Pub Date : 2012-12-14DOI: 10.1109/BIOMEDCOM.2012.17
L. Pullum, O. Ozmen
The research reported in this paper is motivated by the need to validate models of disease spread. To date, reasoned confidence in the results of these models is complicated by the increasing complexity of the models and by their desired predictive use. As part of an overall approach to verification and validation of disease spread models, we investigate metamorphic testing. In this paper, we present early results of initial metamorphic testing on agent-based epidemiological models.
{"title":"Early Results from Metamorphic Testing of Epidemiological Models","authors":"L. Pullum, O. Ozmen","doi":"10.1109/BIOMEDCOM.2012.17","DOIUrl":"https://doi.org/10.1109/BIOMEDCOM.2012.17","url":null,"abstract":"The research reported in this paper is motivated by the need to validate models of disease spread. To date, reasoned confidence in the results of these models is complicated by the increasing complexity of the models and by their desired predictive use. As part of an overall approach to verification and validation of disease spread models, we investigate metamorphic testing. In this paper, we present early results of initial metamorphic testing on agent-based epidemiological models.","PeriodicalId":146495,"journal":{"name":"2012 ASE/IEEE International Conference on BioMedical Computing (BioMedCom)","volume":"154 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126751660","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 : 2012-12-14DOI: 10.1109/BIOMEDCOM.2012.7
M. Ertas, A. Akan, K. Cengiz, M. Kamasak, S. Seyyedi, I. Yildirim
In tomosynthesis imaging, out-of-focus slice blur problem arises due to incomplete sampling problem. Several approaches have been proposed to deal with this problem. Algebraic reconstruction technique (ART) is one of the most commonly used methods. Total variation (TV) minimization has recently been applied to improve performance of the classical approaches. Though it is able to provide improved results, its sensitivity to the regularization parameter is still an important issue. Former studies addressed largely 2-D tomosynthesis image reconstruction problem. In this study, a 3-D phantom model was used to understand the effect of total variation minimization on a 3-D image reconstruction problem. The significance of selecting an appropriate regularization parameter of TV not addressed in the prior studies was also investigated by means of comparing root mean square error (RMSE) and contrast to noise ratio (CNR) values.
{"title":"3-D Tomosynthesis Image Reconstruction Using Total Variation","authors":"M. Ertas, A. Akan, K. Cengiz, M. Kamasak, S. Seyyedi, I. Yildirim","doi":"10.1109/BIOMEDCOM.2012.7","DOIUrl":"https://doi.org/10.1109/BIOMEDCOM.2012.7","url":null,"abstract":"In tomosynthesis imaging, out-of-focus slice blur problem arises due to incomplete sampling problem. Several approaches have been proposed to deal with this problem. Algebraic reconstruction technique (ART) is one of the most commonly used methods. Total variation (TV) minimization has recently been applied to improve performance of the classical approaches. Though it is able to provide improved results, its sensitivity to the regularization parameter is still an important issue. Former studies addressed largely 2-D tomosynthesis image reconstruction problem. In this study, a 3-D phantom model was used to understand the effect of total variation minimization on a 3-D image reconstruction problem. The significance of selecting an appropriate regularization parameter of TV not addressed in the prior studies was also investigated by means of comparing root mean square error (RMSE) and contrast to noise ratio (CNR) values.","PeriodicalId":146495,"journal":{"name":"2012 ASE/IEEE International Conference on BioMedical Computing (BioMedCom)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131286903","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 : 2012-12-14DOI: 10.1109/BIOMEDCOM.2012.30
A. Fuchs, C. Rudolph
Security by Design and Secure Engineering are among the most pressing challenges in IT Security research and practice. Increased attacker potential and dependence on IT-Systems in economy and in critical infrastructures cause a higher demand in securely engineered systems and thus in new approaches and methodologies. This paper introduces a consistent methodology for designing secure systems during the specification phase. The Security Modeling Framework SeMF serves as basis for its security vocabulary. We extend SeMF by the concept of SeMF Building Blocks SeBBs as reasoning tool and provide a security design process utilizing them as refinement artifacts. This process guides the decision making during the system specification phase focused on the security aspects and integrates with refinement driven functional engineering processes. Our approach further results in a security design documentation and residual assumptions that can serve as a basis for risk assessment, code review, and organizational security means during deployment.
