Ebook: pHealth 2013
Health technologies for personalized medicine have become important enablers for monitoring and treatment in both inpatient and outpatient care. The benefits of these technologies lead not only to improvements in medical services quality for all stakeholders, but also to new healthcare business models, promising a better containment of healthcare costs.
This book presents the proceedings of the 2013 pHealth Conference, held in Tallinn, Estonia, in June 2013. The pHealth conferences have established themselves as the leading international conference series on wearable or implantable micro and nanotechnologies for personalized medicine and health service provision. pHealth 2013 proceeds in bringing together a dynamic emerging professional community from Europe and beyond. The keynotes, invited speeches and oral presentations in this book address these wearable technologies, and also other topics such as health games, terminologies and ontologies, medical decision support, monitoring of environmental and living conditions, as well as social and ethical issues.
We are at the beginning of what promises to be revolutionary change in healthcare offering significant opportunities for both patients and healthcare providers. This book will therefore be of interest to the entire healthcare industry.
The pHealth 2013 Conference is the 10th in a series of scientific events bringing together expertise from medical, technological, political, administrative, and social domains related to the provision of personalized health services. Aspects such as health games, terminologies and ontologies, medical decision support, the monitoring of environmental and living conditions of citizens, as well as social and ethical issues, are addressed by almost 40 speakers from various parts of the world. Keynotes, invited talks, and oral presentations discuss foundations and principles as well as requirements and solutions for pHealth. European success stories and national pilots, as well as innovations and industrial products enabling the paradigm shift towards personalized health, form the scientific program, together with demonstrations of existing and emerging applications in the pHealth domain. Presentations will be complemented by panels, workshops dedicated to specific challenges, and updates on road-mapping activities focusing on the year 2020.
In recent years, the pHealth conferences have emerged as the leading international conference series on wearable micro and nano technologies for personalized medicine and personalized health service provision. Since 2003, pHealth has become increasingly recognized for attracting acknowledged scientists and experts in the domains of relevant technologies: medical doctors, policy makers from academic institutions, hospital administrations, governmental and regulatory bodies, the healthcare industry and allied professions. Bringing together the experience of a dynamically emerging professional community from Europe and beyond, the pHealth series of events has made visible the tremendous potential of micro and nano technologies, not only for the future of medicine, but also for the improvement of healthcare and welfare processes today and tomorrow, thereby helping to integrate health and social care.
Microsystems, smart textiles, telemedicine, mobile computing, smart implants, sensor-controlled medical devices, and innovative sensor and actuator principles and techniques as well as related body, local and wide area networks – up to Cloud services – have become important enablers not only for monitoring, diagnosis, and treatment in both inpatient and outpatient care, but also for preventive and predictive medicine. This is, however, just the beginning of evolutionary and revolutionary changes, paradigm shifts, and the respective significant opportunities for patients, citizens, health professionals, healthcare establishments, and companies engaged in the micro and nano technologies. This will indeed be of interest to the entire healthcare industry. The multilateral benefits of the full gamut of enabling pHealth technologies will lead to a win-win situation with enormous potential for all the stakeholders involved, not only in terms of improved medical quality and industrial competitiveness, but also with increasing access to care coupled with manageable healthcare costs.
The pHealth 2013 Conference covers existing and emerging technologies, looks at the outcomes of successful European projects in various domains and addresses topics beyond the traditional scope of clinical, primary care and public health. Presentations range from latest developments, through running projects and initiatives, to lessons learned from keeping project results sustainable.
The pHealth 2013 Conference benefits gratefully from the experience of and the lessons learned by the organizing committees of previous pHealth events, particularly 2009 in Oslo, 2010 in Berlin, 2011 in Lyon, and 2012 in Porto. The 2009 conference introduced the interesting idea of having special sessions focusing on a particular topic organized by a mentor/moderator. The Berlin event in 2010 initiated workshops on particular topics prior to the official start of the conference. Lyon in 2011 saw the launch of so-called dynamic demonstrations, allowing the participants to demonstrate software and hardware solutions on the fly without the need for a booth. Implementing pre-conference events, pHealth 2012 in Porto gave attendees a platform for presenting and discussing recent developments and provocative ideas which helped to animate the sessions.
The pHealth 2013 Conference in Tallinn combines the presentation of emerging principles, future visions and views with a careful analysis of lessons learned from international and national R&D activities and practical solutions. A special focus is given to the newest paradigm changes and challenges coming up within Big Data, Analytics, Translational and Nano Medicine, etc. In this context, the three Working Groups – “Electronic Health Records (EHR)”, “Personal Portable Devices (PPD)” and “Security, Safety and Ethics (SSE)” – of the European Federation for Medical Informatics (EFMI), all actively involved in the preparation and realization of pHealth 2013, have organized a pre-conference workshop dedicated to “Reuse and Secondary Use of Health Data – Interoperability, Privacy and Security Challenges”. Another pre-conference panel session addresses “Gamification and Health”.
