Pub Date : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635166
Vasileios Skaramagkas, Emmanouil Ktistakis, D. Manousos, N. Tachos, E. Kazantzaki, E. Tripoliti, D. Fotiadis, M. Tsiknakis
Cognitive workload is a critical feature in related psychology, ergonomics, and human factors for understanding performance. However, it still is difficult to describe and thus, to measure it. Since there is no single sensor that can give a full understanding of workload, extended research has been conducted in order to present robust biomarkers. During the last years, machine learning techniques have been used to predict cognitive workload based on various features. Gaze extracted features, such as pupil size, blink activity and saccadic measures, have been used as predictors. The aim of this study is to use gaze extracted features as the only predictors of cognitive workload. Two factors were investigated: time pressure and multi tasking. The findings of this study showed that eye and gaze features are useful indicators of cognitive workload levels, reaching up to 88% accuracy.
{"title":"Cognitive workload level estimation based on eye tracking: A machine learning approach","authors":"Vasileios Skaramagkas, Emmanouil Ktistakis, D. Manousos, N. Tachos, E. Kazantzaki, E. Tripoliti, D. Fotiadis, M. Tsiknakis","doi":"10.1109/BIBE52308.2021.9635166","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635166","url":null,"abstract":"Cognitive workload is a critical feature in related psychology, ergonomics, and human factors for understanding performance. However, it still is difficult to describe and thus, to measure it. Since there is no single sensor that can give a full understanding of workload, extended research has been conducted in order to present robust biomarkers. During the last years, machine learning techniques have been used to predict cognitive workload based on various features. Gaze extracted features, such as pupil size, blink activity and saccadic measures, have been used as predictors. The aim of this study is to use gaze extracted features as the only predictors of cognitive workload. Two factors were investigated: time pressure and multi tasking. The findings of this study showed that eye and gaze features are useful indicators of cognitive workload levels, reaching up to 88% accuracy.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125259039","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 : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635486
Dimitrios Boucharas, Christos Androutsos, N. Tachos, E. Tripoliti, Dimitrios Manousos, Vasileios Skaramagkas, Emmanouil Ktistakis, M. Tsiknakis, D. Fotiadis
The aim of this work is to present two different algorithmic pipelines for human activity recognition (HAR) in real time, exploiting inertial measurement unit (IMU) sensors. Various learning classifiers have been developed and tested across different datasets. The experimental results provide a comparative performance analysis based on accuracy and latency during fine-tuning, training and prediction. The overall accuracy of the proposed pipeline reaches 66 % in the publicly available dataset and 90% in the in-house one.
{"title":"Exploring Artificial Intelligence methods for recognizing human activities in real time by exploiting inertial sensors","authors":"Dimitrios Boucharas, Christos Androutsos, N. Tachos, E. Tripoliti, Dimitrios Manousos, Vasileios Skaramagkas, Emmanouil Ktistakis, M. Tsiknakis, D. Fotiadis","doi":"10.1109/BIBE52308.2021.9635486","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635486","url":null,"abstract":"The aim of this work is to present two different algorithmic pipelines for human activity recognition (HAR) in real time, exploiting inertial measurement unit (IMU) sensors. Various learning classifiers have been developed and tested across different datasets. The experimental results provide a comparative performance analysis based on accuracy and latency during fine-tuning, training and prediction. The overall accuracy of the proposed pipeline reaches 66 % in the publicly available dataset and 90% in the in-house one.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133761623","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 : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635586
Thomas K. K. Kjeldsen, Thomas B. Nielsen, Hamzah Ziadeh, Steffen Lehmann, Louise D. Nielsen, Dávid Gulyás, B. I. Hougaard, H. Knoche, M. Jochumsen
Brain-computer interfaces (BCIs) provide users with a means to control external devices or applications using only voluntarily produced brain activity. Controlling a BCI through motor imagery is a skill that must be acquired, however, little evidence is available on how the user's agency and frustration are affected by different types of feedback during an interaction with a BCI. This was investigated during a virtual reality interaction where 14 naive participants controlled an avatar with a BCI while receiving either continuous or discrete feedback on their performance. The agency, frustration, ownership and BCI performance were assessed after each of the two conditions (continuous and discrete feedback). There was no statistical difference between the conditions although the participants generally rated agency higher for the continuous feedback which was also uncorrelated to the BCI performance. This suggests that continuous feedback can be useful for increasing agency for users with poor BCI performance by providing them with some knowledge of performance.
