Pub Date : 2016-09-01DOI: 10.1109/HealthCom.2016.7749482
R. Bharath, P. Rajalakshmi
Medical ultrasound scanning is a widely used diagnostic imaging modality in health-care. Speckle is inherent noise present in ultrasound images reducing the diagnostic accuracy of ultrasound scanning. Speckle noise contributes to high variance between pixels and delineates boundaries of the organs. Effective despeckling involves reducing the variance between pixels corresponding to homogeneous region and to preserve anatomical details simultaneously. Non-Local Means filters are highly successful and produced state of the art results in despeckling ultrasound images. In this paper, we show the effectiveness of Non-Local Means filter with polynomial regression kernel in despeckling ultrasound images. The proposed algorithm is evaluated on software simulated and real time ultrasound images and proved very effective in both despeckling and edge preservation.
{"title":"Non-local means kernel regression based despeckling of B-mode ultrasound images","authors":"R. Bharath, P. Rajalakshmi","doi":"10.1109/HealthCom.2016.7749482","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749482","url":null,"abstract":"Medical ultrasound scanning is a widely used diagnostic imaging modality in health-care. Speckle is inherent noise present in ultrasound images reducing the diagnostic accuracy of ultrasound scanning. Speckle noise contributes to high variance between pixels and delineates boundaries of the organs. Effective despeckling involves reducing the variance between pixels corresponding to homogeneous region and to preserve anatomical details simultaneously. Non-Local Means filters are highly successful and produced state of the art results in despeckling ultrasound images. In this paper, we show the effectiveness of Non-Local Means filter with polynomial regression kernel in despeckling ultrasound images. The proposed algorithm is evaluated on software simulated and real time ultrasound images and proved very effective in both despeckling and edge preservation.","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132228854","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 : 2016-09-01DOI: 10.1109/HealthCom.2016.7749441
I. True, G. Armitage
For some devices and services, a consistently reliable connection to the internet is crucial; a failure to report to an internet service could result in significant financial or property damage or loss of life. We propose a solution through the development and testing of a “High-availability Internet Gateway” (HaIG) which can be installed into a network and utilise multiple redundant internet connections in order to guarantee uptime for a secure tunnel for medical devices. Three potential solutions are evaluated: Layer 2 Bonding (L2B), Multipath TCP (MPTCP) and Stream Control Transport Protocol (SCTP). MPTCP and L2B were found to be less suitable than SCTP at providing a reliable, high-availability fail-over solution. We incorporated the SCTP-based solution into a consumer networking device running OpenWRT, and used controlled testbed trials to demonstrate the use of redundant internet connections for providing a high-availability connection for applications such as remote cardiac monitoring.
{"title":"Potential redundant link fail-over strategies for uptime-sensitive medical telemetry applications","authors":"I. True, G. Armitage","doi":"10.1109/HealthCom.2016.7749441","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749441","url":null,"abstract":"For some devices and services, a consistently reliable connection to the internet is crucial; a failure to report to an internet service could result in significant financial or property damage or loss of life. We propose a solution through the development and testing of a “High-availability Internet Gateway” (HaIG) which can be installed into a network and utilise multiple redundant internet connections in order to guarantee uptime for a secure tunnel for medical devices. Three potential solutions are evaluated: Layer 2 Bonding (L2B), Multipath TCP (MPTCP) and Stream Control Transport Protocol (SCTP). MPTCP and L2B were found to be less suitable than SCTP at providing a reliable, high-availability fail-over solution. We incorporated the SCTP-based solution into a consumer networking device running OpenWRT, and used controlled testbed trials to demonstrate the use of redundant internet connections for providing a high-availability connection for applications such as remote cardiac monitoring.","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133964391","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 : 2016-09-01DOI: 10.1109/HealthCom.2016.7749437
N. Grigoriadis, C. Bakirtzis, C. Politis, K. Danas, Christoph Thuemmler
Multiple sclerosis is a chronic and variable disease in matters of symptoms, clinical course and outcome. The ultimate goal of currently used drugs and therapeutic strategies is the control of disease activity and the delay of the ongoing disability. During the last decades, a number of disease-modifying drugs (DMDs), all products of advanced biotechnology are being used. However, these DMDs are yet partially effective since the ongoing disability progression may hardly be prevented. There is growing evidence that these DMDs might be more effective if more accurate monitoring of the disease itself throughout a period of time might be available. In the new era of MS treatment and on the basis of our current knowledge about MS management, it became pretty clear that the overall therapeutic strategy should always be scheduled on strictly individualized basis. To this, MS patients should be encouraged to take control over