Pub Date : 2022-10-21DOI: 10.1109/ICCSCE54767.2022.9935664
A. Taib, Ariff As-Syadiqin Abdullah, Muhammad Azizi Mohd Ariffin, Rafiza Ruslan
To train new staff to be efficient and ready for the tasks assigned is vital. They must be equipped with knowledge and skills so that they can carry out their responsibility to ensure smooth daily working activities. As transitioning to IPv6 has taken place for more than a decade, it is understood that having a dual-stack network is common in any organization or enterprise. However, many Internet users may not realize the importance of IPv6 security due to a lack of awareness and knowledge of cyber and computer security. Therefore, this paper presents an approach to educating people by introducing a security mechanisms model that can be applied in handling security challenges via network sandboxing by setting up an isolated dual stack network testbed using GNS3 to perform network security analysis. The finding shows that applying security mechanisms such as access control lists (ACLs) and host-based firewalls can help counter the attacks. This proves that knowledge and skills to handle dual-stack security are crucial. In future, more kinds of attacks should be tested and also more types of security mechanisms can be applied on a dual-stack network to provide more information and to provide network engineers insights on how they can benefit from network sandboxing to sharpen their knowledge and skills.
{"title":"Threats and Vulnerabilities Handling via Dual-stack Sandboxing Based on Security Mechanisms Model","authors":"A. Taib, Ariff As-Syadiqin Abdullah, Muhammad Azizi Mohd Ariffin, Rafiza Ruslan","doi":"10.1109/ICCSCE54767.2022.9935664","DOIUrl":"https://doi.org/10.1109/ICCSCE54767.2022.9935664","url":null,"abstract":"To train new staff to be efficient and ready for the tasks assigned is vital. They must be equipped with knowledge and skills so that they can carry out their responsibility to ensure smooth daily working activities. As transitioning to IPv6 has taken place for more than a decade, it is understood that having a dual-stack network is common in any organization or enterprise. However, many Internet users may not realize the importance of IPv6 security due to a lack of awareness and knowledge of cyber and computer security. Therefore, this paper presents an approach to educating people by introducing a security mechanisms model that can be applied in handling security challenges via network sandboxing by setting up an isolated dual stack network testbed using GNS3 to perform network security analysis. The finding shows that applying security mechanisms such as access control lists (ACLs) and host-based firewalls can help counter the attacks. This proves that knowledge and skills to handle dual-stack security are crucial. In future, more kinds of attacks should be tested and also more types of security mechanisms can be applied on a dual-stack network to provide more information and to provide network engineers insights on how they can benefit from network sandboxing to sharpen their knowledge and skills.","PeriodicalId":346014,"journal":{"name":"2022 IEEE 12th International Conference on Control System, Computing and Engineering (ICCSCE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129251121","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 : 2022-10-21DOI: 10.1109/ICCSCE54767.2022.9935655
A. Samat, Muhammad Irfan Bin Ahmad Jaafar, A. I. Tajudin, N. A. Salim, K. Daud, Nornaim Kamarudin
This project designed the speed control of a separately excited direct current (DC) motor by using an Artificial Neural Network (ANN). Any conventional controller such as proportional-integral (PI) can be used to control the speed of a DC Motor. However, the limitation of the conventional controller in controlling the speed of the dc motor is inaccuracy in the ability to obtain the actual speed and maintain the stability of the motor speed in the dynamic condition. Thus, the ANN controller had been introduced to solve the problem involving the limitation of another conventional controller. The neural network is used in this project to control or estimate the motor speed by training the neural network and getting the desired result using MATLAB/SIMULINK software. In this project, the ANN has proven its ability to control motor speed compared to the PI controller effectively and has good performance in a nonlinear system. The simulation results show the advantages and efficiency of an ANN with minimum speed error which is approximately zero rpm.
