Pub Date : 2021-11-09DOI: 10.1109/nbec53282.2021.9618757
Ten Yi Ting, F. Yakub, Mohd Azizi Abdul Rahman, Ahmad Haziq Shamsul Bahri, Mohamad Aiman Syamir, Muhammad Azri Azizan, H. Kaidi, N. Noor, N. Bani, Siti Zura A.Jalil, H. Abdullah, N. Hussien
According to the World Health Organization, there are approximately 17.9 million people in the world who will die under the cause of Cardiovascular diseases (CVDs) in 2019. Heart and Brain are both related to Cardiovascular diseases. Even if the patients do not pass away due to the disease, the post-effect of this illness burdens the patients and their families. Also, the outbreak of COVID-19 makes the patients take a risk of undergoing rehabilitation in the hospital. Thus, a smart healthcare solution which is a Smart Healthcare Tracker through the Internet of Things is designed. The system consists of an EMG sensor, accelerometer, gyroscope, and heart rate/pulse oximeter connected to ESP 32 with an interface of NodeMCU to study the patients’ health condition for arms and legs strength by sending the data to the caregivers or physicians. The project aimed to obtain a consistent and accurate reading for each of the features for arms and legs strength analysis and sleeping disturbance analysis. The BLYNK app is also applied to the project design as a platform to display the analysis result to the caregivers/physicians on the gadgets at any time and anywhere. The prototype has been constructed and the data collection is built successfully. The prototype is trusted to obtain accurate and consistent results and can provide a sustainable way for the rehabilitation to indicate the health condition and the recovery stage of the patients.
{"title":"Development of Smart Healthcare Tracker through Internet of Things","authors":"Ten Yi Ting, F. Yakub, Mohd Azizi Abdul Rahman, Ahmad Haziq Shamsul Bahri, Mohamad Aiman Syamir, Muhammad Azri Azizan, H. Kaidi, N. Noor, N. Bani, Siti Zura A.Jalil, H. Abdullah, N. Hussien","doi":"10.1109/nbec53282.2021.9618757","DOIUrl":"https://doi.org/10.1109/nbec53282.2021.9618757","url":null,"abstract":"According to the World Health Organization, there are approximately 17.9 million people in the world who will die under the cause of Cardiovascular diseases (CVDs) in 2019. Heart and Brain are both related to Cardiovascular diseases. Even if the patients do not pass away due to the disease, the post-effect of this illness burdens the patients and their families. Also, the outbreak of COVID-19 makes the patients take a risk of undergoing rehabilitation in the hospital. Thus, a smart healthcare solution which is a Smart Healthcare Tracker through the Internet of Things is designed. The system consists of an EMG sensor, accelerometer, gyroscope, and heart rate/pulse oximeter connected to ESP 32 with an interface of NodeMCU to study the patients’ health condition for arms and legs strength by sending the data to the caregivers or physicians. The project aimed to obtain a consistent and accurate reading for each of the features for arms and legs strength analysis and sleeping disturbance analysis. The BLYNK app is also applied to the project design as a platform to display the analysis result to the caregivers/physicians on the gadgets at any time and anywhere. The prototype has been constructed and the data collection is built successfully. The prototype is trusted to obtain accurate and consistent results and can provide a sustainable way for the rehabilitation to indicate the health condition and the recovery stage of the patients.","PeriodicalId":297399,"journal":{"name":"2021 IEEE National Biomedical Engineering Conference (NBEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129269999","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 : 2021-11-09DOI: 10.1109/nbec53282.2021.9618750
Saleh I. Alzahrani
Brain-computer interfaces (BCIs) provide a non-muscular means of communication and control for severely paralyzed patients. Many BCIs depend on the P300 which is an exogenous event-related potential (ERP) component produced about 300 ms in response to the presentation of an infrequent, but recognized, stimulus. Although there are different EEG neuroheadsets in the market used to record the P300, not all of them are suitable for daily use due to the system cost and set-up time. The present study investigated the reliability of an affordable, low-cost, and wireless EEG device, namely Emotiv EPOC+, to record the P300 signals. Ten healthy volunteers tested a P300 speller system to spell 10 random characters. EEG data were recorded while the subjects attended to the standard P300 paradigm introduced by Farwell and Donchin in 1988. We examined the effect of using different matrix size (6x6 and 3x3), flash duration (100 and 175 ms), and colored matrix (white/gray and green/blue) on the performance of the P300 speller. The results show that the P300 amplitude is positively correlated with larger matrix size and longer flash duration. Moreover, the results show that using color (green/blue) stimuli enhanced larger P300 amplitude. Using linear discriminant analysis (LDA) classifier, the highest classification accuracy achieved was $75.9 pm 7.22$% when using 6x6 matrix, 175 ms flash duration, and green/blue stimuli condition. We conclude that such an affordable and wireless neuroheadset system can provide severely disabled people an alternative communication and control technology to be used effectively in their real-life environments.