their own disease and collaborate more effectively with their doctors. The advent of the IoT (Internet of Things) and 5G mobile technologies can support patients in this direction. Since a snapshot of the overall patient's condition during a regular follow-up visit may not represent the every day reality of the patient, the advice given under these conditions may not be that effective. However, if hard data on the patient's motoric and cognitive performance were available “theragnostics” might be much more effective and efficient and a typical flare-up of the condition might be recognized much earlier - or even anticipated. Health 4.0 is the translation of Industrie 4.0 design principles into the health domain [36]. Health 4.0 is based on the utilization of the Internet of Things (IoT) and the use of cyber-physical systems to connect the physical and the virtual world. The use of smart pharmaceuticals bio-sensors and cyber-physical systems in the management of MS could optimize the accuracy and allow for a precise mapping of symptoms over time which is an inevitable prerequisite for personalization of care. Ideally captured data would be processed in real time in order to flag problems up to the care team and on an individual basis anticipate motoric and / or cognitive deficits in an attempt to compensate for neurological deficits. 5G networks are expected to provide the infrastructure and ease in supporting various parameters recording on a real time basis. Relevant clinical studies may further highlight the need of information communication technology in MS management, thus contributing to the overall improvement of patent's quality of life (QoL). This is an absolute necessity for a variable, fluctuating and largely unpredictable disease such as MS.
{"title":"Health 4.0: The case of multiple sclerosis","authors":"N. Grigoriadis, C. Bakirtzis, C. Politis, K. Danas, Christoph Thuemmler","doi":"10.1109/HealthCom.2016.7749437","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749437","url":null,"abstract":"Multiple sclerosis is a chronic and variable disease in matters of symptoms, clinical course and outcome. The ultimate goal of currently used drugs and therapeutic strategies is the control of disease activity and the delay of the ongoing disability. During the last decades, a number of disease-modifying drugs (DMDs), all products of advanced biotechnology are being used. However, these DMDs are yet partially effective since the ongoing disability progression may hardly be prevented. There is growing evidence that these DMDs might be more effective if more accurate monitoring of the disease itself throughout a period of time might be available. In the new era of MS treatment and on the basis of our current knowledge about MS management, it became pretty clear that the overall therapeutic strategy should always be scheduled on strictly individualized basis. To this, MS patients should be encouraged to take control over their own disease and collaborate more effectively with their doctors. The advent of the IoT (Internet of Things) and 5G mobile technologies can support patients in this direction. Since a snapshot of the overall patient's condition during a regular follow-up visit may not represent the every day reality of the patient, the advice given under these conditions may not be that effective. However, if hard data on the patient's motoric and cognitive performance were available “theragnostics” might be much more effective and efficient and a typical flare-up of the condition might be recognized much earlier - or even anticipated. Health 4.0 is the translation of Industrie 4.0 design principles into the health domain [36]. Health 4.0 is based on the utilization of the Internet of Things (IoT) and the use of cyber-physical systems to connect the physical and the virtual world. The use of smart pharmaceuticals bio-sensors and cyber-physical systems in the management of MS could optimize the accuracy and allow for a precise mapping of symptoms over time which is an inevitable prerequisite for personalization of care. Ideally captured data would be processed in real time in order to flag problems up to the care team and on an individual basis anticipate motoric and / or cognitive deficits in an attempt to compensate for neurological deficits. 5G networks are expected to provide the infrastructure and ease in supporting various parameters recording on a real time basis. Relevant clinical studies may further highlight the need of information communication technology in MS management, thus contributing to the overall improvement of patent's quality of life (QoL). This is an absolute necessity for a variable, fluctuating and largely unpredictable disease such as MS.","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133301505","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 : 2016-09-01DOI: 10.1109/HealthCom.2016.7749483
Yuefeng Li, Keshi He, Xueli Sun, Honghai Liu
Ultrasound (US) imaging is a promising sensing technique in the field of human-machine interface, and many positive results have been reported in literature on hand gesture recognition or finger angle prediction based on US imaging. However, in most of these studies, linear array ultrasound probes were used to generate US images, which made the US device expensive and bulky. In this paper, a method of extracting forearm muscle information via multiple single-element US transducers is proposed. By using this kind of transducers, a low-cost and small-size human-machine interface can be expected. Preliminary results show that an average recognition accuracy of 96% can be achieved for six motions, including five finger flexions and rest state.