{"title":"Speed Control of SEDC Motor Using Artificial Neural Network","authors":"A. Samat, Muhammad Irfan Bin Ahmad Jaafar, A. I. Tajudin, N. A. Salim, K. Daud, Nornaim Kamarudin","doi":"10.1109/ICCSCE54767.2022.9935655","DOIUrl":"https://doi.org/10.1109/ICCSCE54767.2022.9935655","url":null,"abstract":"This project designed the speed control of a separately excited direct current (DC) motor by using an Artificial Neural Network (ANN). Any conventional controller such as proportional-integral (PI) can be used to control the speed of a DC Motor. However, the limitation of the conventional controller in controlling the speed of the dc motor is inaccuracy in the ability to obtain the actual speed and maintain the stability of the motor speed in the dynamic condition. Thus, the ANN controller had been introduced to solve the problem involving the limitation of another conventional controller. The neural network is used in this project to control or estimate the motor speed by training the neural network and getting the desired result using MATLAB/SIMULINK software. In this project, the ANN has proven its ability to control motor speed compared to the PI controller effectively and has good performance in a nonlinear system. The simulation results show the advantages and efficiency of an ANN with minimum speed error which is approximately zero rpm.","PeriodicalId":346014,"journal":{"name":"2022 IEEE 12th International Conference on Control System, Computing and Engineering (ICCSCE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131383961","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 : 2022-10-21DOI: 10.1109/ICCSCE54767.2022.9935635
Anuar Mohamed, N. A. Othman, H. Ahmad, M. Hassan
Human motion monitoring by means of wearable technologies is not uncommon nowadays. This demonstrates the growing awareness of the importance of healthy lifestyle. Human body motion involves the movement of multiple muscles and joints. However, the optimal location of sensor placement on the body to record the motion in daily activities has not been well understood. This study aims to find the best sensor location for this purpose among three locations on the body, that is on the back, shank, or wrist. In addition, this study seeks to find the best classification algorithm for human daily activities. The data recorded at these three locations were analysed using several classification algorithms in both Orange software and MATLAB. The results show that the sensor on the wrist provided the best classification result, thereby suggesting that wrist is the best place on the body to place the sensor for human motion monitoring. With regards to classification algorithm, we found that Neural Network provides the most accurate classification as compared to other algorithms. Future development of wearables should look into integrating classification algorithm in the system, thus the human motion monitoring will provide a richer information and not only limited to number of steps and calories burned.
{"title":"Investigation of the Optimal Sensor Location and Classifier for Human Motion Classification","authors":"Anuar Mohamed, N. A. Othman, H. Ahmad, M. Hassan","doi":"10.1109/ICCSCE54767.2022.9935635","DOIUrl":"https://doi.org/10.1109/ICCSCE54767.2022.9935635","url":null,"abstract":"Human motion monitoring by means of wearable technologies is not uncommon nowadays. This demonstrates the growing awareness of the importance of healthy lifestyle. Human body motion involves the movement of multiple muscles and joints. However, the optimal location of sensor placement on the body to record the motion in daily activities has not been well understood. This study aims to find the best sensor location for this purpose among three locations on the body, that is on the back, shank, or wrist. In addition, this study seeks to find the best classification algorithm for human daily activities. The data recorded at these three locations were analysed using several classification algorithms in both Orange software and MATLAB. The results show that the sensor on the wrist provided the best classification result, thereby suggesting that wrist is the best place on the body to place the sensor for human motion monitoring. With regards to classification algorithm, we found that Neural Network provides the most accurate classification as compared to other algorithms. Future development of wearables should look into integrating classification algorithm in the system, thus the human motion monitoring will provide a richer information and not only limited to number of steps and calories burned.","PeriodicalId":346014,"journal":{"name":"2022 IEEE 12th International Conference on Control System, Computing and Engineering (ICCSCE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117137923","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 : 2022-10-21DOI: 10.1109/ICCSCE54767.2022.9935656
A. H. M. Saod, Muhammad Khairul Fikri Kushiar, N. H. Ishak
In a telemetry system, data related to the agriculture product can be monitored through the cloud server without physically being at the farm location. This project aims to develop a telemetry system for monitoring significant parameters of highland tomato plants to produce high-quality fresh tomatoes. The system is equipped with a light intensity detector, a single-chip humidity-temperature sensor, and a soil moisture sensor controlled by Raspberry Pi. The data collection is conducted at MARDI Agrotechnology Park, Cameron Highlands, to measure the parameters of red beefsteak tomatoes. The developed system can monitor the measured parameters via Ubidots dashboard and trigger the irrigation system when the soil moisture is below the optimum level. The user will be notified when the surrounding temperature is higher than the threshold value. Results show that the telemetry system can be viable for monitoring tasks. The threshold values of the highland tomato plant parameters are observed, with a minimum moisture level of 60% and a maximum temperature of 29°C to increase the tomato yield.