{"title":"Effect of Continuous and Discrete Feedback on Agency and Frustration in a Brain-Computer Interface Virtual Reality Interaction","authors":"Thomas K. K. Kjeldsen, Thomas B. Nielsen, Hamzah Ziadeh, Steffen Lehmann, Louise D. Nielsen, Dávid Gulyás, B. I. Hougaard, H. Knoche, M. Jochumsen","doi":"10.1109/BIBE52308.2021.9635586","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635586","url":null,"abstract":"Brain-computer interfaces (BCIs) provide users with a means to control external devices or applications using only voluntarily produced brain activity. Controlling a BCI through motor imagery is a skill that must be acquired, however, little evidence is available on how the user's agency and frustration are affected by different types of feedback during an interaction with a BCI. This was investigated during a virtual reality interaction where 14 naive participants controlled an avatar with a BCI while receiving either continuous or discrete feedback on their performance. The agency, frustration, ownership and BCI performance were assessed after each of the two conditions (continuous and discrete feedback). There was no statistical difference between the conditions although the participants generally rated agency higher for the continuous feedback which was also uncorrelated to the BCI performance. This suggests that continuous feedback can be useful for increasing agency for users with poor BCI performance by providing them with some knowledge of performance.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129373133","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 : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635318
T. Djukić, N. Filipovic
It is important to consider the blood flow pattern when planning vascular interventions of atherosclerotic plaques. Large scale computer modeling can be very helpful in this case. The software presented in this paper numerically models blood flow through patient-specific blood vessels. Lattice Boltzmann method was used to simulate blood flow. The principles of GPU (Graphics Processing Unit) programming are applied during implementation and the developed software was parallelized using the CUDA (Compute Unified Device Architecture) and optimized to run on a multi-GPU cluster using the MPI approach. Using the multi-GPU infrastructure, numerical simulations can utilize larger amount of memory resources for the computation, making the level of reality of the models an order of magnitude higher. Execution of the presented software enables fast and reliable case-studies and parametric analyses useful for medical decision-making. The presented software can give medical professionals fast quantitative information about fluid flow in diseased arteries and assist them in selecting the most appropriate treatment.
{"title":"Parallelization of lattice Boltzmann software for execution on multi-GPU clusters with application to the simulation of blood flow through human arteries","authors":"T. Djukić, N. Filipovic","doi":"10.1109/BIBE52308.2021.9635318","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635318","url":null,"abstract":"It is important to consider the blood flow pattern when planning vascular interventions of atherosclerotic plaques. Large scale computer modeling can be very helpful in this case. The software presented in this paper numerically models blood flow through patient-specific blood vessels. Lattice Boltzmann method was used to simulate blood flow. The principles of GPU (Graphics Processing Unit) programming are applied during implementation and the developed software was parallelized using the CUDA (Compute Unified Device Architecture) and optimized to run on a multi-GPU cluster using the MPI approach. Using the multi-GPU infrastructure, numerical simulations can utilize larger amount of memory resources for the computation, making the level of reality of the models an order of magnitude higher. Execution of the presented software enables fast and reliable case-studies and parametric analyses useful for medical decision-making. The presented software can give medical professionals fast quantitative information about fluid flow in diseased arteries and assist them in selecting the most appropriate treatment.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126962766","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 : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635217
Jelisaveta Ignjatović, T. Šušteršič, S. Cvijić, Aleksandar Bodić, Jelena Duris, S. Ibrić, N. Filipovic
Emitted fraction (EF) is one of the critical quality attributes of dry powders for inhalation (DPIs). Traditionally, different in vitro methods have been used for the assessment of DPIs EF. However, the evolution in computer-based (in silico) methods led to the development of special fields, such as Computational Fluid Dynamics (CFD) coupled with fluid-particle dynamics models e.g., Discrete Phase Model (DPM) as a useful alternative for the assessment of DPIs aerodynamic performance. The aim of this study was to design a CFD-DPM model for the prediction of model DPIs EF, and assess the prediction power of this method by comparing the in silico prediction results with in vitro determined EF values, obtained by three different methods. The EFs of the solid lipid microparticles (SLM) DPIs were determined in vitro by Twin stage impinger, Next generation impactor and Fast Screening Impactor. CFD-DPM model was successfully designed, and then the simulation results indicated the percentage of particles that remained in the inhaler. Based on those data, DPI EFs were calculated to be in the range of 83 % and 92%, indicating that CFD-DPM simulations were able to catch the differences between five SLM DPI formulations. In addition, CFD-DPM predicted the regional particle deposition in the inhaler, which cannot be precisely determined based on in vitro experiments. CFD-DPM predicted EF values were generally comparable to the EF values obtained by three in vitro methods, although some differences were observed between in vitro and in silico values. Therefore, it can be concluded that although additional improvements of CFD-DPM designed model are still necessary in order to be able to precisely describe aerodynamic performance of SLM DPIs, CFD-DPM modeling can be considered as a very useful tool in DPIs development.