{"title":"Human-machine interface based on multi-channel single-element ultrasound transducers: A preliminary study","authors":"Yuefeng Li, Keshi He, Xueli Sun, Honghai Liu","doi":"10.1109/HealthCom.2016.7749483","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749483","url":null,"abstract":"Ultrasound (US) imaging is a promising sensing technique in the field of human-machine interface, and many positive results have been reported in literature on hand gesture recognition or finger angle prediction based on US imaging. However, in most of these studies, linear array ultrasound probes were used to generate US images, which made the US device expensive and bulky. In this paper, a method of extracting forearm muscle information via multiple single-element US transducers is proposed. By using this kind of transducers, a low-cost and small-size human-machine interface can be expected. Preliminary results show that an average recognition accuracy of 96% can be achieved for six motions, including five finger flexions and rest state.","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132818770","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 : 2016-09-01DOI: 10.1109/HealthCom.2016.7749472
Taoum Aline, Farah Mourad, Amoud Hassan, A. Chkeir, Ziad Fawal, Jacques Duchêne
This study aims to predict Acute Respiratory Distress Syndrome (ARDS) in hospitalized patients using their physiological signals such as heart rate, breathing rate, peripheral arterial oxygen saturation and mean airway blood pressure. A data fusion approach based on hypothesis testing was developed, and applied to mechanically ventilated subjects in the MIMIC II database. By combining the information extracted from the signals using an aggregation rule, we are able to enhance the sensitivity of the ARDS prediction process. As a result, we obtained a sensitivity of up to 85% for individual signals, reaching approximately 92% using the data fusion rule.
{"title":"Data fusion for predicting ARDS using the MIMIC II physiological database","authors":"Taoum Aline, Farah Mourad, Amoud Hassan, A. Chkeir, Ziad Fawal, Jacques Duchêne","doi":"10.1109/HealthCom.2016.7749472","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749472","url":null,"abstract":"This study aims to predict Acute Respiratory Distress Syndrome (ARDS) in hospitalized patients using their physiological signals such as heart rate, breathing rate, peripheral arterial oxygen saturation and mean airway blood pressure. A data fusion approach based on hypothesis testing was developed, and applied to mechanically ventilated subjects in the MIMIC II database. By combining the information extracted from the signals using an aggregation rule, we are able to enhance the sensitivity of the ARDS prediction process. As a result, we obtained a sensitivity of up to 85% for individual signals, reaching approximately 92% using the data fusion rule.","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115597852","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 : 2016-09-01DOI: 10.1109/HealthCom.2016.7749478
T. Lee, J. G. Lim, K. Leo, S. Sanei, P. Y. Lew, E. Chew, L. Zhao
The global adoption of consumer devices capable of wide connectivity like smartphones and tablets have led to improvements in their support infrastructure. These make e-health systems for rehabilitation entirely feasible. Current assessments of patient condition can be subjective and inconsistent as the monotony of repetitious tasks lowers alertness. We propose a system to automate the scoring process for the patient's state. This is performed by embedding widely available sensors such as accelerometers sensors into the objects used in a rehabilitative assessment. These sensors introduce signal distortions such as drift and noise which require data driven filtering as the trajectories of human motion are statistically nonstationary. Building on previous work, we compare the use of time and transform domain processing of motion signals by using splines and singular spectrum analysis on the signals and use data analytic techniques for deriving the assessment scores with good results. These form the basis of an e-health system which is evidence-based, and provides the basis for gains in efficiency and a higher level of healthcare.
{"title":"Towards rehabilitative e-Health by introducing a new automatic scoring system","authors":"T. Lee, J. G. Lim, K. Leo, S. Sanei, P. Y. Lew, E. Chew, L. Zhao","doi":"10.1109/HealthCom.2016.7749478","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749478","url":null,"abstract":"The global adoption of consumer devices capable of wide connectivity like smartphones and tablets have led to improvements in their support infrastructure. These make e-health systems for rehabilitation entirely feasible. Current assessments of patient condition can be subjective and inconsistent as the monotony of repetitious tasks lowers alertness. We propose a system to automate the scoring process for the patient's state. This is performed by embedding widely available sensors such as accelerometers sensors into the objects used in a rehabilitative assessment. These sensors introduce signal distortions such as drift and noise which require data driven filtering as the trajectories of human motion are statistically nonstationary. Building on previous work, we compare the use of time and transform domain processing of motion signals by using splines and singular spectrum analysis on the signals and use data analytic techniques for deriving the assessment scores with good results. These form the basis of an e-health system which is evidence-based, and provides the basis for gains in efficiency and a higher level of healthcare.","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121415268","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 : 2016-09-01DOI: 10.1109/HealthCom.2016.7749428
Giovanna Sannino, I. D. Falco, G. Pietro
The aim of this study is to identify an explicit relationship between life-style and the risk of falling under the form of a mathematical model. Starting from some personal and behavioral information as, e.g., weight, height, age, data about physical activity habits, and concern about falling, the model would easily estimate the score of the Mini-Balance Evaluation Systems (Mini-BES) test. This would make fall risk assessment less invasive, because subjects would not need to undergo the classical Mini-BES test, rather they could estimate it at home by answering some questionnaires. The mathematical model obtained in this study has been tested over a subset of unseen subjects and the results show an average error of ±2.74.
