{"title":"UbMed: A ubiquitous system for monitoring medication adherence","authors":"V. Silva, M. Rodrigues, R. Barreto, V. Lucena","doi":"10.1109/HealthCom.2016.7749419","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749419","url":null,"abstract":"One of the biggest problems with chronical patients in treatment is medication adherence. Studies indicate that taking drugs out of time influence the patient's treatment decreasing the drugs effect. To minimize this problem, we developed a ubiquitous and intelligent system able to monitor the taking of medicines and to identify whether the patient is meeting the requirements prescribed by the doctor. An architecture provided with a decision system based on rules and trees to evaluate data collected from an intelligent medicine cabinet, sensors and electronic devices available in the patient's home was designed. The system classified the drugs taken pattern and released messages on social networks, SMS, and consumer electronic devices such as TV, smartphone and tablets, without human interference. Its goal is to help keeping the medication on time and helping to decide what to do in case of missing the right time. The algorithms J48, Rep and Random tree, were tested to classify the taking medicine patterns and to chose the right services available. The obtained results are very promising and reached an acceptable accuracy rate.","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"35 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":"128028347","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}
{"title":"Performance improvement of the wireless body area network (WBAN) under interferences","authors":"E. Sarra, T. Ezzedine","doi":"10.1109/HealthCom.2016.7749507","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749507","url":null,"abstract":"WBAN offers promising and innovative applications that enhance the quality of services provided by the healthcare system; it improves patient's quality of life and saves his lives in many situations. WBAN is composed of tiny medical devices on or implanted in the human body and continuously monitors patient's health. The wireless interface and the mobility of the body network enable easier, practical and less cost applications. WBAN provides uninterrupted distantly health monitoring of patient while he is doing his daily activity and it expect to efficiently deliver emergency collected vital signs to the concerned medical entity. However, the high contention level inside the WBAN, the presence of external collocated wireless network and the use of a shared medium, invoke packet collisions and thus affect the communication quality and reliability of the WBAN. Our goal in this article is to analyse the interference's problem of the WBAN based IEEE 802.15.4 protocol in the presence of Wi-Fi transmitters. We opt also for an adequate simulator and configurable mobility model to investigate different effects of the WBAN density as well as the increase number of Wi-Fi transmitters on the performance of the WBAN. We finally propose a simple, no cost and convenient parameters adjustment method to improve the WBAN's capacity, packet delivery, packet transmission and packet delay.","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":"130030564","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}
{"title":"HI-risk: A method to analyse health information risk intelligence","authors":"W. Buchanan, N. V. Deursen","doi":"10.1109/HealthCom.2016.7749536","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749536","url":null,"abstract":"Information security threat intelligence is a prevalent topic amongst researchers, long-established IT-vendors and start-ups. The possibilities of Big Data analytics to security threat and vulnerability scanning offer a significant development in the protection of infrastructures. At the same time, industry research reports continue to state that the main contributing factor in the events leading to a data breach is human error. The common response of information security professionals is to resort to technological solutions to prevent these human errors. However, some very important information security intelligence is not hidden within the network traffic: it's available from the people that work with sensitive information. This article describes the Health Information risk (HI-risk) method to identify non-technical information security risks in healthcare. The method includes risks related to skills, behaviour, processes, organisational culture, physical security, and external influences. HI-risk offers a solution to collect intelligence about nontechnical information security incidents from across the healthcare sector to demonstrate past trends and to be ahead of future incidents. A test of a HI-risk forecast proved the feasibility of this approach in healthcare and beyond. It is suggested that HI-risk could become a valuable addition to existing technical threat and vulnerability monitoring tools.","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"35 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":"128743629","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}
Rute C. Sofia, S. Firdose, L. A. Lopes, W. Moreira, Paulo Mendes