{"title":"Telemetry System for Highland Tomato Plants Using Ubidots Platform","authors":"A. H. M. Saod, Muhammad Khairul Fikri Kushiar, N. H. Ishak","doi":"10.1109/ICCSCE54767.2022.9935656","DOIUrl":"https://doi.org/10.1109/ICCSCE54767.2022.9935656","url":null,"abstract":"In a telemetry system, data related to the agriculture product can be monitored through the cloud server without physically being at the farm location. This project aims to develop a telemetry system for monitoring significant parameters of highland tomato plants to produce high-quality fresh tomatoes. The system is equipped with a light intensity detector, a single-chip humidity-temperature sensor, and a soil moisture sensor controlled by Raspberry Pi. The data collection is conducted at MARDI Agrotechnology Park, Cameron Highlands, to measure the parameters of red beefsteak tomatoes. The developed system can monitor the measured parameters via Ubidots dashboard and trigger the irrigation system when the soil moisture is below the optimum level. The user will be notified when the surrounding temperature is higher than the threshold value. Results show that the telemetry system can be viable for monitoring tasks. The threshold values of the highland tomato plant parameters are observed, with a minimum moisture level of 60% and a maximum temperature of 29°C to increase the tomato yield.","PeriodicalId":346014,"journal":{"name":"2022 IEEE 12th International Conference on Control System, Computing and Engineering (ICCSCE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114551736","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 : 2022-10-21DOI: 10.1109/ICCSCE54767.2022.9935638
Syafiqah Aqilah Saifudin, S. N. Sulaiman, N. Karim, M. K. Osman, I. Isa, N. A. Harron
Microcalcification is the major focus in the early stages of breast cancer detection; thus, microcalcification detection is essential in early treatment and increases the survival rate. Since Digital Breast Tomosynthesis (DBT) images have been shown to improve the overlapping issue in mammograms, the use of this screening process is important to obtain a better perspective of microcalcifications. However, the DBT screening techniques produce blurry artifacts and noises leading this study to propose a stage for DBT image enhancement. Hence, this study proposes an enhancement method based on Non-Linear Unsharp Masking filters (NLUM). The NLUM needs a filter to complete the element of non-linear in the algorithm as Median Filter in conventional NLUM. Previously, the Hybrid Maximum Filter (H3F) and Hybrid Sigma Filter (H4F) have been proposed and demonstrated by other researchers to improve medical images, thus these filters can be adapted to the NLUM and replaced the conventional filter. Following that, the performance of the enhancement process will be assessed using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM). The results show that the H4F is the best filter to use in NLUM successfully enhances the DBT images when compared to Median Filter and H3F, with MSE, PSNR, and SSIM averages of 0.0198, 66.4000, and 0.9417, respectively.