这项研究的目的是在数学模型的形式下确定生活方式和风险之间的明确关系。该模型从一些个人和行为信息出发，如体重、身高、年龄、体育活动习惯数据和对摔倒的担忧，可以很容易地估计出Mini-Balance Evaluation Systems (Mini-BES)测试的分数。这将使跌倒风险评估的侵入性更小，因为受试者不需要进行经典的Mini-BES测试，而是可以在家中通过回答一些问卷来评估。本文所建立的数学模型已在一组未见对象上进行了测试，结果显示平均误差为±2.74。
{"title":"Easy fall risk assessment by estimating the Mini-BES test score","authors":"Giovanna Sannino, I. D. Falco, G. Pietro","doi":"10.1109/HealthCom.2016.7749428","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749428","url":null,"abstract":"The aim of this study is to identify an explicit relationship between life-style and the risk of falling under the form of a mathematical model. Starting from some personal and behavioral information as, e.g., weight, height, age, data about physical activity habits, and concern about falling, the model would easily estimate the score of the Mini-Balance Evaluation Systems (Mini-BES) test. This would make fall risk assessment less invasive, because subjects would not need to undergo the classical Mini-BES test, rather they could estimate it at home by answering some questionnaires. The mathematical model obtained in this study has been tested over a subset of unseen subjects and the results show an average error of ±2.74.","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114926441","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 : 2016-09-01DOI: 10.1109/HealthCom.2016.7749531
S. Kulaç, H. Arslan
Health care applications of wireless communication have been finding places dramatically. One of these applications is communication of implantable medical devices (IMD)s. It is expected that the number of IMDs will increase greatly in the near future. As a result, significant congestion will be experienced in medical implant communication service (MICS) band, leading to interference problems. In this study, we propose reliable listen-before-talk (LBT) mechanism at low signal-to-noise ratios (SNR)s for medical implant communication systems in order to mitigate the interference effects. In our method, we have just brought out power difference between mean peak and mean lowest power spectral values and it provides reliable and simple monitoring of MICS channels' occupation fastly. Our proposed method has superior performance when threshold power level is considered according to the federal communication commission (FCC) Part 95 regulatory standard.
{"title":"Reliable listen-before-talk mechanism for medical implant communication systems","authors":"S. Kulaç, H. Arslan","doi":"10.1109/HealthCom.2016.7749531","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749531","url":null,"abstract":"Health care applications of wireless communication have been finding places dramatically. One of these applications is communication of implantable medical devices (IMD)s. It is expected that the number of IMDs will increase greatly in the near future. As a result, significant congestion will be experienced in medical implant communication service (MICS) band, leading to interference problems. In this study, we propose reliable listen-before-talk (LBT) mechanism at low signal-to-noise ratios (SNR)s for medical implant communication systems in order to mitigate the interference effects. In our method, we have just brought out power difference between mean peak and mean lowest power spectral values and it provides reliable and simple monitoring of MICS channels' occupation fastly. Our proposed method has superior performance when threshold power level is considered according to the federal communication commission (FCC) Part 95 regulatory standard.","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125589970","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 : 2016-09-01DOI: 10.1109/HealthCom.2016.7749535
Mohammad H. Nassralla, Ahmad M. El-Hajj, Fady Baly, Z. Dawy
The development of a neurologically-oriented mobile health system involves significant challenges in terms of the proper sensing and efficient transmission of electroencephalogram (EEG) signals, and the faithful reconstruction of these signals at the receiving node. EEG compression has been widely used to reduce storage requirements, improve the real time processing of the sensed signals, and provide a better and timely feedback to the concerned patients. The non-stationarity of the EEG signals and the large volumes of data being continuously processed mandate the development of data reduction schemes that provide a good tradeoff between compression performance and the preservation of the signal quality and integrity. To this end, we propose in this work a dynamic and effective compression approach for EEG data that relies on a sequence of compression and decompression phases to optimize the compression rate while maintaining a distortion level below a target threshold. Simulation results using real EEG data segments show that even with stringent quality requirements, a notable compression ratio can be attained with minimal processing overhead.