{"title":"NSense: A people-centric, non-intrusive opportunistic sensing tool for contextualizing nearness","authors":"Rute C. Sofia, S. Firdose, L. A. Lopes, W. Moreira, Paulo Mendes","doi":"10.1109/HealthCom.2016.7749490","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749490","url":null,"abstract":"In the context of social well-being and context awareness several eHealth applications have been focused on tracking activities, such as sleep or specific fitness habits, with the purpose of promoting physical well-being with increasing success. Sensing technology can, however, be applied to improve social well-being, in addition to physical well-being. This paper addresses NSense, a tool that has been developed to capture and to infer social interaction patterns aiming to assist in the promotion of social well-being. Experiments carried out under realistic settings validate the NSense performance in terms of its capability to infer social interaction context based on our proposed computational utility functions. Traces obtained during the experiments are available via the CRAWDAD international trace repository.","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"21 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":"133792358","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}
{"title":"Buffer-aware and QoS-effective resource allocation scheme in WBANs","authors":"Zhiqiang Liu, B. Liu, C. Chen","doi":"10.1109/HealthCom.2016.7749508","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749508","url":null,"abstract":"Wireless Body Area Network (WBAN) represents one of the most promising networks to provide health applications for improving the quality of life, such as ubiquitous e-Health services and real-time health monitoring. The resource allocation of an energy-constrained, heterogeneous WBAN is a critical issue that should consider both energy efficiency and Quality of Service (QoS) requirements with the dynamic link characteristics, especially when the limited resource cannot satisfy the expected QoS requirements. In this paper, a buffer aware Energy-efficient and QoS-effective resource allocation scheme is proposed in which the sensor queue buffer states, constraints of QoS metrics and the characteristics of dynamic links are considered. Specifically, a buffer aware sensor evaluation method is designed to dynamically evaluate the sensor state with considering the sensor buffer states for improving the system performance. We then formulate the resource allocation problem for optimizing the transmission power, the transmission rate and the allocated time slots for each sensor to minimize the sum mix-cost, which is defined to characterize the energy cost and QoS cost between attainable QoS support and QoS requirements. Simulation results demonstrate the effectiveness of the buffer aware sensor evaluation method and the proposed energy-efficient and QoS-effective resource allocation scheme.","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"26 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":"124904361","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}
{"title":"CorporateMeasures: A clinical analytics framework leading to clinical intelligence","authors":"F. Nammour, K. Danas, N. Mansour","doi":"10.1109/HealthCom.2016.7749451","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749451","url":null,"abstract":"Patient information in healthcare organizations is distributed across several systems and data silos. Clinicians make decisions based on data in patient health records. Improving the efficiency of decision-support requires collective knowledge of all patient information. The classical approach of linking patient data from many databases into one data warehouse poses various problems when it comes to building clinical analytics. An implementation of the Performance Measurement and Management approach used in Engineering and Business is adapted to healthcare scenarios, and a new system is developed that allows clinicians that are not technical professionals to develop, test and apply custom analytics to patient health data. Part I of this paper is an introduction to the problems and current situation in healthcare data analytics. Part II states the aim and objectives. Part III explains the system design and its modular components. Part IV presents the results of three performance indicators evaluated through the system, and evaluates the system through technical and clinical usability methods. Part V concludes and discusses future work.","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":"124840841","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}
A. A. Iskandar, Reiner Kolla, K. Schilling, W. Völker