{"title":"Security Engineering Based on Structured Formal Reasoning","authors":"A. Fuchs, C. Rudolph","doi":"10.1109/BIOMEDCOM.2012.30","DOIUrl":"https://doi.org/10.1109/BIOMEDCOM.2012.30","url":null,"abstract":"Security by Design and Secure Engineering are among the most pressing challenges in IT Security research and practice. Increased attacker potential and dependence on IT-Systems in economy and in critical infrastructures cause a higher demand in securely engineered systems and thus in new approaches and methodologies. This paper introduces a consistent methodology for designing secure systems during the specification phase. The Security Modeling Framework SeMF serves as basis for its security vocabulary. We extend SeMF by the concept of SeMF Building Blocks SeBBs as reasoning tool and provide a security design process utilizing them as refinement artifacts. This process guides the decision making during the system specification phase focused on the security aspects and integrates with refinement driven functional engineering processes. Our approach further results in a security design documentation and residual assumptions that can serve as a basis for risk assessment, code review, and organizational security means during deployment.","PeriodicalId":146495,"journal":{"name":"2012 ASE/IEEE International Conference on BioMedical Computing (BioMedCom)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129425816","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 : 2012-12-14DOI: 10.1109/BIOMEDCOM.2012.12
Nick Leaf, K. Ma, Cui Zhang, G. Galli
Existing molecular visualizations do a good job of illuminating well-defined structures, but are ill-suited to visualizing subtle organization when the data is almost entirely disordered. We introduce a process to explore these subtle structures by visualizing an abstraction of the data. The process includes a quantitative verification step to mitigate the ambiguity induced by abstraction. We perform a case study using molecular dynamics simulation data of bulk water to demonstrate the efficacy of our process. The visualized abstractions show molecular behaviors which only happen amongst aggregates, and which would be occluded or indiscernible in a direct visualization. The effect is shown across multiple data sets with different temporal and thermal parameters. In conjunction with domain experts from the Chemistry department who performed the simulation, we confirm our observations using the quantitative verification step of our process.
{"title":"A Process for Visualizing Disordered Molecular Data with a Case Study in Bulk Water","authors":"Nick Leaf, K. Ma, Cui Zhang, G. Galli","doi":"10.1109/BIOMEDCOM.2012.12","DOIUrl":"https://doi.org/10.1109/BIOMEDCOM.2012.12","url":null,"abstract":"Existing molecular visualizations do a good job of illuminating well-defined structures, but are ill-suited to visualizing subtle organization when the data is almost entirely disordered. We introduce a process to explore these subtle structures by visualizing an abstraction of the data. The process includes a quantitative verification step to mitigate the ambiguity induced by abstraction. We perform a case study using molecular dynamics simulation data of bulk water to demonstrate the efficacy of our process. The visualized abstractions show molecular behaviors which only happen amongst aggregates, and which would be occluded or indiscernible in a direct visualization. The effect is shown across multiple data sets with different temporal and thermal parameters. In conjunction with domain experts from the Chemistry department who performed the simulation, we confirm our observations using the quantitative verification step of our process.","PeriodicalId":146495,"journal":{"name":"2012 ASE/IEEE International Conference on BioMedical Computing (BioMedCom)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115262940","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 : 2012-12-14DOI: 10.1109/BIOMEDCOM.2012.13
Y. Goyal, A. Jain
Heart rate variability (HRV) provides a non-invasive means of quantifying cardiac autonomic activity. It has been shown to be a powerful predictor of arrhythmia related complications in patients surviving the acute phase of myocardial infarction. It has also increasingly been used to measure autonomic nervous system activities. This work aims to study heart rate variability during normal or abnormal functioning of the heart and whether it can be used to predict the occurrence of any abnormality. Additionally, it aims to compare results based on wavelet analysis and Pan Tompkins algorithm. Both time domain analysis and frequency domain analysis of HRV are presented. The HRV dynamics is evaluated using non-parametric (Fast Fourier Transform) method. Results of stimulations in MATLAB are presented.
