Pub Date : 2023-03-16DOI: 10.1109/ICBSII58188.2023.10181065
R. Chitra, N. Jayapreetha, D. Swetha, S. Swetha
The main problem that often arises for doctor is using stethoscope to detect lung sounds. The analysis of lung sound obtained by a stethoscope is challenging when the signal level is extremely low. As a consequence, the current acoustic stethoscope has to be replaced by a digital electronic stethoscope. The primary goal of this study is to design a digital stethoscope that monitor lungs sounds and identify potential illnesses using CNN model. The notification of the detected disease is sent by pushover application and graphical representation is shown in the THINKSPEAK application. Based on the analysis of the CNN model, the lung disease detection method achieved an average accuracy of 95%, which means it could be applied to diagnosis of lung disease in the real world.
{"title":"Digital Stethoscope For Instant Monitoring For Cardiac Auscultation","authors":"R. Chitra, N. Jayapreetha, D. Swetha, S. Swetha","doi":"10.1109/ICBSII58188.2023.10181065","DOIUrl":"https://doi.org/10.1109/ICBSII58188.2023.10181065","url":null,"abstract":"The main problem that often arises for doctor is using stethoscope to detect lung sounds. The analysis of lung sound obtained by a stethoscope is challenging when the signal level is extremely low. As a consequence, the current acoustic stethoscope has to be replaced by a digital electronic stethoscope. The primary goal of this study is to design a digital stethoscope that monitor lungs sounds and identify potential illnesses using CNN model. The notification of the detected disease is sent by pushover application and graphical representation is shown in the THINKSPEAK application. Based on the analysis of the CNN model, the lung disease detection method achieved an average accuracy of 95%, which means it could be applied to diagnosis of lung disease in the real world.","PeriodicalId":388866,"journal":{"name":"2023 International Conference on Bio Signals, Images, and Instrumentation (ICBSII)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116596843","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 : 2023-03-16DOI: 10.1109/ICBSII58188.2023.10181080
S. Krishnakumar, M. Najeeba., A. Fazila, J. Bethanney Janney, Sindu Divakaran, A. Sabarivani
The mandated project is concerned with coming up with a sophisticated and cutting-edge way for average people to assess their health without a doctor’s or lab technician’s assistance. They can use the suggested equipment to independently monitor themselves. Sensors like the LM35 temperature sensor and blood pressure sensor are used in health monitoring systems. The technology has been modified for a wireless emergency telemedicine system, and in this prototype, it aids the blinded by converting text into speech so they can hear the output. Remote health monitoring raises the standard of care, cuts down on attention, and gives patients more control. The system includes an Arduino, data cable, non-invasive glucometer, digital sphygmomanometer, pulse oximeter sensor, heartbeat sensor, A/D device, signal learning circuit, and mobile. It is a reasonably priced, detachable device. Blood pressure, oxygen saturation, temperature, pulse rate, glucose level, and voice communication will all be displayed as a result of this gadget. In this project, an Arduino Uno is used to demonstrate IoT based e - health monitoring system. The system tracks the patient’s critical health metrics and broadcasts the observed data on a specific IP address (WiFi). To demonstrate the effectiveness of the suggested system, a prototype has been created. In order to avert problems and provide care for patients at the appropriate moment, the current research enables people periodically evaluate their health metrics.