{"title":"A wearable 1-lead necklace ECG for continuous heart rate monitoring","authors":"A. A. Iskandar, Reiner Kolla, K. Schilling, W. Völker","doi":"10.1109/HealthCom.2016.7749480","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749480","url":null,"abstract":"Nowadays, people are getting more and more concern on their own fitness conditions and it has become a digital healthy lifestyle movement. A basic activity tracker has given many data on the user's movement to fulfill their daily goal of calories burned. An advance tracker can also measure heart rate with accurate activity intense level. However, consider if these fitness devices can be used for clinical diagnosis by adding an ECG, then it can be a daily health monitoring or a health assisted device by connecting it to the internet via a smartphone. To use an ECG as a wearable device, the electrode positions has to fulfill the clinical placement. A new biomedical electrodes placement is proposed in this paper to meet the practicality of a fitness lifestyle device but has a medical ECG result for continuous heart monitoring in a form of a necklace. The device has a single lead ECG analog front end that is connected to an ARM-Cortex M4 microcontroller. It uses a 4 GB memory card, rechargeable battery, and a Bluetooth Low Energy 4.0 to communicate with an Android 4.3 smartphone. The test results were taken from a 32-year-old male subject with normal heart condition. The signal acquired from the electrode placement at the backside of the neck shows Lead I waveform with 10% from the normal position amplitude value. The R-wave of every heartbeat can be seen for heart rate calculation. Therefore, it is able to do a daily heart monitoring with a lifestyle device.","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"134 7 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":"124257716","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}
Sanjay P. Nair, Matin Kheirkhahan, A. Davoudi, Parisa Rashidi, A. Wanigatunga, D. Corbett, T. Manini, Sanjay Ranka
{"title":"ROAMM: A software infrastructure for real-time monitoring of personal health","authors":"Sanjay P. Nair, Matin Kheirkhahan, A. Davoudi, Parisa Rashidi, A. Wanigatunga, D. Corbett, T. Manini, Sanjay Ranka","doi":"10.1109/HEALTHCOM.2016.7749479","DOIUrl":"https://doi.org/10.1109/HEALTHCOM.2016.7749479","url":null,"abstract":"Mobile health (mHealth) based on smartphone and smartwatch technology is changing the landscape for how patients and research participants communicate about their health in real time. Flexible control of the different interconnected and frequently communicating mobile devices can provide a rich set of health care applications that can adapt dynamically to their environment. In this paper, we propose a real-time online activity and mobility monitoring (ROAMM) framework consisting of a smart-watch application for data collection, a server for data storage and retrieval as well as online monitoring and administrative tasks. We evaluated this framework to collect actigraphy data on the wrist and used it for feature detection and classification of different tasks of daily living conducted by participants. The information retrieved from the smartwatches yielded high accuracy for sedentary behavior prediction (accuracy = 97.44%) and acceptable performance for activity intensity level estimation (rMSE = 0.67 and R2 = 0.52).","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"52 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":"131042773","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}
{"title":"Muscular activation intervals detection using gaussian mixture model GMM applied to sEMG signals","authors":"Amal Naseem, M. Jabloun, P. Ravier, O. Buttelli","doi":"10.1109/HealthCom.2016.7749430","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749430","url":null,"abstract":"We propose to apply the Gaussian Mixture Model (GMM) to surface electromyography (sEMG) signals in order to detect the muscular activation (MA) onset, timing off and intervals. First, classical time and frequency features are extracted from the sEMG signals, beside the Teager-Kaiser energy operator (TKEO) is evaluated and added as a new feature which enhances the detection performance. All the obtained features are then used as the input for the GMM to conduct the binary clustering. Finally, a decision theory is applied in order to declare sEMG activation timing of human skeletal museles during movement. Accuracy and precision of the algorithm are assessed by using a set of synthetic simulated sEMG signals and real ones. A comparison with two previously published techniques is conducted: wavelet transform-based method and double threshold-based method. Our experimental results prove that the proposed GMM-based algorithm is able to accurately reveal the MA timing with performance beyond that of the state-of-the-art methods. Moreover, this proposed algorithm is automatic and user-independent.","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"24 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":"129960080","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}
{"title":"Midwifery E-Health: From design to validation of “Mammastyle — Gravidanza Fisiologica”","authors":"G. Tommasone, M. Bazzani, V. Solinas, P. Serafini","doi":"10.1109/HealthCom.2016.7749499","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749499","url":null,"abstract":"Health education for pregnant women is one of the aims chased by using mobile applications for health prevention and healthy lifestyle promotion. The literature claims the lack of mobile applications with evidence-based information and supported by health professionals. This paper deals with the design, the implementation, and the evaluation of a service for healthy women with a normal pregnancy aimed at promoting healthy lifestyles, monitoring the eating habits and the physical activity, for educational purposes and overweight and obesity prevention in pregnancy.","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"317 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":"122151780","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}