{"title":"Comparative Assessment of Computational vs. In Vitro Methods for the Estimation of Dry Powders for Inhalation Emitted Fraction","authors":"Jelisaveta Ignjatović, T. Šušteršič, S. Cvijić, Aleksandar Bodić, Jelena Duris, S. Ibrić, N. Filipovic","doi":"10.1109/BIBE52308.2021.9635217","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635217","url":null,"abstract":"Emitted fraction (EF) is one of the critical quality attributes of dry powders for inhalation (DPIs). Traditionally, different in vitro methods have been used for the assessment of DPIs EF. However, the evolution in computer-based (in silico) methods led to the development of special fields, such as Computational Fluid Dynamics (CFD) coupled with fluid-particle dynamics models e.g., Discrete Phase Model (DPM) as a useful alternative for the assessment of DPIs aerodynamic performance. The aim of this study was to design a CFD-DPM model for the prediction of model DPIs EF, and assess the prediction power of this method by comparing the in silico prediction results with in vitro determined EF values, obtained by three different methods. The EFs of the solid lipid microparticles (SLM) DPIs were determined in vitro by Twin stage impinger, Next generation impactor and Fast Screening Impactor. CFD-DPM model was successfully designed, and then the simulation results indicated the percentage of particles that remained in the inhaler. Based on those data, DPI EFs were calculated to be in the range of 83 % and 92%, indicating that CFD-DPM simulations were able to catch the differences between five SLM DPI formulations. In addition, CFD-DPM predicted the regional particle deposition in the inhaler, which cannot be precisely determined based on in vitro experiments. CFD-DPM predicted EF values were generally comparable to the EF values obtained by three in vitro methods, although some differences were observed between in vitro and in silico values. Therefore, it can be concluded that although additional improvements of CFD-DPM designed model are still necessary in order to be able to precisely describe aerodynamic performance of SLM DPIs, CFD-DPM modeling can be considered as a very useful tool in DPIs development.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122097187","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 : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635574
M. Ivanovic, A. Živić, N. Tachos, George Gois, N. Filipovic, D. Fotiadis
The paper describes experiences from building and cloudification of the in-silico research platform SilicoFCM, an innovative in-silico clinical trials' solution for the design and functional optimization of whole heart performance and monitoring effectiveness of pharmacological treatment, with the aim to reduce the animal studies and the human clinical trials. The primary aim of cloudification was to prove portability, improve scalability and reduce long-term infrastructure costs. The most computationally expensive part of the platform, the scientific workflow manager, was successfully ported to Amazon Web Services. We benchmarked the performance on three distinct research workflows, each of them having different resource requirements and execution time. The first benchmark was pure performance of running workflow sequentially. The aim of the second test was to stress-test the underlying infrastructure by submitting multiple workflows simultaneously. The benchmark results are promising, painting the infrastructure launching overhead almost negligible in this kind of heavy computational use-case.
