Pub Date : 2015-11-02DOI: 10.1109/BIBE.2015.7367690
A. Tolonen, L. Cluitmans, E. Smits, M. Gils, N. Maurits, R. Zietsma
An easily performed and objective test of patients fine motor skills would be valuable in the diagnosis of Parkinson's disease (PD). In this study we present a set of automatic methods for quantifying the motor symptoms of PD and show that these automatically extracted features can be used to distinguish PD from other movement disorders causing tremor, namely essential tremor (ET), functional tremor (FT) and enhanced physiological tremor (EPT). The classification accuracies (mean of sensitivity and specificity) for separating PD from the other tremor syndromes were 82.0 % for ET, 69.8 % for FT and 72.2 % for EPT.
{"title":"Distinguishing Parkinson's disease from other syndromes causing tremor using automatic analysis of writing and drawing tasks","authors":"A. Tolonen, L. Cluitmans, E. Smits, M. Gils, N. Maurits, R. Zietsma","doi":"10.1109/BIBE.2015.7367690","DOIUrl":"https://doi.org/10.1109/BIBE.2015.7367690","url":null,"abstract":"An easily performed and objective test of patients fine motor skills would be valuable in the diagnosis of Parkinson's disease (PD). In this study we present a set of automatic methods for quantifying the motor symptoms of PD and show that these automatically extracted features can be used to distinguish PD from other movement disorders causing tremor, namely essential tremor (ET), functional tremor (FT) and enhanced physiological tremor (EPT). The classification accuracies (mean of sensitivity and specificity) for separating PD from the other tremor syndromes were 82.0 % for ET, 69.8 % for FT and 72.2 % for EPT.","PeriodicalId":422807,"journal":{"name":"2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133991944","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 : 2015-11-02DOI: 10.1109/BIBE.2015.7367658
B. Cirkovic, A. Cvetkovic, S. Ninkovic, N. Filipovic
In this paper, we described the practical application of data mining methods for estimation of survival rate and disease relapse for breast cancer patients. A comparative study of prominent machine learning models was carried out and according to the achieved results we concluded that the classifiers obviously learn some of the concepts of breast cancer survivability and recurrence. These algorithms were successfully applied to a novel breast cancer data set of the Clinical Center of Kragujevac. The Naive Bayes classifier is selected as a model for prognosis of cancer survivability on the basis of the 5 years survival rate, while the Artificial Neural Network has achieved the best performance in prognosis of cancer recurrence. Selection of twenty attributes that are the most related to success of prognosis on survivability can give new insights into the set of prognostic factors which need to be observed by medical experts.
{"title":"Prediction models for estimation of survival rate and relapse for breast cancer patients","authors":"B. Cirkovic, A. Cvetkovic, S. Ninkovic, N. Filipovic","doi":"10.1109/BIBE.2015.7367658","DOIUrl":"https://doi.org/10.1109/BIBE.2015.7367658","url":null,"abstract":"In this paper, we described the practical application of data mining methods for estimation of survival rate and disease relapse for breast cancer patients. A comparative study of prominent machine learning models was carried out and according to the achieved results we concluded that the classifiers obviously learn some of the concepts of breast cancer survivability and recurrence. These algorithms were successfully applied to a novel breast cancer data set of the Clinical Center of Kragujevac. The Naive Bayes classifier is selected as a model for prognosis of cancer survivability on the basis of the 5 years survival rate, while the Artificial Neural Network has achieved the best performance in prognosis of cancer recurrence. Selection of twenty attributes that are the most related to success of prognosis on survivability can give new insights into the set of prognostic factors which need to be observed by medical experts.","PeriodicalId":422807,"journal":{"name":"2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127268121","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 : 2015-11-02DOI: 10.1109/BIBE.2015.7367625
K. Delibasis, Ilias Maglogiannis
In this paper we present an algorithm that can discriminate between standing and fallen silhouettes in video sequences acquired by a fish-eye camera, in order to detect falls in an indoor environment. The proposed algorithm exploits the model of image formation that is based on the spherical projection to derive the orientation in the image of elongated vertical structures. The algorithm does not require the camera to be calibrated. The only requirement is that the optical axis of the camera being parallel to the vertical axis. Initial results show that fall detection can be performed with high accuracy, whereas, the algorithm itself is very efficient, allowing real time implementation.