{"title":"Study of HRV Dynamics and Comparison Using Wavelet Analysis and Pan Tompkins Algorithm","authors":"Y. Goyal, A. Jain","doi":"10.1109/BIOMEDCOM.2012.13","DOIUrl":"https://doi.org/10.1109/BIOMEDCOM.2012.13","url":null,"abstract":"Heart rate variability (HRV) provides a non-invasive means of quantifying cardiac autonomic activity. It has been shown to be a powerful predictor of arrhythmia related complications in patients surviving the acute phase of myocardial infarction. It has also increasingly been used to measure autonomic nervous system activities. This work aims to study heart rate variability during normal or abnormal functioning of the heart and whether it can be used to predict the occurrence of any abnormality. Additionally, it aims to compare results based on wavelet analysis and Pan Tompkins algorithm. Both time domain analysis and frequency domain analysis of HRV are presented. The HRV dynamics is evaluated using non-parametric (Fast Fourier Transform) method. Results of stimulations in MATLAB are presented.","PeriodicalId":146495,"journal":{"name":"2012 ASE/IEEE International Conference on BioMedical Computing (BioMedCom)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128818105","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 : 2012-12-14DOI: 10.1109/BIOMEDCOM.2012.20
S. K. Jha, A. Ramanathan
Modern epidemiology has made use of a number of mathematical models, including ordinary differential equation (ODE) based models and agent based models (ABMs) to describe the dynamics of how a disease may spread within a population and enable the rational design of strategies for intervention that effectively contain the spread of the disease. Although such predictions are of fundamental importance in preventing the next global pandemic, there is a significant gap in trusting the outcomes/predictions solely based on such models. Hence, there is a need to develop approaches such that mathematical models can be calibrated against historical data. In addition, there is a need to develop rigorous uncertainty quantification approaches that can provide insights into when a model will fail and characterize the confidence in the (possibly multiple) model outcomes/predictions, when such retrospective analysis cannot be performed. In this paper, we outline an approach to develop uncertainty quantification approaches for epidemiological models using formal methods and model checking. By specifying the outcomes expected from a model in a suitable spatio-temporal logic, we use probabilistic model checking methods to quantify the probability with which the epidemiological model satisfies a given behavioral specification. We argue that statistical model checking methods can solve the uncertainty quantification problem for complex epidemiological models.
{"title":"Quantifying Uncertainty in Epidemiological Models","authors":"S. K. Jha, A. Ramanathan","doi":"10.1109/BIOMEDCOM.2012.20","DOIUrl":"https://doi.org/10.1109/BIOMEDCOM.2012.20","url":null,"abstract":"Modern epidemiology has made use of a number of mathematical models, including ordinary differential equation (ODE) based models and agent based models (ABMs) to describe the dynamics of how a disease may spread within a population and enable the rational design of strategies for intervention that effectively contain the spread of the disease. Although such predictions are of fundamental importance in preventing the next global pandemic, there is a significant gap in trusting the outcomes/predictions solely based on such models. Hence, there is a need to develop approaches such that mathematical models can be calibrated against historical data. In addition, there is a need to develop rigorous uncertainty quantification approaches that can provide insights into when a model will fail and characterize the confidence in the (possibly multiple) model outcomes/predictions, when such retrospective analysis cannot be performed. In this paper, we outline an approach to develop uncertainty quantification approaches for epidemiological models using formal methods and model checking. By specifying the outcomes expected from a model in a suitable spatio-temporal logic, we use probabilistic model checking methods to quantify the probability with which the epidemiological model satisfies a given behavioral specification. We argue that statistical model checking methods can solve the uncertainty quantification problem for complex epidemiological models.","PeriodicalId":146495,"journal":{"name":"2012 ASE/IEEE International Conference on BioMedical Computing (BioMedCom)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129989011","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 : 2012-12-14DOI: 10.1109/BIOMEDCOM.2012.19
S. Sukumar, J. Nutaro
This paper is motivated by the need to design model validation strategies for epidemiological disease-spread models. We consider both agent-based and equation-based models of pandemic disease spread and study the nuances and complexities one has to consider from the perspective of model validation. For this purpose, we instantiate an equation-based model and an agent-based model of the 1918 Spanish flu and we leverage data published in the literature for our case study. We present our observations from the perspective of each implementation and discuss the application of model-selection criteria to compare the risk in choosing one modeling paradigm to another. We conclude with a discussion of our experience and document future ideas for a model validation framework.