{"title":"A Comparative Study of Unsharp Masking Filters for Enhancement of Digital Breast Tomosynthesis Images","authors":"Syafiqah Aqilah Saifudin, S. N. Sulaiman, N. Karim, M. K. Osman, I. Isa, N. A. Harron","doi":"10.1109/ICCSCE54767.2022.9935638","DOIUrl":"https://doi.org/10.1109/ICCSCE54767.2022.9935638","url":null,"abstract":"Microcalcification is the major focus in the early stages of breast cancer detection; thus, microcalcification detection is essential in early treatment and increases the survival rate. Since Digital Breast Tomosynthesis (DBT) images have been shown to improve the overlapping issue in mammograms, the use of this screening process is important to obtain a better perspective of microcalcifications. However, the DBT screening techniques produce blurry artifacts and noises leading this study to propose a stage for DBT image enhancement. Hence, this study proposes an enhancement method based on Non-Linear Unsharp Masking filters (NLUM). The NLUM needs a filter to complete the element of non-linear in the algorithm as Median Filter in conventional NLUM. Previously, the Hybrid Maximum Filter (H3F) and Hybrid Sigma Filter (H4F) have been proposed and demonstrated by other researchers to improve medical images, thus these filters can be adapted to the NLUM and replaced the conventional filter. Following that, the performance of the enhancement process will be assessed using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM). The results show that the H4F is the best filter to use in NLUM successfully enhances the DBT images when compared to Median Filter and H3F, with MSE, PSNR, and SSIM averages of 0.0198, 66.4000, and 0.9417, respectively.","PeriodicalId":346014,"journal":{"name":"2022 IEEE 12th International Conference on Control System, Computing and Engineering (ICCSCE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127027155","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 : 2022-10-21DOI: 10.1109/ICCSCE54767.2022.9935653
Haruna Abdu, M. H. M. Noor
Environmental contamination is a major issue affecting all inhabitants living in any environment. The domestic environment is engulfed with many trash items such as solid and toxic trashes, leading to severe environmental contamination and causing life-threatening diseases if not appropriately managed. Trash classification is at the heart of these issues because the inability to classify the trash leads to difficulty in recycling. Humans categorize trash based on what they understand about the trash object rather than on the recyclability status of an object, which frequently leads to incorrect classification in manual classification. Additionally, coming into contact with toxic waste directly could be physically dangerous for those involved. Few machine learning and Deep Learning (DL) techniques were proposed using benchmarked trash classification datasets. However, most benchmarked datasets used to train DL models have a transparent or white background, which leads to a lack of model generalization, particularly in the real world. In this paper, we propose a Deep Learning model based on the VGG16 Architecture that can accurately classify various types of trash objects. On the TrashNet dataset plus the images collected in the wild, we achieved an accuracy of more than 96%.
{"title":"Domestic Trash Classification with Transfer Learning Using VGG16","authors":"Haruna Abdu, M. H. M. Noor","doi":"10.1109/ICCSCE54767.2022.9935653","DOIUrl":"https://doi.org/10.1109/ICCSCE54767.2022.9935653","url":null,"abstract":"Environmental contamination is a major issue affecting all inhabitants living in any environment. The domestic environment is engulfed with many trash items such as solid and toxic trashes, leading to severe environmental contamination and causing life-threatening diseases if not appropriately managed. Trash classification is at the heart of these issues because the inability to classify the trash leads to difficulty in recycling. Humans categorize trash based on what they understand about the trash object rather than on the recyclability status of an object, which frequently leads to incorrect classification in manual classification. Additionally, coming into contact with toxic waste directly could be physically dangerous for those involved. Few machine learning and Deep Learning (DL) techniques were proposed using benchmarked trash classification datasets. However, most benchmarked datasets used to train DL models have a transparent or white background, which leads to a lack of model generalization, particularly in the real world. In this paper, we propose a Deep Learning model based on the VGG16 Architecture that can accurately classify various types of trash objects. On the TrashNet dataset plus the images collected in the wild, we achieved an accuracy of more than 96%.","PeriodicalId":346014,"journal":{"name":"2022 IEEE 12th International Conference on Control System, Computing and Engineering (ICCSCE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114500494","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 : 2022-10-21DOI: 10.1109/ICCSCE54767.2022.9935649
Mat Nizam Mahmud, Muhammad Hiszarul Azim, M Fazmi Hisham, M. K. Osman, A. P. Ismail, F. Ahmad, K. A. Ahmad, A. Ibrahim, Azmir Hasnur Rabiani
DeepLab V3+ semantic segmentation develops road segmentation from UAV images. First, a camera-equipped UAV captures road images from 3 altitudes in Perlis. The images will be resized and augmented to provide additional road images for deep learning model training. Next, images are manually segmented into road and background using CVAT. The DeepLab V3+ with Resnet-18, Resnet-50, and MobileNet V2 backbone network is utilised to segment the road using Matlab. Finally, the suggested method's performance is compared to all backbone network approaches at 3 various altitudes to determine pixel accuracy (PA), mean intersection over union (mIoU), and meanF1-score (meanF1). The study develops an accurate and robust approach for road segmentation from UAV images that road surveyors may employ for inspection and monitoring. This technique might be implemented to identify road cracks and potholes in the future study.