{"title":"A fall detection algorithm for indoor video sequences captured by fish-eye camera","authors":"K. Delibasis, Ilias Maglogiannis","doi":"10.1109/BIBE.2015.7367625","DOIUrl":"https://doi.org/10.1109/BIBE.2015.7367625","url":null,"abstract":"In this paper we present an algorithm that can discriminate between standing and fallen silhouettes in video sequences acquired by a fish-eye camera, in order to detect falls in an indoor environment. The proposed algorithm exploits the model of image formation that is based on the spherical projection to derive the orientation in the image of elongated vertical structures. The algorithm does not require the camera to be calibrated. The only requirement is that the optical axis of the camera being parallel to the vertical axis. Initial results show that fall detection can be performed with high accuracy, whereas, the algorithm itself is very efficient, allowing real time implementation.","PeriodicalId":422807,"journal":{"name":"2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129110467","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 : 2015-11-02DOI: 10.1109/BIBE.2015.7367698
Kostas M. Tsiouris, S. Konitsiotis, S. Markoula, D. Koutsouris, A. Sakellarios, D. Fotiadis
An unsupervised methodology for the detection of Epileptic seizures in EEG recordings is proposed. The time-frequency content of the EEG signals is extracted using the Short Time Fourier Transform. The analysis focuses on the EEG energy distribution among the well-established delta, theta and alpha rhythms (2-13 Hz), as energy variations in these frequency bands are widely associated with seizure activity. Relying on seizure rhythmicity, the classification is performed by isolating the segments where each rhythm is more clearly and dominantly expressed over the others. For the first time, an unsupervised methodology is evaluated using more than 978 hours of EEG recordings from a public database. The results show that the proposed methodology achieves high seizure detection sensitivity with significantly reduced human intervention.
{"title":"An unsupervised methodology for the detection of epileptic seizures in long-term EEG signals","authors":"Kostas M. Tsiouris, S. Konitsiotis, S. Markoula, D. Koutsouris, A. Sakellarios, D. Fotiadis","doi":"10.1109/BIBE.2015.7367698","DOIUrl":"https://doi.org/10.1109/BIBE.2015.7367698","url":null,"abstract":"An unsupervised methodology for the detection of Epileptic seizures in EEG recordings is proposed. The time-frequency content of the EEG signals is extracted using the Short Time Fourier Transform. The analysis focuses on the EEG energy distribution among the well-established delta, theta and alpha rhythms (2-13 Hz), as energy variations in these frequency bands are widely associated with seizure activity. Relying on seizure rhythmicity, the classification is performed by isolating the segments where each rhythm is more clearly and dominantly expressed over the others. For the first time, an unsupervised methodology is evaluated using more than 978 hours of EEG recordings from a public database. The results show that the proposed methodology achieves high seizure detection sensitivity with significantly reduced human intervention.","PeriodicalId":422807,"journal":{"name":"2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127788447","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 : 2015-11-02DOI: 10.1109/BIBE.2015.7367677
Amal Anwer, Marios Prasinos, D. Bamiou, Nora Macdonald, M. Pavlou, T. Exarchos, G. Spanoudakis, L. Luxon
Dizziness is a common symptom for both benign and life-threatening disorders with subtle distinguishing features. This poses a clinical challenge for physicians dealing with patients suffering from dizziness and vertigo and managing them within primary care. The objective of the EMBalance project is to present a decision support system to assist general practitioners in the diagnosis and management of vestibular disorders. In this work we review the modeling techniques integrated with clinical data to produce a multi-scale, patient-specific balance model that is incorporated in the DSS based on data mining techniques. To understand this we have outlined both technical and clinical aspects to the project. Further we discuss how we intend to test this product in a multicentred, double blind, parallel group randomized controlled trial and the impact we expect the DSS to have both clinically and technologically.