{"title":"Designing of IoT Based Portable Vital Health Parameter Monitoring System","authors":"S. Krishnakumar, M. Najeeba., A. Fazila, J. Bethanney Janney, Sindu Divakaran, A. Sabarivani","doi":"10.1109/ICBSII58188.2023.10181080","DOIUrl":"https://doi.org/10.1109/ICBSII58188.2023.10181080","url":null,"abstract":"The mandated project is concerned with coming up with a sophisticated and cutting-edge way for average people to assess their health without a doctor’s or lab technician’s assistance. They can use the suggested equipment to independently monitor themselves. Sensors like the LM35 temperature sensor and blood pressure sensor are used in health monitoring systems. The technology has been modified for a wireless emergency telemedicine system, and in this prototype, it aids the blinded by converting text into speech so they can hear the output. Remote health monitoring raises the standard of care, cuts down on attention, and gives patients more control. The system includes an Arduino, data cable, non-invasive glucometer, digital sphygmomanometer, pulse oximeter sensor, heartbeat sensor, A/D device, signal learning circuit, and mobile. It is a reasonably priced, detachable device. Blood pressure, oxygen saturation, temperature, pulse rate, glucose level, and voice communication will all be displayed as a result of this gadget. In this project, an Arduino Uno is used to demonstrate IoT based e - health monitoring system. The system tracks the patient’s critical health metrics and broadcasts the observed data on a specific IP address (WiFi). To demonstrate the effectiveness of the suggested system, a prototype has been created. In order to avert problems and provide care for patients at the appropriate moment, the current research enables people periodically evaluate their health metrics.","PeriodicalId":388866,"journal":{"name":"2023 International Conference on Bio Signals, Images, and Instrumentation (ICBSII)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124855857","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 : 2023-03-16DOI: 10.1109/ICBSII58188.2023.10181085
D. V. Pravin, A. J. Ragavkumar, S. Abinesh, G. Kavitha
Muscle fatigue is a condition where a muscle or group of muscles lose their ability to contract and generate force. This can happen due to a variety of factors, including prolonged physical activity, lack of oxygen, and depletion of energy stores in the muscle. The raw sEMG signal is extracted by means of gel electrode attached to biceps of right arm. The preprocessing method used in the work involves different order of filters to process the raw signal. Further, the filtered signal is also amplified using instrumentation amplifier. The designed hardware extracts the signal at a frequency range between 56 Hz and 170 Hz. Six statistical features are extracted from the filtered signal in the time domain. The extracted features are given to various trained machine learning models using different algorithms such as Random Forest (RF), Support Vector Machine (SVM) and Logistic Regression (LR). The highest accuracy of about 87.5 % is achieved using random forest algorithm with the precision of 90%. The results that are obtained proves that machine learning methods can be used effectively to detect muscle fatigue from sEMG signals. The proposed method shows the propitious results in terms of accuracy and decisiveness. It can be used in areas such as sports training, rehabilitation, and ergonomics. This complete circuit is easy to produce and implement which could be used in the development of wearable and portable devices.
{"title":"Extraction, Processing and Analysis of Surface Electromyogram Signal and Detection of Muscle Fatigue Using Machine Learning Methods","authors":"D. V. Pravin, A. J. Ragavkumar, S. Abinesh, G. Kavitha","doi":"10.1109/ICBSII58188.2023.10181085","DOIUrl":"https://doi.org/10.1109/ICBSII58188.2023.10181085","url":null,"abstract":"Muscle fatigue is a condition where a muscle or group of muscles lose their ability to contract and generate force. This can happen due to a variety of factors, including prolonged physical activity, lack of oxygen, and depletion of energy stores in the muscle. The raw sEMG signal is extracted by means of gel electrode attached to biceps of right arm. The preprocessing method used in the work involves different order of filters to process the raw signal. Further, the filtered signal is also amplified using instrumentation amplifier. The designed hardware extracts the signal at a frequency range between 56 Hz and 170 Hz. Six statistical features are extracted from the filtered signal in the time domain. The extracted features are given to various trained machine learning models using different algorithms such as Random Forest (RF), Support Vector Machine (SVM) and Logistic Regression (LR). The highest accuracy of about 87.5 % is achieved using random forest algorithm with the precision of 90%. The results that are obtained proves that machine learning methods can be used effectively to detect muscle fatigue from sEMG signals. The proposed method shows the propitious results in terms of accuracy and decisiveness. It can be used in areas such as sports training, rehabilitation, and ergonomics. This complete circuit is easy to produce and implement which could be used in the development of wearable and portable devices.","PeriodicalId":388866,"journal":{"name":"2023 International Conference on Bio Signals, Images, and Instrumentation (ICBSII)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121708292","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 : 2023-03-16DOI: 10.1109/icacea.2015.7164862
{"title":"Track Schedule","authors":"","doi":"10.1109/icacea.2015.7164862","DOIUrl":"https://doi.org/10.1109/icacea.2015.7164862","url":null,"abstract":"","PeriodicalId":388866,"journal":{"name":"2023 International Conference on Bio Signals, Images, and Instrumentation (ICBSII)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126857531","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 : 2023-03-16DOI: 10.1109/ICBSII58188.2023.10181062
S. Rubin Bose, K. Varun Sharma, V. Karrthik Kishore, S. Tharunraj, G. Nikhil Srinivas, Regin R
The health of children and women are highly affected due to conflicts or war. The effects of war create terrible emotional consequences and physical consequences. The well-being and development of nation are also ensured by an intelligent defense system. Threats faced by tanks and other armored vehicles on the battlefield are getting more complicated. The proposed vision based active protection system installed in the tank is capable of recognizing the hostile targets precisely and destroy targets in the air before entering into the territory. This real-time Active Protection System can save the life of the civilians during warfare. The proposed model integrates a vision-based image processing technique with ultrasonic sensor for the real-time active protection system. The model utilizes lightweight deep CNN model (YOLOv5s architecture) on a Raspberry-Pi1 processor to recognize the hostile targets. Then, the predicted data is transferred from Raspberry-Pi1 processor to the cloud. Raspberry-Pi2 processor receives the information from the cloud and controls the missile operation of the tank in real-time. The Raspberry Pi processor is a low-power computing device, and YOLOv5s is familiar for its light weight and timely recognition. The proposed YOLOv5s model obtained an Average Precision of 93.10%, Average Recall of 89.50%, and F1-score of 91.26%. The Prediction time of the model is 4.1ms on Google Colab and 405ms on Raspberry-Pi processor.