{"title":"Agent-Based vs. Equation-Based Epidemiological Models: A Model Selection Case Study","authors":"S. Sukumar, J. Nutaro","doi":"10.1109/BIOMEDCOM.2012.19","DOIUrl":"https://doi.org/10.1109/BIOMEDCOM.2012.19","url":null,"abstract":"This paper is motivated by the need to design model validation strategies for epidemiological disease-spread models. We consider both agent-based and equation-based models of pandemic disease spread and study the nuances and complexities one has to consider from the perspective of model validation. For this purpose, we instantiate an equation-based model and an agent-based model of the 1918 Spanish flu and we leverage data published in the literature for our case study. We present our observations from the perspective of each implementation and discuss the application of model-selection criteria to compare the risk in choosing one modeling paradigm to another. We conclude with a discussion of our experience and document future ideas for a model validation framework.","PeriodicalId":146495,"journal":{"name":"2012 ASE/IEEE International Conference on BioMedical Computing (BioMedCom)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115563157","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 : 2012-12-14DOI: 10.1109/BIOMEDCOM.2012.23
W. Kim, C. S. Moon, S. Chung, T. Escrig, B. Endicott-Popovsky
Developing and maintaining security software systems can be cost-prohibitive for all but the largest organizations. New requirements of hardware and software quickly outdate current security systems. Modern software architecture technology, such as Spring Framework, and the new deployment paradigm of cloud computing infrastructure services can help with the challenge of maintaining software security systems. Spring Framework allows for better reusability of our code to adapt to different input vulnerabilities and cloud computing helps to deploy quickly and easily. The application of software architectural patterns and cloud computing to the development of attack aware software improves reusability and scalability.
{"title":"Scalable and Reusable Attack Aware Software","authors":"W. Kim, C. S. Moon, S. Chung, T. Escrig, B. Endicott-Popovsky","doi":"10.1109/BIOMEDCOM.2012.23","DOIUrl":"https://doi.org/10.1109/BIOMEDCOM.2012.23","url":null,"abstract":"Developing and maintaining security software systems can be cost-prohibitive for all but the largest organizations. New requirements of hardware and software quickly outdate current security systems. Modern software architecture technology, such as Spring Framework, and the new deployment paradigm of cloud computing infrastructure services can help with the challenge of maintaining software security systems. Spring Framework allows for better reusability of our code to adapt to different input vulnerabilities and cloud computing helps to deploy quickly and easily. The application of software architectural patterns and cloud computing to the development of attack aware software improves reusability and scalability.","PeriodicalId":146495,"journal":{"name":"2012 ASE/IEEE International Conference on BioMedical Computing (BioMedCom)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130956949","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 : 2012-12-14DOI: 10.1109/BIOMEDCOM.2012.18
A. Ramanathan, C. Steed, L. Pullum
Compartmental models in epidemiology are widely used as a means to model disease spread mechanisms and understand how one can best control the disease in case an outbreak of a widespread epidemic occurs. However, a significant challenge within the community is in the development of approaches that can be used to rigorously verify and validate these models. In this paper, we present an approach to quantify and verify the behavioral properties of compartmental epidemiological models under several common modeling scenarios including: birth/death rates and multi-host/pathogen species. We build a workflow that uses metamorphic testing, novel visualization tools and model checking to gain insights into the functionality of compartmental epidemiological models. Our initial results indicate that metamorphic testing can be used to verify the implementation of these models and provide insights into special conditions where these mathematical models may fail. The visualization front-end allows the end-user to scan through a variety of parameters commonly used in these models to elucidate the conditions under which an epidemic can occur. Furthermore, specifying these models using a process algebra allows one to automatically construct behavioral properties that can be rigorously verified using model checking. Together, our approach allows for detecting implementation errors as well as handling conditions under which compartmental epidemiological models may fail to provide insights into disease spread dynamics.