This proceedings volume covers keynotes and invited speeches as well as oral presentations selected from submissions to the pHealth 2013 conference. All submissions have been carefully and critically reviewed. The editors are indebted to the expertise of the reviewers, which has undoubtedly contributed greatly to the quality of the conference and the book at hand.
Neither the pHealth 2013 Conference nor the publication of the pHealth 2013 proceedings by IOS Press would have been possible without the support of the sponsors: Estonian Health Tech Cluster and its partner organizationsmost notably ELIKO Competence Center for Electronics and ICT, and European Federation for Medical Informatics (EFMI). Supporters who have contributed their time and effort to the success of the conference also include Fraunhofer Institute for Digital Media Technologies IDMT – Germany and ITS Norway.
The editors are also grateful for the dedicated efforts of the Local Organizing Committee, chaired by Liisa Parv, whose members and helpers have ensured the smooth operation of the conference. They would especially like to thank Indrek Ruiso, Mike Tiffin, Riin Ehin, and Tõnis Allik from Estonian HealthTech Cluster and its Board for their continuous involvement in the preparation and realization of the conference.
Bernd Blobel, Peter Pharow and Liisa Parv
The fast developments in information and communication technology as well as R&D work on micro and nano systems in biology and biomedical engineering are stimulating the explosive growth in life sciences, which is leading to an ever increasing understanding of life at sub-cellular and molecular level and so revolutionizing and personalizing diagnosis and therapy. By bringing these parallel developments to biomedicine and health, ultrafast and sensitive systems can be developed to prevention & lifestyle support, to early diagnose or treat diseases with high accuracy and less invasiveness, and to support body functions or to replace lost functionality. Such pHealth systems will enable the delivery of individualized health services with better access & outcomes at lower costs than previously deemed possible, making a substantial contribution to bring healthcare expenditures under control and increase its productivity.
For improving quality, safety and efficiency of care processes, health care systems perform two paradigm changes: the organizational transformation of the health care system from organization-centric to person-centric structures and the methodological transformation from the traditional phenomenological approach to individualized health care based on translational medicine. Both paradigm changes are interrelated and require advanced interoperability between different organizations and multiple disciplines. The paper presents a system-theoretical, architecture-centric approach to analyze, design and develop the systems of health care and medicine for enabling personalized health services. According to the translational medicine paradigm, the considered model must be able to describe the subject of care at all levels of granularity from elements to population including the technologies applied at those levels to perform diagnosis and therapy. The system components reflected through different domains, their concepts and interrelations must be consistently described based on the domain ontologies representing those system perspectives. The medical and technological instances of the personalized health system are exemplified, thereby especially focusing on nano and micro levels and discussing biological and technical sensors and actuators, but also addressing profiling, bridging between genotyping and phenotyping and thereby combining molecular and epidemiological studies.
Mobile devices are becoming more and more important for services offered either directly to individuals, or indirectly as part of a therapeutic or rehabilitation procedure. Representing the work of the EFMI WG “Personal Portable Devices”, this paper offers an introduction to some of the most important technical and privacy-related challenges that arise when introducing mobile sensor or actuator devices (and networks) into health care, wellness, and fitness processes in order to exploit their capability to collect, record and process personal health data. Data processing can be viewed in three classes of application, namely processes for recommendations, decision support and decision making in personalized health and wellbeing service provision. This paper therefore briefly addresses aspects such as the Medical Device Directive for certifying devices in that respect. But regardless whether being MDD-compliant or not, each class of devices may have its own benefits and weaknesses in terms of enabling health-related personalized decisions.
This paper provides an introduction to some of the most important challenges that may occur when introducing the principle of Personal Portable Devices for providing information in terms of Big Data on the one hand, and the concept of the Virtual Physiological Human on the other. Both concepts can be applied to exploit their specific capability to collect and record personal health data of different levels of granularity into processes of personalized health service provision. The paper thus analyzes Big Data approaches and their capability to provide information for personalized service provision, and the same goes for the Virtual Physiological Human as such. But it is not only devices, concepts, models, and strategies that are involved in personalized health care as well as welfare and wellness service provision to human beings – it is the human being himself, too. This paper addresses technological and methodological aspects of using large amounts of data whereas another paper submitted to this conference will bring forward the aspects of applied sensor and device technology in relation to decision support and decision making for pHealth services.
Portable systems and global communications open a broad spectrum for new health applications. In the framework of electrophysiological applications, several challenges are faced when developing portable systems embedded in Cloud computing services. In order to facilitate new developers in this area based on our experience, five areas of interest are presented in this paper where strategies can be applied for improving the performance of portable systems: transducer and conditioning, processing, wireless communications, battery and power management. Likewise, for Cloud services, scalability, portability, privacy and security guidelines have been highlighted.