{"title":"Vision Based Real-Time Active Protection System Using Deep Convolutional Neural Network","authors":"S. Rubin Bose, K. Varun Sharma, V. Karrthik Kishore, S. Tharunraj, G. Nikhil Srinivas, Regin R","doi":"10.1109/ICBSII58188.2023.10181062","DOIUrl":"https://doi.org/10.1109/ICBSII58188.2023.10181062","url":null,"abstract":"The health of children and women are highly affected due to conflicts or war. The effects of war create terrible emotional consequences and physical consequences. The well-being and development of nation are also ensured by an intelligent defense system. Threats faced by tanks and other armored vehicles on the battlefield are getting more complicated. The proposed vision based active protection system installed in the tank is capable of recognizing the hostile targets precisely and destroy targets in the air before entering into the territory. This real-time Active Protection System can save the life of the civilians during warfare. The proposed model integrates a vision-based image processing technique with ultrasonic sensor for the real-time active protection system. The model utilizes lightweight deep CNN model (YOLOv5s architecture) on a Raspberry-Pi1 processor to recognize the hostile targets. Then, the predicted data is transferred from Raspberry-Pi1 processor to the cloud. Raspberry-Pi2 processor receives the information from the cloud and controls the missile operation of the tank in real-time. The Raspberry Pi processor is a low-power computing device, and YOLOv5s is familiar for its light weight and timely recognition. The proposed YOLOv5s model obtained an Average Precision of 93.10%, Average Recall of 89.50%, and F1-score of 91.26%. The Prediction time of the model is 4.1ms on Google Colab and 405ms on Raspberry-Pi processor.","PeriodicalId":388866,"journal":{"name":"2023 International Conference on Bio Signals, Images, and Instrumentation (ICBSII)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125531280","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 : 2023-03-16DOI: 10.1109/ICBSII58188.2023.10181082
Ramakrishnan Maharajan
In this work, a blood flow-based method has been proposed to estimate beat to beat blood pressure parameters from the photoplethysmography (PPG) signal from a single PPG sensor. PPG signal represents the changes in the blood volume. The first derivative of it reflects the blood flow rate. In this work, the features are extracted from the blood flow rate reflected by the first derivative of PPG signal. The proposed method has been validated using Clinical data available in MIMIC II database. The validation shows that the systolic blood pressure and diastolic blood pressure estimated from a single site PPG signal has the mean error ± SD as 0.95 ± 5.14 mmHg for the beat-to-beat Pulse Pressure (PP) and 0.402 ± 4.85 mmHg for beat-to-beat Systolic Blood Pressure (SBP).