{"title":"EMBalance data repository modeling and clinical application","authors":"Amal Anwer, Marios Prasinos, D. Bamiou, Nora Macdonald, M. Pavlou, T. Exarchos, G. Spanoudakis, L. Luxon","doi":"10.1109/BIBE.2015.7367677","DOIUrl":"https://doi.org/10.1109/BIBE.2015.7367677","url":null,"abstract":"Dizziness is a common symptom for both benign and life-threatening disorders with subtle distinguishing features. This poses a clinical challenge for physicians dealing with patients suffering from dizziness and vertigo and managing them within primary care. The objective of the EMBalance project is to present a decision support system to assist general practitioners in the diagnosis and management of vestibular disorders. In this work we review the modeling techniques integrated with clinical data to produce a multi-scale, patient-specific balance model that is incorporated in the DSS based on data mining techniques. To understand this we have outlined both technical and clinical aspects to the project. Further we discuss how we intend to test this product in a multicentred, double blind, parallel group randomized controlled trial and the impact we expect the DSS to have both clinically and technologically.","PeriodicalId":422807,"journal":{"name":"2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129663198","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 : 2015-11-02DOI: 10.1109/BIBE.2015.7367649
Jelena R. Dorovic, D. Milenkovic, Z. Marković
Free radical scavenging of gallic acid was studied through electron transfer mechanism (ET) in water and pentylethanoate solutions. Examination was performed using density functional theory (DFT) and Marcus theory. Three particular free radicals were selected for analysis of mechanistic pathway of the second step of sequential proton loss electron transfer (SPLET). Based on the thermochemical and kinetic data, it is presumed which hydroxyl group of gallic acid is more suitable for reaction through mentioned antioxidant mechanism. Obtained results are in line with our previous reports.
{"title":"Study of electron transfer mechanism of gallic acid","authors":"Jelena R. Dorovic, D. Milenkovic, Z. Marković","doi":"10.1109/BIBE.2015.7367649","DOIUrl":"https://doi.org/10.1109/BIBE.2015.7367649","url":null,"abstract":"Free radical scavenging of gallic acid was studied through electron transfer mechanism (ET) in water and pentylethanoate solutions. Examination was performed using density functional theory (DFT) and Marcus theory. Three particular free radicals were selected for analysis of mechanistic pathway of the second step of sequential proton loss electron transfer (SPLET). Based on the thermochemical and kinetic data, it is presumed which hydroxyl group of gallic acid is more suitable for reaction through mentioned antioxidant mechanism. Obtained results are in line with our previous reports.","PeriodicalId":422807,"journal":{"name":"2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126557047","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 : 2015-11-02DOI: 10.1109/BIBE.2015.7367728
Yasar Khan, Muntazir Mehdi, Alokkumar Jha, Saleem Raza, André Freitas, Marggie Jones, Ratnesh Sahay
The inner ear is physically inaccessible in living humans, which leads to unique difficulties in studying its normal function and pathology as in other human organs. Recently, biosimulation model has gained a significant attention to understand the exact causative factors that give rise to impairment in human organs. However, to build a biosimulation model for human organ concepts and their topological relationships from multiple and semantically overlapping domains such as biology, anatomy, geometrical, mathematical, physical models are required. In this paper, we focus on modelling the inner-ear macro anatomical concepts and their topological relationships. We extended the Foundational Model of Anatomy (FMA) ontology to cover micro-level version of human inner-ear anatomy where connection between simulating tissues, liquids, soft tissues and connecting adjacent (e.g. hair cells, perilymph) parts studied in detail, included and implemented.