{"title":"Implementation of P300 based BCI Using a Consumer-grade EEG Neuroheadset","authors":"Saleh I. Alzahrani","doi":"10.1109/nbec53282.2021.9618750","DOIUrl":"https://doi.org/10.1109/nbec53282.2021.9618750","url":null,"abstract":"Brain-computer interfaces (BCIs) provide a non-muscular means of communication and control for severely paralyzed patients. Many BCIs depend on the P300 which is an exogenous event-related potential (ERP) component produced about 300 ms in response to the presentation of an infrequent, but recognized, stimulus. Although there are different EEG neuroheadsets in the market used to record the P300, not all of them are suitable for daily use due to the system cost and set-up time. The present study investigated the reliability of an affordable, low-cost, and wireless EEG device, namely Emotiv EPOC+, to record the P300 signals. Ten healthy volunteers tested a P300 speller system to spell 10 random characters. EEG data were recorded while the subjects attended to the standard P300 paradigm introduced by Farwell and Donchin in 1988. We examined the effect of using different matrix size (6x6 and 3x3), flash duration (100 and 175 ms), and colored matrix (white/gray and green/blue) on the performance of the P300 speller. The results show that the P300 amplitude is positively correlated with larger matrix size and longer flash duration. Moreover, the results show that using color (green/blue) stimuli enhanced larger P300 amplitude. Using linear discriminant analysis (LDA) classifier, the highest classification accuracy achieved was $75.9 pm 7.22$% when using 6x6 matrix, 175 ms flash duration, and green/blue stimuli condition. We conclude that such an affordable and wireless neuroheadset system can provide severely disabled people an alternative communication and control technology to be used effectively in their real-life environments.","PeriodicalId":297399,"journal":{"name":"2021 IEEE National Biomedical Engineering Conference (NBEC)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132002485","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 : 2021-11-09DOI: 10.1109/nbec53282.2021.9618760
Nayli Nabila Azman, M. M. Addi
The world is currently facing a pandemic attack of Coronovirus disease (COVID-19), which is an infectious disease causing mild to moderate respiratory illness. One of the most common and early symptoms of COVID-19 is fever which is the reaction to a disease-specific stimulus causing the increase of the human body temperature. To slow down the transmission of the COVID-19 virus, the public is required have their body temperature measured when entering any premises. The current common method of monitoring the human body temperature uses the application of non-contact infrared thermometer (NCIT) and is only limited for stationary conditions within short distances and mostly suitable for indoor premises. The available technology to detect human body temperature for longer distances uses the thermal camera which is costly and large. Thus, it is challenging to detect anyone with high body temperature is non-stationary conditions, at longer distances, especially outdoor. The project proposes an innovation to the current practice, for a wearable noncontact temperature detector device which is portable. The wearable non-contact temperature detector embeds a thermal sensor and a microcontroller to a normal hat. It is able to detect objects with higher temperature (37.5°C) within 1 meter radius of 60° angle view in stationary and non-stationary conditions. The wearable device communicates via Bluetooth to a mobile device to display the detected temperature and notifies the user via alert message and alarm for high temperature detection. Display of the object’s thermal image is also available with a resolution of 8 $times$ 8 pixel. The wearable non-contact temperature detector is able to achieve 99% accuracy of temperature measurement for detection distance of up to 70 cm for indoor and within 20 cm for outdoor when tested with normal temperature subject and high temperature object and compared with the actual temperature detected via a commercial NCIT device.