{"title":"Cuffless BP Measurement Using Single Site Photoplethysmography","authors":"Ramakrishnan Maharajan","doi":"10.1109/ICBSII58188.2023.10181082","DOIUrl":"https://doi.org/10.1109/ICBSII58188.2023.10181082","url":null,"abstract":"In this work, a blood flow-based method has been proposed to estimate beat to beat blood pressure parameters from the photoplethysmography (PPG) signal from a single PPG sensor. PPG signal represents the changes in the blood volume. The first derivative of it reflects the blood flow rate. In this work, the features are extracted from the blood flow rate reflected by the first derivative of PPG signal. The proposed method has been validated using Clinical data available in MIMIC II database. The validation shows that the systolic blood pressure and diastolic blood pressure estimated from a single site PPG signal has the mean error ± SD as 0.95 ± 5.14 mmHg for the beat-to-beat Pulse Pressure (PP) and 0.402 ± 4.85 mmHg for beat-to-beat Systolic Blood Pressure (SBP).","PeriodicalId":388866,"journal":{"name":"2023 International Conference on Bio Signals, Images, and Instrumentation (ICBSII)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133866380","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 : 2023-03-16DOI: 10.1109/ICBSII58188.2023.10181095
N. Keerthika., E. Sathish, V. Kiruthika, M. Santhakumar
Reaction Time (RT) is crucial for detecting cognitive abilities in sports and clinical applications. RT Measurements can be used to evaluate the performance and sensory-motor integration of individuals. It determines a person’s attentiveness because RT indicates how rapidly an individual reacts toward a stimulus. A novel experimental setup called Automatic Reaction Time Tester (ARTT) system is proposed in this study to measure the RT using Audio Stimulus (AS), Visual Stimulus (VS), and Muscular Reaction Time (MRT). The ARTT system helps in reducing human intervention and time consumption. It improves accuracy and makes it easier to test the RT in terms of AS, VS, and MRT in a single system. In the sports field, coaches are able to analyze the current condition of the players and modified their training sessions accordingly, Moreover, the individual player can also check their performance through self-diagnosis methods for improving their performance. In the medical field, it assists clinicians in determining a patient’s response to medication and facilitates a speedy recovery through this RT test.
{"title":"A novel design for automatic measurement of reaction time for audiovisual and muscular stimulus","authors":"N. Keerthika., E. Sathish, V. Kiruthika, M. Santhakumar","doi":"10.1109/ICBSII58188.2023.10181095","DOIUrl":"https://doi.org/10.1109/ICBSII58188.2023.10181095","url":null,"abstract":"Reaction Time (RT) is crucial for detecting cognitive abilities in sports and clinical applications. RT Measurements can be used to evaluate the performance and sensory-motor integration of individuals. It determines a person’s attentiveness because RT indicates how rapidly an individual reacts toward a stimulus. A novel experimental setup called Automatic Reaction Time Tester (ARTT) system is proposed in this study to measure the RT using Audio Stimulus (AS), Visual Stimulus (VS), and Muscular Reaction Time (MRT). The ARTT system helps in reducing human intervention and time consumption. It improves accuracy and makes it easier to test the RT in terms of AS, VS, and MRT in a single system. In the sports field, coaches are able to analyze the current condition of the players and modified their training sessions accordingly, Moreover, the individual player can also check their performance through self-diagnosis methods for improving their performance. In the medical field, it assists clinicians in determining a patient’s response to medication and facilitates a speedy recovery through this RT test.","PeriodicalId":388866,"journal":{"name":"2023 International Conference on Bio Signals, Images, and Instrumentation (ICBSII)","volume":"189 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132118577","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 : 2023-03-16DOI: 10.1109/ICBSII58188.2023.10181074
B. Banu Rekha, S. Charushree, P. Divyadharshan, K. Harish Babu, V. Vasunthra
3D Virtualisation refers to the process of creation of graphical contents using 3D softwares. These include images and animations that improve the communication and provide the users with a more realistic online experience. It is also used to demonstrate either a prototype or a finished product to the stakeholders. In the present, the customers and learners prefer viewing and learning the virtual model of the product in order to efficiently understand the concept, theory and principle behind it. In this case, 3D virtualization provides an excellent opportunity for customers and learners to see the layout of the product design, texture and working model as animation.