{"title":"Verification of Compartmental Epidemiological Models Using Metamorphic Testing, Model Checking and Visual Analytics","authors":"A. Ramanathan, C. Steed, L. Pullum","doi":"10.1109/BIOMEDCOM.2012.18","DOIUrl":"https://doi.org/10.1109/BIOMEDCOM.2012.18","url":null,"abstract":"Compartmental models in epidemiology are widely used as a means to model disease spread mechanisms and understand how one can best control the disease in case an outbreak of a widespread epidemic occurs. However, a significant challenge within the community is in the development of approaches that can be used to rigorously verify and validate these models. In this paper, we present an approach to quantify and verify the behavioral properties of compartmental epidemiological models under several common modeling scenarios including: birth/death rates and multi-host/pathogen species. We build a workflow that uses metamorphic testing, novel visualization tools and model checking to gain insights into the functionality of compartmental epidemiological models. Our initial results indicate that metamorphic testing can be used to verify the implementation of these models and provide insights into special conditions where these mathematical models may fail. The visualization front-end allows the end-user to scan through a variety of parameters commonly used in these models to elucidate the conditions under which an epidemic can occur. Furthermore, specifying these models using a process algebra allows one to automatically construct behavioral properties that can be rigorously verified using model checking. Together, our approach allows for detecting implementation errors as well as handling conditions under which compartmental epidemiological models may fail to provide insights into disease spread dynamics.","PeriodicalId":146495,"journal":{"name":"2012 ASE/IEEE International Conference on BioMedical Computing (BioMedCom)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130961030","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 : 2012-12-14DOI: 10.1109/BIOMEDCOM.2012.14
T. Koshio, S. Sakurazawa, M. Toda, J. Akita, K. Kondo, Y. Nakamura
It is generally difficult to identify surface and deep layer muscle activity. If we could identify surface and deep layer muscle activity, we can provide effective tools for rehabilitation. Therefore, in this study, we aim to identify surface and deep layer muscle activity using surface electrodes. We focused on the intersection of muscle fibers of surface layer muscle and deep layer muscle. On such a place, we found that we could measure independent muscle activity along each muscle fiber. In this result, we could confirm that we could measure surface layer muscle activity independently by comparing with the signal which is measured on extended line of surface muscle fiber and on outside of the deep layer muscle. Additionally, we identified propagation direction of EMG signal using electrode array. It was determined from delay time of EMG signal measured on each electrode. When we activated surface and deep layer muscle independently, each propagation direction of EMG signal corresponded to each muscle fiber direction. Thus, we indicated that identification of surface and deep layer muscle activity is possible.
{"title":"Identification of Surface and Deep Layer Muscle Activity by EMG Propagation Direction","authors":"T. Koshio, S. Sakurazawa, M. Toda, J. Akita, K. Kondo, Y. Nakamura","doi":"10.1109/BIOMEDCOM.2012.14","DOIUrl":"https://doi.org/10.1109/BIOMEDCOM.2012.14","url":null,"abstract":"It is generally difficult to identify surface and deep layer muscle activity. If we could identify surface and deep layer muscle activity, we can provide effective tools for rehabilitation. Therefore, in this study, we aim to identify surface and deep layer muscle activity using surface electrodes. We focused on the intersection of muscle fibers of surface layer muscle and deep layer muscle. On such a place, we found that we could measure independent muscle activity along each muscle fiber. In this result, we could confirm that we could measure surface layer muscle activity independently by comparing with the signal which is measured on extended line of surface muscle fiber and on outside of the deep layer muscle. Additionally, we identified propagation direction of EMG signal using electrode array. It was determined from delay time of EMG signal measured on each electrode. When we activated surface and deep layer muscle independently, each propagation direction of EMG signal corresponded to each muscle fiber direction. Thus, we indicated that identification of surface and deep layer muscle activity is possible.","PeriodicalId":146495,"journal":{"name":"2012 ASE/IEEE International Conference on BioMedical Computing (BioMedCom)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122726622","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}
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