{"title":"A Wearable Non-Contact Temperature Detector for Early Detection of Fever","authors":"Nayli Nabila Azman, M. M. Addi","doi":"10.1109/nbec53282.2021.9618760","DOIUrl":"https://doi.org/10.1109/nbec53282.2021.9618760","url":null,"abstract":"The world is currently facing a pandemic attack of Coronovirus disease (COVID-19), which is an infectious disease causing mild to moderate respiratory illness. One of the most common and early symptoms of COVID-19 is fever which is the reaction to a disease-specific stimulus causing the increase of the human body temperature. To slow down the transmission of the COVID-19 virus, the public is required have their body temperature measured when entering any premises. The current common method of monitoring the human body temperature uses the application of non-contact infrared thermometer (NCIT) and is only limited for stationary conditions within short distances and mostly suitable for indoor premises. The available technology to detect human body temperature for longer distances uses the thermal camera which is costly and large. Thus, it is challenging to detect anyone with high body temperature is non-stationary conditions, at longer distances, especially outdoor. The project proposes an innovation to the current practice, for a wearable noncontact temperature detector device which is portable. The wearable non-contact temperature detector embeds a thermal sensor and a microcontroller to a normal hat. It is able to detect objects with higher temperature (37.5°C) within 1 meter radius of 60° angle view in stationary and non-stationary conditions. The wearable device communicates via Bluetooth to a mobile device to display the detected temperature and notifies the user via alert message and alarm for high temperature detection. Display of the object’s thermal image is also available with a resolution of 8 $times$ 8 pixel. The wearable non-contact temperature detector is able to achieve 99% accuracy of temperature measurement for detection distance of up to 70 cm for indoor and within 20 cm for outdoor when tested with normal temperature subject and high temperature object and compared with the actual temperature detected via a commercial NCIT device.","PeriodicalId":297399,"journal":{"name":"2021 IEEE National Biomedical Engineering Conference (NBEC)","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133345094","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 : 2021-11-09DOI: 10.1109/nbec53282.2021.9618748
Nurul Fathia Mohamand Noor, N. Ahmad, N. Noor
Fetal Health monitoring is important to reduce or minimize the mortality of both mother and child. This paper presents a study on a dataset of 2126 records on features extracted from cardiotocography exam with 21 attributes including baseline value accelerations, fetal movement, uterine contractions, light, severe and prolonged decelerations, abnormal short-term variability, the mean value of short-term variability, percentage of time with abnormal long-term variability, the mean value of long-term variability, histogram width, min, max, number of peaks, number of zeroes, mode, mean, median, variance, and tendency. This paper will be using Supervised Machine Learning to compare and classify the data set using K-NN, Linear SVM, Naive Bayes, Decision Tree (J4S), Ada Boost, Bagging and Stacking. Lastly, Bayesian networks are then developed and compared with the other classifier. By comparing all of the classifiers, classifier Ada Boost with sub-model Random Forest has the highest accuracy 94.7% with k = 10.
胎儿健康监测是重要的，以减少或尽量减少母婴死亡率。本文研究了2126条心动图特征数据集，包括基线值加速、胎儿运动、子宫收缩、轻减速、严重减速和长减速、短期异常变异性、短期变异性均值、长期异常变异性时间百分比、长期变异性均值、直方图宽度、最小、最大、峰值数、零数、模态、平均值、中位数、方差和趋势。本文将使用监督机器学习来比较和分类数据集，使用K-NN，线性支持向量机，朴素贝叶斯，决策树(J4S)， Ada Boost, Bagging和Stacking。最后，发展贝叶斯网络并与其他分类器进行比较。通过比较所有分类器，具有子模型Random Forest的分类器Ada Boost在k = 10时准确率最高，为94.7%。
{"title":"Fetal Health Classification Using Supervised Learning Approach","authors":"Nurul Fathia Mohamand Noor, N. Ahmad, N. Noor","doi":"10.1109/nbec53282.2021.9618748","DOIUrl":"https://doi.org/10.1109/nbec53282.2021.9618748","url":null,"abstract":"Fetal Health monitoring is important to reduce or minimize the mortality of both mother and child. This paper presents a study on a dataset of 2126 records on features extracted from cardiotocography exam with 21 attributes including baseline value accelerations, fetal movement, uterine contractions, light, severe and prolonged decelerations, abnormal short-term variability, the mean value of short-term variability, percentage of time with abnormal long-term variability, the mean value of long-term variability, histogram width, min, max, number of peaks, number of zeroes, mode, mean, median, variance, and tendency. This paper will be using Supervised Machine Learning to compare and classify the data set using K-NN, Linear SVM, Naive Bayes, Decision Tree (J4S), Ada Boost, Bagging and Stacking. Lastly, Bayesian networks are then developed and compared with the other classifier. By comparing all of the classifiers, classifier Ada Boost with sub-model Random Forest has the highest accuracy 94.7% with k = 10.","PeriodicalId":297399,"journal":{"name":"2021 IEEE National Biomedical Engineering Conference (NBEC)","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121382055","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 : 2021-11-09DOI: 10.1109/nbec53282.2021.9618752