Decision-making is a crucial task for decision makers in healthcare, especially because decisions have to be made quickly, accurately and under uncertainty. Taking into account the importance of providing quality decisions, offering assistance in this complex process has been one of the main challenges of Artificial Intelligence throughout history. Decision Support Systems (DSS) have gained popularity in the medical field for their efficacy to assist decision-making. In this sense, many DSS have been developed, but only few of them consider processing and analysis of information contained in electronic health records, in order to identify individual or population health risk factors. This paper deals with Intelligent Decision Support Systems that are integrated into Electronic Health Records Systems (EHRS) or Public Health Information Systems (PHIS). It provides comprehensive support for a wide range of decisions with the purpose of improving quality of care delivered to patients or public health planning, respectively.
ICT innovations are constantly developed, and there is no lack of elderly customers, as the number of the elderly is dramatically increasing. Elderly are willing to use ICT to increase their own safety and social activity, but they need trust on the reliability, accessibility and other ethical aspects of ICT including the maintenance of privacy and self-determination. Ethical standards for ICT are usually not considered. “Ethicted” characterizes an ICT service or product as ethically evaluated. As a standardized procedure, it will not only increase the acceptability of ICT, but also provide services for ICT developers. In the future scenario, ICT under development should be evaluated by using a process model that is specifically built to find the lacks in ethical aspects. The model would then be tested by end-users, the formal and informal care givers, to receive direct feedback for redeveloping solutions. As final outcomes, there should be standards for ICT in elderly care and a service for ICT developers to utilize the evaluation model. This future scenario work included partners from 6 EU member countries. The combination of academic research and industrial/commercial interest of ICT developers should and can bring new value to assistive ICT for elderly care.
Miniaturized, noninvasive, wearable sensors constitute a fundamental prerequisite for pervasive, predictive, and preventive healthcare systems. In this sense, this paper presents the design, realization, and evaluation of a wireless multi-channel measurement system based on a cost-effective high-performance integrated circuit for electrical bioimpedance (EBI) measurements in the frequency range from 1 kHz to 1 MHz. The resulting on-chip spectrometer provides high measuring EBI capabilities and together with a low-cost, commercially available radio frequency transceiver device. It provides reliable wireless communication, constitutes the basic node to build EBI wireless sensor networks (EBI-WSNs)
Abnormal human behavior detection under free-living conditions is a reliable technique to detect activity disorders and diseases. This work proposes an acceleration-based algorithm to detect abnormal behavior as an abnormal increase or decrease in physical activity (PA). The algorithm is based on statistical features of human physical activity. Using a period of observed physical activity as a reference, the algorithm is able to detect abnormal behavior in other periods of time. The approach is unsupervised as the modeling of the reference behavior is not required. It has been validated with a group of 12 users under free-living conditions for two days. Results show a precision greater than 75% and a recall of 92%.
This paper describes CaptureMyEmotion, an app for smartphones and tablets which uses wireless sensors to capture physiological data together with facial expression recognition to provide a very personalized way to help autistic children identify and understand their emotions. Many apps are targeting autistic children and their carer, but none of the existing apps uses the full potential offered by mobile technology and sensors to overcome one of autistic children's main difficulty: the identification and expression of emotions. CaptureMyEmotion enables autistic children to capture photos, videos or sounds, and identify the emotion they felt while taking the picture. Simultaneously, a self-portrait of the child is taken, and the app measures the arousal and stress levels using wireless sensors. The app uses the self-portrait to provide a better estimate of the emotion felt by the child. The app has the potential to help autistic children understand their emotions and it gives the carer insight into the child's emotions and offers a means to discuss the child's feelings.
As the amount of data acquired from humans is constantly increasing, efficient tools are needed for extracting relevant information from this data. This paper presents a Matlab implementation of a method to classify and visually explore (highly) multi-variate patient data. The method uses the so-called Disease State Index (DSI) which measures the fit of a test subject's data to two classes present in the data (e.g. ‘controls’ and ‘positives’). DSI values of the different variables measured from a patient can be combined and visualized in a tree-like form using the Disease State Fingerprint (DSF) method. This allows a researcher to explore and understand the relevance of the different variables in classification problems. Moreover, the method is robust with respect to missing data. After giving an introduction to the DSF and DSI methods, the paper describes the steps required to use the methods and presents a MATLAB toolbox to perform these steps. To demonstrate the methods' versatility, the paper illustrates the usage of the toolbox in a few different contexts in which personal health data is to be classified. With this implementation, a powerful and flexible tool is made available to the biomedical research community.