{"title":"Extending inner-ear anatomical concepts in the Foundational Model of Anatomy (FMA) ontology","authors":"Yasar Khan, Muntazir Mehdi, Alokkumar Jha, Saleem Raza, André Freitas, Marggie Jones, Ratnesh Sahay","doi":"10.1109/BIBE.2015.7367728","DOIUrl":"https://doi.org/10.1109/BIBE.2015.7367728","url":null,"abstract":"The inner ear is physically inaccessible in living humans, which leads to unique difficulties in studying its normal function and pathology as in other human organs. Recently, biosimulation model has gained a significant attention to understand the exact causative factors that give rise to impairment in human organs. However, to build a biosimulation model for human organ concepts and their topological relationships from multiple and semantically overlapping domains such as biology, anatomy, geometrical, mathematical, physical models are required. In this paper, we focus on modelling the inner-ear macro anatomical concepts and their topological relationships. We extended the Foundational Model of Anatomy (FMA) ontology to cover micro-level version of human inner-ear anatomy where connection between simulating tissues, liquids, soft tissues and connecting adjacent (e.g. hair cells, perilymph) parts studied in detail, included and implemented.","PeriodicalId":422807,"journal":{"name":"2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124516974","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 : 2015-11-02DOI: 10.1109/BIBE.2015.7367685
Nantia D. Iakovidou, Manolis Christodoulakis, E. Papathanasiou, S. Papacostas, G. Mitsis
It is fairly established that dynamic recordings of functional activity maps can naturally and efficiently be represented by functional connectivity networks. In this article we study weighted and fully-connected brain networks, created from electroencephalographic (EEG) measurements that concern patients with focal and generalized epilepsy. We introduce a totally new methodology that has never been utilized before and that investigates weighted and fully-connected networks, which includes eigen-decomposition analysis, feature extraction and quantitative comparisons among entire graph datasets. Our goal is to establish epileptic seizure detection/prediction rules, by identifying repetitive EEG activity in patients before and after each seizure onset. In the present paper we treat each brain network as a weighted and full adjacency matrix, without cutting, binarizing or ignoring any values. In this way, it is the first time that the full structure of the connectivity weighing profile is exploited. Also apart from graph theory approaches, mathematical models such as eigen-decomposition analysis are used in our research, in order to study and analyze brain networks. Finally, we present and discuss the results and conclusions of our new method, which are in line with earlier EEG epilepsy findings and demonstrate a standard EEG behavior in both the postictal and preictal period.
{"title":"Introducing weighted approaches to study network brain dynamics from EEG epilepsy measurements: The EigenBrain algorithm","authors":"Nantia D. Iakovidou, Manolis Christodoulakis, E. Papathanasiou, S. Papacostas, G. Mitsis","doi":"10.1109/BIBE.2015.7367685","DOIUrl":"https://doi.org/10.1109/BIBE.2015.7367685","url":null,"abstract":"It is fairly established that dynamic recordings of functional activity maps can naturally and efficiently be represented by functional connectivity networks. In this article we study weighted and fully-connected brain networks, created from electroencephalographic (EEG) measurements that concern patients with focal and generalized epilepsy. We introduce a totally new methodology that has never been utilized before and that investigates weighted and fully-connected networks, which includes eigen-decomposition analysis, feature extraction and quantitative comparisons among entire graph datasets. Our goal is to establish epileptic seizure detection/prediction rules, by identifying repetitive EEG activity in patients before and after each seizure onset. In the present paper we treat each brain network as a weighted and full adjacency matrix, without cutting, binarizing or ignoring any values. In this way, it is the first time that the full structure of the connectivity weighing profile is exploited. Also apart from graph theory approaches, mathematical models such as eigen-decomposition analysis are used in our research, in order to study and analyze brain networks. Finally, we present and discuss the results and conclusions of our new method, which are in line with earlier EEG epilepsy findings and demonstrate a standard EEG behavior in both the postictal and preictal period.","PeriodicalId":422807,"journal":{"name":"2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120943520","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 : 2015-11-02DOI: 10.1109/BIBE.2015.7367689
F. Zubaydi, Ayat Saleh, F. Aloul, A. Sagahyroon
mHealth is a growing field that enables individuals to monitor their health status and facilitates the sharing of medical records with physicians and between hospitals anytime and anywhere. Unfortunately, smartphones and mHealth applications are still vulnerable to a wide range of security threats due to their portability and weaknesses in management and design. Nevertheless, mHealth users are becoming more aware of the security and privacy issues related to their personal healthcare information. This survey discusses the security and privacy issues in current mHealth systems and their impact. We also discuss the latest threats, attacks and proposed countermeasures that could support secure sensitive mHealth systems. Finally, we conclude with a brief summary of open security problems that still need to be addressed in the mHealth field.