C. Jing, Goh Chuan Meng, C. M. Tyng, S. Aluwee, Wong Pei Voon
Intravenous (IV) access is a common and yet important daily clinical procedure that delivers fluids or medication into a patient’s vein. However, IV insertion is very challenging where clinicians are suffering in locating the subcutaneous vein due to patients’ physiological factors such as hairy forearm and thick dermis fat, and also medical staff’s level of fatigue. As a result, the patients are suffering from multiple IV insertions and the problem has not yet been resolved till-date. Thus, researchers have proposed an autonomous machine for IV access, but such equipment is lack of an artificial intelligence (AI) algorithm in detecting the vein accurately. Therefore, this project proposes an automatic vein detection algorithm using deep learning for Intravenous (IV) access purposes. U-Net, a fully connected network (FCN) architecture is employed in this project due to its capability in detecting the near-infrared (NIR) subcutaneous vein. In our experiment, data augmentation is applied to increase the dataset size and reduce the bias from overfitting. The original U-Net architecture is optimized by replacing up-sampling with transpose convolution as well as the additional implementation of batch normalization. Lastly, the proposed algorithm has achieved an accuracy and specificity of 0.9909 and 0.9970, respectively. This result indicates that the proposed algorithm can be applied into the venipuncture machine to locate the Subcutaneous vein for intravenous (IV) procedures.
{"title":"An Automatic Vein Detection System Using Deep Learning for Intravenous (IV) Access Procedure","authors":"C. Jing, Goh Chuan Meng, C. M. Tyng, S. Aluwee, Wong Pei Voon","doi":"10.1109/nbec53282.2021.9618752","DOIUrl":"https://doi.org/10.1109/nbec53282.2021.9618752","url":null,"abstract":"Intravenous (IV) access is a common and yet important daily clinical procedure that delivers fluids or medication into a patient’s vein. However, IV insertion is very challenging where clinicians are suffering in locating the subcutaneous vein due to patients’ physiological factors such as hairy forearm and thick dermis fat, and also medical staff’s level of fatigue. As a result, the patients are suffering from multiple IV insertions and the problem has not yet been resolved till-date. Thus, researchers have proposed an autonomous machine for IV access, but such equipment is lack of an artificial intelligence (AI) algorithm in detecting the vein accurately. Therefore, this project proposes an automatic vein detection algorithm using deep learning for Intravenous (IV) access purposes. U-Net, a fully connected network (FCN) architecture is employed in this project due to its capability in detecting the near-infrared (NIR) subcutaneous vein. In our experiment, data augmentation is applied to increase the dataset size and reduce the bias from overfitting. The original U-Net architecture is optimized by replacing up-sampling with transpose convolution as well as the additional implementation of batch normalization. Lastly, the proposed algorithm has achieved an accuracy and specificity of 0.9909 and 0.9970, respectively. This result indicates that the proposed algorithm can be applied into the venipuncture machine to locate the Subcutaneous vein for intravenous (IV) procedures.","PeriodicalId":297399,"journal":{"name":"2021 IEEE National Biomedical Engineering Conference (NBEC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115379482","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 : 2021-11-09DOI: 10.1109/nbec53282.2021.9618747
Nurul Fathia Mohamand Noor, Herold Sylvestro Sipail, N. Ahmad, N. Noor
This paper presented work on supervised machine learning techniques using K-NN, Linear SVM, Naïve Bayes, Decision Tree (J48), Ada Boost, Bagging and Stacking for the purpose to classify the severity group of covid-19 symptoms. The data was obtained from Kaggle dataset, which was obtained through a survey collected from the participant with varying gender and age that had visited 10 or more countries including China, France, Germany Iran, Italy, Republic of Korean, Spain, UAE, other European Countries (Other-EUR) and Others. The survey is Covid-19 symptom based on guidelines given by the World Health Organization (WHO) and the Ministry of Health and Family Welfare, India which then classified into 4 different levels of severity, Mild, Moderate, Severe, and None. The results from the seven classifiers used in this study showed very low classification results.
