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On-Line Blood Glucose Level Calculation

KOUTNÝ, T., KRČMA, M., KOHOUT, J., JEŽEK, P., VARNUŠKOVÁ, J., VČELÁK, P., STRNÁDEK, J. On-Line Blood Glucose Level Calculation. Procedia Computer Science, 2016, roč. 2016, č. 98, s. 228-235. ISSN: 1877-0509
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Diabetes is a silent disease. It is the 8th most common cause of death that does not hurt until it is too late and the disease has developed. Technology plays a vital role in managing diabetes and educating patients about importance of the treatment. The patient must be able to manage his blood glucose level. However, blood glucose level is measured sporadically as it causes important discomfort to the patient. Measuring glucose level in subcutaneous tissue is minimally invasive technique and thus considerably comfortable, but this level may be different from blood glucose level. We implemented a recently proposed method of blood glucose level calculation from the continuously measured subcutaneous tissue glucose level. Then, we developed a web portal that makes this method accessible to any doctor?s office and any diabetic patient. To the best of our knowledge, we are the very first web portal that does this. In this paper, we describe the portal.

MoBio - A mobile application for collecting data from sensors

JEŽEK, P., MOUČEK, R. MoBio - A mobile application for collecting data from sensors. In Proceedings of the International Conference on Information and Communication Technologies for Ageing Well and e-Health. Setúbal: SciTePress, 2016. s. 115-121. ISBN: 978-989-758-180-9
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There are a lot of sensors for monitoring human health and/or fitness level on the market. They facilitate collection of data from the human body and advanced devices even facilitate data transfer to remote servers where the collected data are further processed. While health data, obtained e.g. from accelerometers or chest straps, are collected rather frequently, brain electrophysiology data, obtained from surface electrodes, are still collected relatively rarely. However, integration and correlation of brain signals with other sensory data would be very interesting for next research of physical and mental health. Although capturing brain signals in real environment still faces technological difficulties, current development of common infrastructure seems to be useful. Then this article deals with various architectures and data formats used for storage and transfer of sensory data and their possible integration with existing neuroinformatics approaches. As a solution we introduced a terminology describing data from a limited collection of sensors. The terminology is implemented in the odML format and integrated in a proof-of-concept Android application. Data transfer, storage and visualisation as well as integration with a remote neuroinformatics resource are presented.

Guess the Number - applying a simple brain-computer interface to school-age children

VAŘEKA, L., PROKOP, T., ŠTĚBETÁK, J., MOUČEK, R. Guess the Number - applying a simple brain-computer interface to school-age children. In Biostec 2016, Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies. Setúbal: SciTePress, 2016. s. 263-270. ISBN: 978-989-758-170-0
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Although research into brain-computer interfaces is more common in recent years, studies concerning large groups of specific subjects are still lacking. This paper describes a simple brain-computer interface (BCI) experiment that was performed on a group of over 200 school-age children using the technique and methods of event related potentials. In the first phase, experimental data were recorded in various elementary and secondary schools, mainly in the Pilsen region of the Czech Republic. The task was to guess the number between 1 and 9 that the measured subject thinks on. Concurrently, a human expert made a decision about the target number based on averaged P300 waveforms observed on-line. In the second phase, an application for automatic classification was developed for off-line data. A small subset of the data was used for training; the rest of the data was used to evaluate the accuracy of classification. Two feature extraction methods were compared; subsampling and discrete wavelet transform for feature extraction. Multi-layer perceptron was used for classification. The human expert achieved the accuracy of 67.6%, while some of the automatic algorithms were able to significantly outperform the expert; the maximum classification accuracy reached 77.2%.

Ontology Based Description of Analytic Methods for Electrophysiology

ŠTĚBETÁK, J., MOUČEK, R. Ontology Based Description of Analytic Methods for Electrophysiology. In BIOSTEC 2016 ? Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies, Volume 5: HEALTHINF. Setúbal: SciTiPress, 2016. s. 420-425. ISBN: 978-989-758-170-0
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The growing electrophysiology research leads to the collection of large amounts of experimental data and consequently to the broader application, eventually development of analytic methods, algorithms, and workflows. Then appropriate metadata definition and related data description is critical for long term storage and later identification of experimental data. Although a detailed description of electrophysiology data has not become a commonly used procedure so far, publicly available and well described data have started to appear in professional journals. The next reasonable step is to shift attention to the analysis of electrophysiology data. Since the analysis of this kind of data is rather complex, identification and appropriate description of used methods, algorithms and workflows would help reproducibility of the research in the field. This description would also allow developing automatic or semi-automatic systems for data analysis or constructing complex workflows in a more user friendly way. Based on these assumptions authors present a custom ontology for description of analytic methods and workflows in electrophysiology that is proposed to be discussed within the scientific community.

Link between Sentiment and Human Activity Represented by Footsteps ? Experiment Exploiting IoT Devices and Social Networks

SALAMON, J., MOUČEK, R. Link between Sentiment and Human Activity Represented by Footsteps ? Experiment Exploiting IoT Devices and Social Networks. In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016) - Volume 5: HEALTHINF. Setúbal: SciTePress, 2016. s. 450-457. ISBN: 978-989-758-170-0
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The Internet of Things world brings to our lives many opportunities to monitor our daily activities by collecting data from various devices. Complementary to it, the data expressing opinions, suggestions, interpretations, contradictions, and uncertainties are more accessible within variety of online resources. This paper deals with collection and analysis of hard data representing the number of steps and soft data representing the sentiment of participants who underwent a pilot experiment. The paper defines outlines of the problem and presents possible sources of reliable data, sentiment evaluation, sentiment extraction using machine learning methods, and links between the data collected from IoT devices and sentiment expressed by the participant in a textual form. Then the results provided by using inferential statistics are presented. The paper is concluded by discussion and summarization of results and future work proposals.

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