{"title":"Altitude Analysis of Road Segmentation from UAV Images with DeepLab V3+","authors":"Mat Nizam Mahmud, Muhammad Hiszarul Azim, M Fazmi Hisham, M. K. Osman, A. P. Ismail, F. Ahmad, K. A. Ahmad, A. Ibrahim, Azmir Hasnur Rabiani","doi":"10.1109/ICCSCE54767.2022.9935649","DOIUrl":"https://doi.org/10.1109/ICCSCE54767.2022.9935649","url":null,"abstract":"DeepLab V3+ semantic segmentation develops road segmentation from UAV images. First, a camera-equipped UAV captures road images from 3 altitudes in Perlis. The images will be resized and augmented to provide additional road images for deep learning model training. Next, images are manually segmented into road and background using CVAT. The DeepLab V3+ with Resnet-18, Resnet-50, and MobileNet V2 backbone network is utilised to segment the road using Matlab. Finally, the suggested method's performance is compared to all backbone network approaches at 3 various altitudes to determine pixel accuracy (PA), mean intersection over union (mIoU), and meanF1-score (meanF1). The study develops an accurate and robust approach for road segmentation from UAV images that road surveyors may employ for inspection and monitoring. This technique might be implemented to identify road cracks and potholes in the future study.","PeriodicalId":346014,"journal":{"name":"2022 IEEE 12th International Conference on Control System, Computing and Engineering (ICCSCE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126969031","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 : 2022-10-21DOI: 10.1109/ICCSCE54767.2022.9935641
Fatini Divana Mohamad Fadzil, M. M. A. Abdul Jamil, R. Ambar, W. S. Wan Zaki, Nur Adilah Abd Rahman
The purpose of this study was to compare the maximal dominant hand grip strength and the non-dominant grip force of professional athletes with grip-related soft tissue injury. This is to pinpoint the damaged soft tissue muscle area as it cannot be located by any means of screening, for example, x-ray. The subjects were chosen by male and female of five different sports that uses both hands crucially and with probability attained said injury. The subjects will test for an all-out maximum spherical grasps by gripping a softball as hard as they can with the dominant hand in an interval with repetitions. On each data collection, each subject was given three trials grasping a softball spaced thirty seconds apart. They were then required to repeat the same effort with the alternative grip force. The means were computed for dominant and non-dominant scores. An analysis of comparison of the subjects with the grip strength standard is then obtained and recorded.
{"title":"Athletes Soft Tissue Injury Monitoring System via Grip Strength Measurement","authors":"Fatini Divana Mohamad Fadzil, M. M. A. Abdul Jamil, R. Ambar, W. S. Wan Zaki, Nur Adilah Abd Rahman","doi":"10.1109/ICCSCE54767.2022.9935641","DOIUrl":"https://doi.org/10.1109/ICCSCE54767.2022.9935641","url":null,"abstract":"The purpose of this study was to compare the maximal dominant hand grip strength and the non-dominant grip force of professional athletes with grip-related soft tissue injury. This is to pinpoint the damaged soft tissue muscle area as it cannot be located by any means of screening, for example, x-ray. The subjects were chosen by male and female of five different sports that uses both hands crucially and with probability attained said injury. The subjects will test for an all-out maximum spherical grasps by gripping a softball as hard as they can with the dominant hand in an interval with repetitions. On each data collection, each subject was given three trials grasping a softball spaced thirty seconds apart. They were then required to repeat the same effort with the alternative grip force. The means were computed for dominant and non-dominant scores. An analysis of comparison of the subjects with the grip strength standard is then obtained and recorded.","PeriodicalId":346014,"journal":{"name":"2022 IEEE 12th International Conference on Control System, Computing and Engineering (ICCSCE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127383512","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 : 2022-10-21DOI: 10.1109/ICCSCE54767.2022.9935646
M. Azraai, S. Z. Yahaya, I. A. Chong, Z. H. C. Soh, Z. Hussain, R. Boudville
People who have suffered a spinal cord injury (SCI) may experience temporary or permanent loss of motor function, sensory function, and autonomic function. Tetraplegia patients are only able to move their upper body parts, such as the head, neck, and shoulder. They require wheelchair assistance to move around for their daily activities. The existing electric wheelchairs, on the other hand, rely on the users' upper arm for control which makes it difficult for the tetraplegia patients to control it. To address this issue, this project developed a control system in which control can be performed by head gesture. A gyro accelerometer is used to detect the user's head gesture. A microcontroller connected to the sensor will read the data and translate it into instructions to control the movement of the electric wheelchair based on the pre-defined head motion patterns. To obtain an average test result of the system's functionality, the system was tested on a healthy adult subject. The average maneuvering error of the trial run using electric wheelchair model on the smooth surface was 3.18cm and an average 5.2cm on the rough tar road surface. Thus, the developed control system can be assumed to be effective in detecting head gesture and that it accurately maneuvers the electric wheelchair according to the head gesture pattern.