本文介绍了硅芯片研究平台硅ofcm的构建和云化经验，硅ofcm是一种创新的硅芯片临床试验解决方案，用于全心脏性能的设计和功能优化以及药物治疗效果的监测，旨在减少动物研究和人体临床试验。云化的主要目标是证明可移植性、提高可伸缩性和降低长期基础设施成本。该平台中计算成本最高的部分，即科学工作流管理器，已成功移植到Amazon Web Services。我们在三个不同的研究工作流上对性能进行基准测试，每个工作流都有不同的资源需求和执行时间。第一个基准测试是连续运行工作流的纯粹性能。第二个测试的目的是通过同时提交多个工作流来对底层基础结构进行压力测试。基准测试结果很有希望，在这种计算量很大的用例中，基础设施的启动开销几乎可以忽略不计。
{"title":"In-silico Research Platform in the Cloud - Performance and Scalability Analysis","authors":"M. Ivanovic, A. Živić, N. Tachos, George Gois, N. Filipovic, D. Fotiadis","doi":"10.1109/BIBE52308.2021.9635574","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635574","url":null,"abstract":"The paper describes experiences from building and cloudification of the in-silico research platform SilicoFCM, an innovative in-silico clinical trials' solution for the design and functional optimization of whole heart performance and monitoring effectiveness of pharmacological treatment, with the aim to reduce the animal studies and the human clinical trials. The primary aim of cloudification was to prove portability, improve scalability and reduce long-term infrastructure costs. The most computationally expensive part of the platform, the scientific workflow manager, was successfully ported to Amazon Web Services. We benchmarked the performance on three distinct research workflows, each of them having different resource requirements and execution time. The first benchmark was pure performance of running workflow sequentially. The aim of the second test was to stress-test the underlying infrastructure by submitting multiple workflows simultaneously. The benchmark results are promising, painting the infrastructure launching overhead almost negligible in this kind of heavy computational use-case.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129486967","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 : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635248
Valmir Ferreira dos Santos Junior, João Araújo Castelo Branco, Marcos Antonio de Oliveira, T. C. D. Silva, L. A. Cruz, R. P. Magalhães
It is increasingly common to use chatbots as an interface to use services. Making this experience more humanized requires the chatbot to understand natural language and express itself using natural language. One crucial step to achieve this is to label the data with intentions and entities. After labeling, one can use the labeled data to train a Natural Language Understanding (NLU) component. The NLU component interprets the text extracting the intentions and entities present in that text. Manually label the data is an onerous and impracticable process due to the high volume of data. Thus, an unsupervised machine learning technique, such as data clustering, is usually used to find patterns in the data and thereby label them. For this task, it is essential to have an effective vector embedding representation of texts that depicts the semantic information and helps the machine understand the context, intent, and other nuances of the entire text. In this paper, we perform an extensive evaluation of different text embedding models for clustering, labeling, and training an NLU model using the text of attendances from the Coronavirus Platform Service of Ceará, Brazil. We also show how different text embeddings result in different clustering, thus capturing different intentions of patients.
{"title":"A Natural Language Understanding Model COVID-19 based for chatbots","authors":"Valmir Ferreira dos Santos Junior, João Araújo Castelo Branco, Marcos Antonio de Oliveira, T. C. D. Silva, L. A. Cruz, R. P. Magalhães","doi":"10.1109/BIBE52308.2021.9635248","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635248","url":null,"abstract":"It is increasingly common to use chatbots as an interface to use services. Making this experience more humanized requires the chatbot to understand natural language and express itself using natural language. One crucial step to achieve this is to label the data with intentions and entities. After labeling, one can use the labeled data to train a Natural Language Understanding (NLU) component. The NLU component interprets the text extracting the intentions and entities present in that text. Manually label the data is an onerous and impracticable process due to the high volume of data. Thus, an unsupervised machine learning technique, such as data clustering, is usually used to find patterns in the data and thereby label them. For this task, it is essential to have an effective vector embedding representation of texts that depicts the semantic information and helps the machine understand the context, intent, and other nuances of the entire text. In this paper, we perform an extensive evaluation of different text embedding models for clustering, labeling, and training an NLU model using the text of attendances from the Coronavirus Platform Service of Ceará, Brazil. We also show how different text embeddings result in different clustering, thus capturing different intentions of patients.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123246163","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 : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635457
I. Šaveljić, T. Djukić, D. Nikolić, S. Djorovic, N. Filipovic
Cardiovascular diseases are the leading cause of death in the world with an incidence of about 30% of total mortality. It is a disease of the blood vessels of the heart that most often occurs due to the process of atherosclerosis. The process of atherosclerosis leads to narrowing of the coronary arteries, and thus to a reduced supply of blood or oxygen to the heart muscle. A fractional flow reserve (FFR) indicates the severity of blood flow blockages in the coronary arteries and allows physicians to identify which specific lesion or lesions are responsible for patient ischemia. In this paper, we studied the values of the FFR, using numerical simulations, on the geometries obtained by reconstructing the angiogram images.