{"title":"A Novel Approach in Web Based 3D Virtualization For Healthcare","authors":"B. Banu Rekha, S. Charushree, P. Divyadharshan, K. Harish Babu, V. Vasunthra","doi":"10.1109/ICBSII58188.2023.10181074","DOIUrl":"https://doi.org/10.1109/ICBSII58188.2023.10181074","url":null,"abstract":"3D Virtualisation refers to the process of creation of graphical contents using 3D softwares. These include images and animations that improve the communication and provide the users with a more realistic online experience. It is also used to demonstrate either a prototype or a finished product to the stakeholders. In the present, the customers and learners prefer viewing and learning the virtual model of the product in order to efficiently understand the concept, theory and principle behind it. In this case, 3D virtualization provides an excellent opportunity for customers and learners to see the layout of the product design, texture and working model as animation.","PeriodicalId":388866,"journal":{"name":"2023 International Conference on Bio Signals, Images, and Instrumentation (ICBSII)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115428168","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 : 2023-03-16DOI: 10.1109/ICBSII58188.2023.10181071
P. Suveetha Dhanaselvam, C. Nadia Chellam
Electroencephalogram (EEG) is the documentation of brain’s electrical activity tapped from the scalp. The signals picked from the scalp do not express an accurate representation of the brain signals. These bio-signals need to be processed in order to be used for the desired application. To unravel this problem, there is a necessity to define a strong and repeatable EEG pre-processing method. EEG data pre-processing specifies a procedure of remodeling the raw EEG data into a clean EEG data by removing the undesirable noise and artifacts thereby converting it into suitable format for further analysis and interpretable by the user. This paper tends to review various EEG preprocessing techniques that has been described within the published literatures so as to focus on the acceptable preprocessing modality for a specific application.
{"title":"A Review on Preprocessing of EEG Signal","authors":"P. Suveetha Dhanaselvam, C. Nadia Chellam","doi":"10.1109/ICBSII58188.2023.10181071","DOIUrl":"https://doi.org/10.1109/ICBSII58188.2023.10181071","url":null,"abstract":"Electroencephalogram (EEG) is the documentation of brain’s electrical activity tapped from the scalp. The signals picked from the scalp do not express an accurate representation of the brain signals. These bio-signals need to be processed in order to be used for the desired application. To unravel this problem, there is a necessity to define a strong and repeatable EEG pre-processing method. EEG data pre-processing specifies a procedure of remodeling the raw EEG data into a clean EEG data by removing the undesirable noise and artifacts thereby converting it into suitable format for further analysis and interpretable by the user. This paper tends to review various EEG preprocessing techniques that has been described within the published literatures so as to focus on the acceptable preprocessing modality for a specific application.","PeriodicalId":388866,"journal":{"name":"2023 International Conference on Bio Signals, Images, and Instrumentation (ICBSII)","volume":"175 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128634107","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 : 2023-03-16DOI: 10.1109/ICBSII58188.2023.10181053
G. Umashankar, G. Krishnan, T. Sudhakar, G. Mohandass, T. Devaraju, V. Devika, S. Shaina Banu
Devices are locked and unlocked using biometric security based on eye blinks rather than PINs. On the other hand, using devices in uncontrolled situations makes you extremely vulnerable to spoofing by replay assaults. Stationary facial expression authentication is an instance of a biometric; hackers use the facial images of fictitious individuals to activate devices, leading to unprotected activities and the unintentional disclosure of personal data. In place of static face authentication, we addressed a biometric security system that tracks a person’s eye blinking motions as well as their face. The suggested system gets a stream of images as information by instructing the user to follow an eye-blink pattern. The developed scheme then confirms the individual’s identity using the proper eye-blink pattern.
{"title":"Eye Blink Based Biometric Authentication System","authors":"G. Umashankar, G. Krishnan, T. Sudhakar, G. Mohandass, T. Devaraju, V. Devika, S. Shaina Banu","doi":"10.1109/ICBSII58188.2023.10181053","DOIUrl":"https://doi.org/10.1109/ICBSII58188.2023.10181053","url":null,"abstract":"Devices are locked and unlocked using biometric security based on eye blinks rather than PINs. On the other hand, using devices in uncontrolled situations makes you extremely vulnerable to spoofing by replay assaults. Stationary facial expression authentication is an instance of a biometric; hackers use the facial images of fictitious individuals to activate devices, leading to unprotected activities and the unintentional disclosure of personal data. In place of static face authentication, we addressed a biometric security system that tracks a person’s eye blinking motions as well as their face. The suggested system gets a stream of images as information by instructing the user to follow an eye-blink pattern. The developed scheme then confirms the individual’s identity using the proper eye-blink pattern.","PeriodicalId":388866,"journal":{"name":"2023 International Conference on Bio Signals, Images, and Instrumentation (ICBSII)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129644786","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：