{"title":"Head Gestures Based Movement Control of Electric Wheelchair for People with Tetraplegia","authors":"M. Azraai, S. Z. Yahaya, I. A. Chong, Z. H. C. Soh, Z. Hussain, R. Boudville","doi":"10.1109/ICCSCE54767.2022.9935646","DOIUrl":"https://doi.org/10.1109/ICCSCE54767.2022.9935646","url":null,"abstract":"People who have suffered a spinal cord injury (SCI) may experience temporary or permanent loss of motor function, sensory function, and autonomic function. Tetraplegia patients are only able to move their upper body parts, such as the head, neck, and shoulder. They require wheelchair assistance to move around for their daily activities. The existing electric wheelchairs, on the other hand, rely on the users' upper arm for control which makes it difficult for the tetraplegia patients to control it. To address this issue, this project developed a control system in which control can be performed by head gesture. A gyro accelerometer is used to detect the user's head gesture. A microcontroller connected to the sensor will read the data and translate it into instructions to control the movement of the electric wheelchair based on the pre-defined head motion patterns. To obtain an average test result of the system's functionality, the system was tested on a healthy adult subject. The average maneuvering error of the trial run using electric wheelchair model on the smooth surface was 3.18cm and an average 5.2cm on the rough tar road surface. Thus, the developed control system can be assumed to be effective in detecting head gesture and that it accurately maneuvers the electric wheelchair according to the head gesture pattern.","PeriodicalId":346014,"journal":{"name":"2022 IEEE 12th International Conference on Control System, Computing and Engineering (ICCSCE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122061767","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 : 2022-10-21DOI: 10.1109/ICCSCE54767.2022.9935628
Siti Hazurah Indera Putera, M. Dzulkifli, N. Sidek, Z. A. Bakar, Nurul Nadia Binti Mohammad
Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are standard imaging techniques used for diagnosis of various medical conditions in clinical settings. They are used to generate two-dimensional (2D) images of internal organs and tissues. Digital Imaging and Communications in Medicine (DICOM) is the standardized practice for processing, transferring, and storing medical images such as CT, x-ray, and MRI. This paper proposes a method to produce three-dimensional (3D) descriptions of the lungs and estimation of the length and volumes of the lungs. The 3D image generation and estimation of lung volumes were performed using simple image processing tools in Matlab® on two sets of 2D DICOM protocol images of the thorax taken from different healthy volunteers. Two sets of images are used in this paper; a set of 2D MRI slices a set of of 2D CT images. The DICOM images are obtained from the Sheffield Royal Hallamshire Hospital, United Kingdom. Generation of the 3D images of the lungs were performed by determining the grey-scale equivalent values for the lung tissues and setting the threshold levels for the lung tissues. The grey-scale images are converted into binary images and the estimated 3D images are rendered. Information from the DICOM image metafile such as the pixel equivalent area, calibration factor, slice thickness, and the size of the reconstructed areas were used to estimate the lengths and volumes of the lungs. Extrapolation of the estimated lungs were made using linear regression and second order polynomial regression analysis to ensure all areas of the lungs were considered in the lung volume estimations. The resulting volume estimations were between 2588ml and 3273ml for the MRI images and between 1891.55ml and 2223.84ml for the CT images.
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