{"title":"Dynamic EEG compression approach with optimized distortion level for mobile health solutions","authors":"Mohammad H. Nassralla, Ahmad M. El-Hajj, Fady Baly, Z. Dawy","doi":"10.1109/HealthCom.2016.7749535","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749535","url":null,"abstract":"The development of a neurologically-oriented mobile health system involves significant challenges in terms of the proper sensing and efficient transmission of electroencephalogram (EEG) signals, and the faithful reconstruction of these signals at the receiving node. EEG compression has been widely used to reduce storage requirements, improve the real time processing of the sensed signals, and provide a better and timely feedback to the concerned patients. The non-stationarity of the EEG signals and the large volumes of data being continuously processed mandate the development of data reduction schemes that provide a good tradeoff between compression performance and the preservation of the signal quality and integrity. To this end, we propose in this work a dynamic and effective compression approach for EEG data that relies on a sequence of compression and decompression phases to optimize the compression rate while maintaining a distortion level below a target threshold. Simulation results using real EEG data segments show that even with stringent quality requirements, a notable compression ratio can be attained with minimal processing overhead.","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126930385","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 : 2016-09-01DOI: 10.1109/HealthCom.2016.7749512
John Edison Muñoz Cardona, T. Paulino, H. Vasanth, Karolina Baras
Virtual Reality (VR) is morphing into a ubiquitous technology by leveraging of smartphones and screenless cases in order to provide highly immersive experiences at a low price point. The result of this shift in paradigm is now known as mobile VR (mVR). Although mVR offers numerous advantages over conventional immersive VR methods, one of the biggest limitations is related with the interaction pathways available for the mVR experiences. Using physiological computing principles, we created the PhysioVR framework, an Open-Source software tool developed to facilitate the integration of physiological signals measured through wearable devices in mVR applications. PhysioVR includes heart rate (HR) signals from Android wearables, electroencephalography (EEG) signals from a low-cost brain computer interface and electromyography (EMG) signals from a wireless armband. The physiological sensors are connected with a smartphone via Bluetooth and the PhysioVR facilitates the streaming of the data using UDP communication protocol, thus allowing a multicast transmission for a third party application such as the Unity3D game engine. Furthermore, the framework provides a bidirectional communication with the VR content allowing an external event triggering using a real-time control as well as data recording options. We developed a demo game project called EmoCat Rescue which encourage players to modulate HR levels in order to successfully complete the in-game mission. EmoCat Rescue is included in the PhysioVR project which can be freely downloaded. This framework simplifies the acquisition, streaming and recording of multiple physiological signals and parameters from wearable consumer devices providing a single and efficient interface to create novel physiologically-responsive mVR applications.
{"title":"PhysioVR: A novel mobile virtual reality framework for physiological computing","authors":"John Edison Muñoz Cardona, T. Paulino, H. Vasanth, Karolina Baras","doi":"10.1109/HealthCom.2016.7749512","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749512","url":null,"abstract":"Virtual Reality (VR) is morphing into a ubiquitous technology by leveraging of smartphones and screenless cases in order to provide highly immersive experiences at a low price point. The result of this shift in paradigm is now known as mobile VR (mVR). Although mVR offers numerous advantages over conventional immersive VR methods, one of the biggest limitations is related with the interaction pathways available for the mVR experiences. Using physiological computing principles, we created the PhysioVR framework, an Open-Source software tool developed to facilitate the integration of physiological signals measured through wearable devices in mVR applications. PhysioVR includes heart rate (HR) signals from Android wearables, electroencephalography (EEG) signals from a low-cost brain computer interface and electromyography (EMG) signals from a wireless armband. The physiological sensors are connected with a smartphone via Bluetooth and the PhysioVR facilitates the streaming of the data using UDP communication protocol, thus allowing a multicast transmission for a third party application such as the Unity3D game engine. Furthermore, the framework provides a bidirectional communication with the VR content allowing an external event triggering using a real-time control as well as data recording options. We developed a demo game project called EmoCat Rescue which encourage players to modulate HR levels in order to successfully complete the in-game mission. EmoCat Rescue is included in the PhysioVR project which can be freely downloaded. This framework simplifies the acquisition, streaming and recording of multiple physiological signals and parameters from wearable consumer devices providing a single and efficient interface to create novel physiologically-responsive mVR applications.","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117323944","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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