Electroencephalogram (EEG) reflects the brain activity and is widely used in biomedical research. However, analysis of this signal is still a challenging issue. This paper presents a hybrid approach for assessing stress using the EEG signal. It applies Multivariate Multi-scale Entropy Analysis (MMSE) for the data level fusion. Case-based reasoning is used for the classification tasks. Our preliminary result indicates that EEG sensor based classification could be an efficient technique for evaluation of the psychological state of individuals. Thus, the system can be used for personal health monitoring in order to improve users health.
In this paper, we discuss the phototherapy concepts developed by Philips in the EU FP7 PLACE-it project. These concepts demonstrate the use of e-textiles for medical applications in a meaningful way. By introducing a comfortable, wearable technology, Philips has enabled a new world of devices which provide comfortable home treatment of different diseases and complaints. Here, we show concepts and clinical validation, and give insight in the development steps to be taken to build this kind of devices.
Pacemakers, ICDs, neurostimulators like deep brain stimulator electrodes, spiral cord stimulators, insulin pumps, cochlear implants, retinal implants, hearing aids, electro cardio gram (ECG) leads, or devices in interventional MRI such as vascular guide wires or catheters are affected by MRI magnetic and electromagnetic fields. Design of MRI Safe medical devices requires computer modeling, bench testing, phantom testing, and animal studies. Implanted medical devices can be MRI unsafe, MRI conditional or MRI safe (see glossary). In the following paragraphs we will investigate how to design implanted medical devices MRI safe.
The paper presents a non-invasive method and system for a long-term and continuous monitoring of the central aortic pressure (CAP) waveform and the augmentation index (AI). The CAP curve is estimated from the measured radial electrical bio-impedance (EBI) using spectral domain transfer functions (TF), which are established on the basis of data analysis during clinical experiments. Experiments were carried out on 3 volunteers by now. During the experiment, a 0.5 mg sublingual nitroglycerin tablet was administrated to each volunteer. Both, the reconstructed CAP curve and the AI have very good correlation with the results obtained by the SphygmoCor system. But, in opposite to the traditional tonometry based CAP curve and AI estimation methods, the proposed one is more convenient to use and allows continuous and long-term personalized monitoring of the CAP curve and of the AI.
The paper proposes a wearable multimodal data acquisition system for biological signals. The system enables logging of electrical bioimpedance signals from multiple electrodes, electrocardiographic signals (ECG), acceleration signals from multiple locations, and spirometric data from a moving object. Later it will be used to conduct field measurements for characterizing health of the object under investigation. Main goal is to acquire enough data for development, refinement, and simplification of signal processing algorithms. The system is center part of the new wearable compact data acquisition modules ZCardio. Those modules enable multichannel impedance spectroscopy by logging ECG signals and data from the spirometric sensor. Initial reference measurements were conducted. Alternatively, tests were performed using Plessey Semiconductors capacitive sensors. Acceleration signals are gathered.
Vibration exposure is a serious risk within work physiology for several work groups. Combined with cold artic climate, the risk for permanent harm is even higher. Equipment that can monitor the vibration exposure and warn the user when at risk will provide a safer work environment for these work groups. This study evaluates whether data from a wearable wireless multi-parameter sensor module can be used to estimate vibration exposure and exposure time. This work has been focused on the characterization of the response from the accelerometer in the sensor module and the optimal location of the module in the hand-arm configuration.
Despite the proliferation of low-cost wearable sensors, until recently we have not been able to monitor our brain activity in our daily lives with affordable and wearable devices. In this paper we provide an overview of the state-of-the-art of wearable electroencephalographic (EEG) devices after determining the key characteristics that enable wider adoption. Furthermore, we explore their potential applications, which suggest that wearable EEG devices can become a major enabler for personalized health. This review can serve as a reference on the state-of-the-art at the beginning of 2013.
Handheld technology finds slowly its place in the healthcare world. Some clinicians already use intensively dedicated mobile applications to consult clinical references. However, handheld technology hasn't still broadly embraced to the core of the healthcare business, the hospitals. The weak penetration of handheld technology in the hospitals can be partly explained by the caution of stakeholders that must be convinced about the efficiency of these tools before going forward. In a domain where temporal constraints are increasingly strong, caregivers cannot loose time on playing with gadgets. All users are not comfortable with tactile manipulations and the lack of dedicated peripheral complicates entering data for novices. Stakeholders must be convinced that caregivers will be able to master handheld devices. In this paper, we make the assumption that the proper design of an interface may influence users' performances to record information. We are also interested to find out whether users increase their efficiency when using handheld tools repeatedly. To answer these questions, we have set up a field study to compare users' performances on three different user interfaces while recording vital signs. Some user interfaces were familiar to users, and others were totally innovative. Results showed that users' familiarity with smartphone influences their performances and that users improve their performances by repeating a task.