{"title":"Numerical simulation of fractional flow reserve in atherosclerotic coronary arteries","authors":"I. Šaveljić, T. Djukić, D. Nikolić, S. Djorovic, N. Filipovic","doi":"10.1109/BIBE52308.2021.9635457","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635457","url":null,"abstract":"Cardiovascular diseases are the leading cause of death in the world with an incidence of about 30% of total mortality. It is a disease of the blood vessels of the heart that most often occurs due to the process of atherosclerosis. The process of atherosclerosis leads to narrowing of the coronary arteries, and thus to a reduced supply of blood or oxygen to the heart muscle. A fractional flow reserve (FFR) indicates the severity of blood flow blockages in the coronary arteries and allows physicians to identify which specific lesion or lesions are responsible for patient ischemia. In this paper, we studied the values of the FFR, using numerical simulations, on the geometries obtained by reconstructing the angiogram images.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131655275","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 : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635539
Anton Bjømdahl Mortensen, Emil Tribler Pedersen, J. Knudsen, J. Mortensen, Marie Hiorth Bogestrand, L. Struijk, Ásgerdur Arna Pálsdóttir
Individuals with tetraplegia are severely limited in terms of independence, which can lead to depression and premature death. The field of robotics has sought to create solutions for increased independence by enabling individuals with tetraplegia to perform physical activities. This includes semiautomatic solutions. The use case of an assistive robotic manipulator (ARM) and thus increased independence could be expanded by allowing remote control, e.g. when the user is lying in bed. This study presents and evaluates semi-autonomous intraoral tongue-based control of a seven degree of freedom ARM/gripper in a near and remote setting. The system consists of a tongue control interface (iTongue), a semi-automatic framework based on YOLOv5 and adaptive levels of automation, an Intel Realsense D435i camera and a JACO robotic manipulator. Two study participants completed ten rounds of controlling the JACO ARM to reach and pick up a soda bottle and pour from it in a cup. The semi-automation improved the grasping performance for both of the study participants, particularly when controlling in a remote setting, decreasing the cognitive load and the overall task completion time. The presented system has the potential to increase the independence and quality of life for individuals with tetraplegia, by enabling the user to perform physical activities even when lying in bed. Future work should include expanding the semi-automation to other activities of daily living and evaluating the system in a greater population and by individuals with tetraplegia.
{"title":"Adaptive Semi-automatic Robot Control by Tongue in a Remote Setting for Individuals with Tetraplegia","authors":"Anton Bjømdahl Mortensen, Emil Tribler Pedersen, J. Knudsen, J. Mortensen, Marie Hiorth Bogestrand, L. Struijk, Ásgerdur Arna Pálsdóttir","doi":"10.1109/BIBE52308.2021.9635539","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635539","url":null,"abstract":"Individuals with tetraplegia are severely limited in terms of independence, which can lead to depression and premature death. The field of robotics has sought to create solutions for increased independence by enabling individuals with tetraplegia to perform physical activities. This includes semiautomatic solutions. The use case of an assistive robotic manipulator (ARM) and thus increased independence could be expanded by allowing remote control, e.g. when the user is lying in bed. This study presents and evaluates semi-autonomous intraoral tongue-based control of a seven degree of freedom ARM/gripper in a near and remote setting. The system consists of a tongue control interface (iTongue), a semi-automatic framework based on YOLOv5 and adaptive levels of automation, an Intel Realsense D435i camera and a JACO robotic manipulator. Two study participants completed ten rounds of controlling the JACO ARM to reach and pick up a soda bottle and pour from it in a cup. The semi-automation improved the grasping performance for both of the study participants, particularly when controlling in a remote setting, decreasing the cognitive load and the overall task completion time. The presented system has the potential to increase the independence and quality of life for individuals with tetraplegia, by enabling the user to perform physical activities even when lying in bed. Future work should include expanding the semi-automation to other activities of daily living and evaluating the system in a greater population and by individuals with tetraplegia.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"1119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128611436","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 : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635407
H. Nieto-Chaupis
At first phases of renal damage, kidney would allow to pass giant proteins such as albumin whose size is of order of nm., surpassing the glomerular zone and therefore going to the Bowman's space. In this manner at the glomerular area proteins can form charge density that would give origin to spontaneous currents. In this paper equations corresponding to the Kirchhoff equation are derived. The usage of classical electrodynamics and the diffusion equation allows to derive the voltage as function of permittivity. This can be seen as an efficient biomarker. Therefore advanced technologies with capabilities for measuring micro and nano voltages in prospective implanted devices might to play a crucial role to anticipate the renal damage.
{"title":"Bioelectrical Circuit from Charged Proteins as Biomarker of Renal Damage","authors":"H. Nieto-Chaupis","doi":"10.1109/BIBE52308.2021.9635407","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635407","url":null,"abstract":"At first phases of renal damage, kidney would allow to pass giant proteins such as albumin whose size is of order of nm., surpassing the glomerular zone and therefore going to the Bowman's space. In this manner at the glomerular area proteins can form charge density that would give origin to spontaneous currents. In this paper equations corresponding to the Kirchhoff equation are derived. The usage of classical electrodynamics and the diffusion equation allows to derive the voltage as function of permittivity. This can be seen as an efficient biomarker. Therefore advanced technologies with capabilities for measuring micro and nano voltages in prospective implanted devices might to play a crucial role to anticipate the renal damage.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114633348","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：