{"title":"Covid-19 Severity Classification Using Supervised Learning Approach","authors":"Nurul Fathia Mohamand Noor, Herold Sylvestro Sipail, N. Ahmad, N. Noor","doi":"10.1109/nbec53282.2021.9618747","DOIUrl":"https://doi.org/10.1109/nbec53282.2021.9618747","url":null,"abstract":"This paper presented work on supervised machine learning techniques using K-NN, Linear SVM, Naïve Bayes, Decision Tree (J48), Ada Boost, Bagging and Stacking for the purpose to classify the severity group of covid-19 symptoms. The data was obtained from Kaggle dataset, which was obtained through a survey collected from the participant with varying gender and age that had visited 10 or more countries including China, France, Germany Iran, Italy, Republic of Korean, Spain, UAE, other European Countries (Other-EUR) and Others. The survey is Covid-19 symptom based on guidelines given by the World Health Organization (WHO) and the Ministry of Health and Family Welfare, India which then classified into 4 different levels of severity, Mild, Moderate, Severe, and None. The results from the seven classifiers used in this study showed very low classification results.","PeriodicalId":297399,"journal":{"name":"2021 IEEE National Biomedical Engineering Conference (NBEC)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130278542","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 : 2021-11-09DOI: 10.1109/nbec53282.2021.9618756
N. Salleh, M. Mazlan, N. Abdullah, Ida Laila Ahmad, A. H. Abdullah, M. H. Jalil, H. Takano, Nur Dalilah Diyana Nordin
Degenerative Disc Disease is a condition of the spine when the intervertebral disc begins to collapse. This disease occurs in the human spine, especially in the lumbar spine, because the primary function of the lumbar spine is to support the weight of the body. There are many treatments for this disease, and one of the treatment methods is Posterior Lumbar Interbody Fusion (PLIF) surgery. There are few implications of the PLIF surgery, such as cage subsidence, cage failure, cage migration, and highly concentrated stress effect on the cage. The aim of the study was to develop an interbody cage that can be implanted into the spine and reduce the post-operative effects using the Finite Element Analysis (FEA) approach. In this study, various infill densities of the interbody cage were designed using Solidworks software and analyzed using Ansys software. Polylactic Acid (PLA) was assigned as a cage material. The cage was implanted between L4 and L5 to create the three dimensional (3D) model, in which the spine model was developed from extracted CT scan images using 3D Slicer software. The model was analyzed based on von Mises stress and maximum principal stress compared with the yield strength and ultimate tensile strength of the material, respectively. The 3D model was loaded with flexion, extension, axial rotation, lateral bending and compression to mimic the physiological motions of the spine. The analysis showed that the interbody cage with 50% infill density has been identified as the most appropriate design according to the acceptable range of stresses generated, fastest estimated printing time, and required the least amount of printing material.
{"title":"Design and analysis of infill density effects on interbody fusion cage construct based on finite element analysis","authors":"N. Salleh, M. Mazlan, N. Abdullah, Ida Laila Ahmad, A. H. Abdullah, M. H. Jalil, H. Takano, Nur Dalilah Diyana Nordin","doi":"10.1109/nbec53282.2021.9618756","DOIUrl":"https://doi.org/10.1109/nbec53282.2021.9618756","url":null,"abstract":"Degenerative Disc Disease is a condition of the spine when the intervertebral disc begins to collapse. This disease occurs in the human spine, especially in the lumbar spine, because the primary function of the lumbar spine is to support the weight of the body. There are many treatments for this disease, and one of the treatment methods is Posterior Lumbar Interbody Fusion (PLIF) surgery. There are few implications of the PLIF surgery, such as cage subsidence, cage failure, cage migration, and highly concentrated stress effect on the cage. The aim of the study was to develop an interbody cage that can be implanted into the spine and reduce the post-operative effects using the Finite Element Analysis (FEA) approach. In this study, various infill densities of the interbody cage were designed using Solidworks software and analyzed using Ansys software. Polylactic Acid (PLA) was assigned as a cage material. The cage was implanted between L4 and L5 to create the three dimensional (3D) model, in which the spine model was developed from extracted CT scan images using 3D Slicer software. The model was analyzed based on von Mises stress and maximum principal stress compared with the yield strength and ultimate tensile strength of the material, respectively. The 3D model was loaded with flexion, extension, axial rotation, lateral bending and compression to mimic the physiological motions of the spine. The analysis showed that the interbody cage with 50% infill density has been identified as the most appropriate design according to the acceptable range of stresses generated, fastest estimated printing time, and required the least amount of printing material.","PeriodicalId":297399,"journal":{"name":"2021 IEEE National Biomedical Engineering Conference (NBEC)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134412662","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 : 2021-11-09DOI: 10.1109/nbec53282.2021.9618754