{"title":"Security of mobile health (mHealth) systems","authors":"F. Zubaydi, Ayat Saleh, F. Aloul, A. Sagahyroon","doi":"10.1109/BIBE.2015.7367689","DOIUrl":"https://doi.org/10.1109/BIBE.2015.7367689","url":null,"abstract":"mHealth is a growing field that enables individuals to monitor their health status and facilitates the sharing of medical records with physicians and between hospitals anytime and anywhere. Unfortunately, smartphones and mHealth applications are still vulnerable to a wide range of security threats due to their portability and weaknesses in management and design. Nevertheless, mHealth users are becoming more aware of the security and privacy issues related to their personal healthcare information. This survey discusses the security and privacy issues in current mHealth systems and their impact. We also discuss the latest threats, attacks and proposed countermeasures that could support secure sensitive mHealth systems. Finally, we conclude with a brief summary of open security problems that still need to be addressed in the mHealth field.","PeriodicalId":422807,"journal":{"name":"2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121261182","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 : 2015-11-02DOI: 10.1109/BIBE.2015.7367719
Kento Konishi, H. Hagiwara
The objective of this study was to monitor changes in physiological indexes of alpha attenuation coefficient (AAC), high-frequency component (HF) and oxygenated hemoglobin (oxyHb) as objective parameters and Roken Arousal Scale (RAS) as a subjective parameter in experimental participants performing simple tasks related to motor skills, as necessary for safe driving. The oxyHb signal was monitored from the frontal association and somatosensory areas using near-infrared spectroscopy (NIRS), which can measure changes in brain hemodynamics during tasks noninvasively and without constraint. Experimental results showed oxyHb and AAC increased, while HF and tracking error decreased when experimental participants were exposed to body sensory vibrations. From these findings, we suggest that body sensory vibration stimuli are valid for monotonous work. In conclusion, we showed the usability of body sensory vibration stimuli for monotonous work such as UniMove, with influences on the autonomic and central nervous systems.
{"title":"Influence of monotonous work and body sensory vibration stimulus on physiological responses","authors":"Kento Konishi, H. Hagiwara","doi":"10.1109/BIBE.2015.7367719","DOIUrl":"https://doi.org/10.1109/BIBE.2015.7367719","url":null,"abstract":"The objective of this study was to monitor changes in physiological indexes of alpha attenuation coefficient (AAC), high-frequency component (HF) and oxygenated hemoglobin (oxyHb) as objective parameters and Roken Arousal Scale (RAS) as a subjective parameter in experimental participants performing simple tasks related to motor skills, as necessary for safe driving. The oxyHb signal was monitored from the frontal association and somatosensory areas using near-infrared spectroscopy (NIRS), which can measure changes in brain hemodynamics during tasks noninvasively and without constraint. Experimental results showed oxyHb and AAC increased, while HF and tracking error decreased when experimental participants were exposed to body sensory vibrations. From these findings, we suggest that body sensory vibration stimuli are valid for monotonous work. In conclusion, we showed the usability of body sensory vibration stimuli for monotonous work such as UniMove, with influences on the autonomic and central nervous systems.","PeriodicalId":422807,"journal":{"name":"2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131144567","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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