worsen with time. People with Parkinson's disease may have difficulties walking and speaking as the condition develops. Numerous recent studies have shown a direct association between Parkinson's disease and the incident of having epileptic seizures, which is defined to be a burst of the uncontrollable electrical activity of the brain cells, that is associated with an increased risk of sudden unexplained deaths. This project aims to obtain a real-time seizure prediction system for Parkinson's disease patients based on the electroencephalogram (EEG) signals, enabling the detection of a seizure before it happens. This will hopefully save them from risky situations or sudden death, as they will be alerted and have the time enabling them to be prepared and take the needed precautions and steps to prevent the seizure from happening. For this project, we've used the Neural Network and the ANFIS (“udaptive neuro-fuzzy inference system ‘’) to process and analyze the electroencephalogram (EEG) data signals of the Parkinson patients to detect seizures starting point.
{"title":"Adaptive Neuro-Fuzzy Inference System As New Real-Time Approach For Parkinson Seizures Prediction","authors":"Alaa Daher, Sally Yassin, Hadi Alsamra, Hassan Ali","doi":"10.1109/BioSMART54244.2021.9677698","DOIUrl":"https://doi.org/10.1109/BioSMART54244.2021.9677698","url":null,"abstract":"Parkinson's disease, as a definition, is a neurological condition that affects the brain and causes tremors, stiffness, and difficulties walking, balancing, and coordinating. Symptoms of Parkinson's disease normally appear gradually and worsen with time. People with Parkinson's disease may have difficulties walking and speaking as the condition develops. Numerous recent studies have shown a direct association between Parkinson's disease and the incident of having epileptic seizures, which is defined to be a burst of the uncontrollable electrical activity of the brain cells, that is associated with an increased risk of sudden unexplained deaths. This project aims to obtain a real-time seizure prediction system for Parkinson's disease patients based on the electroencephalogram (EEG) signals, enabling the detection of a seizure before it happens. This will hopefully save them from risky situations or sudden death, as they will be alerted and have the time enabling them to be prepared and take the needed precautions and steps to prevent the seizure from happening. For this project, we've used the Neural Network and the ANFIS (“udaptive neuro-fuzzy inference system ‘’) to process and analyze the electroencephalogram (EEG) data signals of the Parkinson patients to detect seizures starting point.","PeriodicalId":286026,"journal":{"name":"2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134409025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Internet of Things is redesigning a wide range of remote monitoring applications in the variant domain, including the health care industry. Remote Patient Monitoring (RPM) is not a new concept in modern technology, but the Internet of Things (IoT) makes it a better equipped and sophisticated control system. This research paper approaches a noninvasive wearable device that will monitor the vital signs of a patient in real-time by using the Internet of Things (IoT). The proposed device can monitor the body temperature, blood pressure, heart rate, oxygen saturation, glucose level in the blood, ECG, patient fall detection, location parameters. In addition, it has a breath analyzer unit that measures the total volatile organic compounds (TVOC), carbon dioxide, alcohol, hydrogen sulfide, and ammonia level in breath. The system is designed with an 8-bit microcontroller along with corresponding sensors. The sensor's data are fed into a web database using a wifi communication protocol. Furthermore, the system has a web dashboard and Role-Based Access (RBA) smartphone app to monitor multiple patients remotely. The proposed approach demonstrates advanced remote patient monitoring and diagnosis system for chronic disease patients, especially in a pandemic.
{"title":"An Internet of Things Application on Continuous Remote Patient Monitoring and Diagnosis","authors":"Md. Masnun Hossain Mia, Nagib Mahfuz, Md. Redowan Habib, Rifat Hossain","doi":"10.1109/BioSMART54244.2021.9677715","DOIUrl":"https://doi.org/10.1109/BioSMART54244.2021.9677715","url":null,"abstract":"The Internet of Things is redesigning a wide range of remote monitoring applications in the variant domain, including the health care industry. Remote Patient Monitoring (RPM) is not a new concept in modern technology, but the Internet of Things (IoT) makes it a better equipped and sophisticated control system. This research paper approaches a noninvasive wearable device that will monitor the vital signs of a patient in real-time by using the Internet of Things (IoT). The proposed device can monitor the body temperature, blood pressure, heart rate, oxygen saturation, glucose level in the blood, ECG, patient fall detection, location parameters. In addition, it has a breath analyzer unit that measures the total volatile organic compounds (TVOC), carbon dioxide, alcohol, hydrogen sulfide, and ammonia level in breath. The system is designed with an 8-bit microcontroller along with corresponding sensors. The sensor's data are fed into a web database using a wifi communication protocol. Furthermore, the system has a web dashboard and Role-Based Access (RBA) smartphone app to monitor multiple patients remotely. The proposed approach demonstrates advanced remote patient monitoring and diagnosis system for chronic disease patients, especially in a pandemic.","PeriodicalId":286026,"journal":{"name":"2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114607783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：