Muhammad Izzuddin Md Isa, S. Shuib, A. Z. Romli, A. Shokri, Iffa Mohd Arrif, Najwa Syakirah Hamizan
Polymethyl methacrylate (PMMA) bone cement was introduced for the total hip replacement component’s fixation. Cement failure in total hip replacement whether in the short-term or long-term will be harmful to the patient’s health and caused osteoarthritis, hip fractures, and dislocations. The purpose was to find the suitable cement mixtures for total hip replacement consists of Young’s Modulus of 2240 MPa, 312.931 MPa, 33.939 MPa and 79.609 MPa which were taken from the previous research. The PMMA cement was used with three different types of proximal cemented techniques such as 40 mm cement reduction, 80 mm cement reduction and full cement (datum). The ANSYS Workbench 2020 R2 software was used to analyze the Charnley Hip Implant with Titanium Ti-6A1-4V (Ti-41) stem model using a Young’s Modulus of 100,000 MPa and a Poisson’s ratio of 0.3. The analysis was based on total deformation and Von Mises stress under different types of loading conditions such as standing, walking, stair climbing and falling. The results showed that all the hip implants were considered safe because their stress does not exceed the yield strength value of the material assigned which is 880 MPa. In conclusion, the 40 mm cement reduction with Young’s Modulus of 2240 MPa obtained the most improved in terms of Von Mises stress and total deformation compared with the full cement (datum) and 80 mm cement reduction with Young’s Modulus of 2240 MPa, 312.931 MPa, 33.939 MPa and 79.609 MPa.
{"title":"Finite Element Analysis (FEA) of the Different Cement Mixture for Total Hip Replacement","authors":"Muhammad Izzuddin Md Isa, S. Shuib, A. Z. Romli, A. Shokri, Iffa Mohd Arrif, Najwa Syakirah Hamizan","doi":"10.1109/nbec53282.2021.9618754","DOIUrl":"https://doi.org/10.1109/nbec53282.2021.9618754","url":null,"abstract":"Polymethyl methacrylate (PMMA) bone cement was introduced for the total hip replacement component’s fixation. Cement failure in total hip replacement whether in the short-term or long-term will be harmful to the patient’s health and caused osteoarthritis, hip fractures, and dislocations. The purpose was to find the suitable cement mixtures for total hip replacement consists of Young’s Modulus of 2240 MPa, 312.931 MPa, 33.939 MPa and 79.609 MPa which were taken from the previous research. The PMMA cement was used with three different types of proximal cemented techniques such as 40 mm cement reduction, 80 mm cement reduction and full cement (datum). The ANSYS Workbench 2020 R2 software was used to analyze the Charnley Hip Implant with Titanium Ti-6A1-4V (Ti-41) stem model using a Young’s Modulus of 100,000 MPa and a Poisson’s ratio of 0.3. The analysis was based on total deformation and Von Mises stress under different types of loading conditions such as standing, walking, stair climbing and falling. The results showed that all the hip implants were considered safe because their stress does not exceed the yield strength value of the material assigned which is 880 MPa. In conclusion, the 40 mm cement reduction with Young’s Modulus of 2240 MPa obtained the most improved in terms of Von Mises stress and total deformation compared with the full cement (datum) and 80 mm cement reduction with Young’s Modulus of 2240 MPa, 312.931 MPa, 33.939 MPa and 79.609 MPa.","PeriodicalId":297399,"journal":{"name":"2021 IEEE National Biomedical Engineering Conference (NBEC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130268753","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 : 2021-11-09DOI: 10.1109/nbec53282.2021.9618711
Z. Taghizadeh, Sina Nateghi
Real-time control of prosthetic hands has attracted huge attention from researchers in recent years. Real-time analysis of Electromyography (EMG) signals has several challenges. The most important one is to achieve an acceptable classification accuracy by observing a limited length of the EMG signal. In this paper, we address these challenges i.e., we enhance the classification accuracy and reduce the required observation signal’s length. These goals are achieved by employing extracted features from time, frequency, and time-frequency domains and introducing a new neural network architecture to combine these features. The experimental results illustrate that combining features from different domains and the proposed architecture improve the accuracy of real-time classification of EMG signals in comparison to existing state-of-the-art methods.
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