Smart Health

Prolonged life expectancy along with the increasing complexity of medicine and health services raises health costs worldwide dramatically. Advancements in ubiquitous computing applications in combination with the use of sophisticated intelligent sensor networks may provide a basis for help. Whilst the smart health concept has much potential to support the concept of the emerging P4-medicine (preventive, participatory, predictive, and personalized), such high-tech medicine produces large amounts of high-dimensional, weakly-structured data sets and massive amounts of unstructured information. All these technological approaches along with “big data” are turning the medical sciences into a data-intensive science. To keep pace with the growing amounts of complex data, smart hospital approaches are a commandment of the future, necessitating context aware computing along with advanced interaction paradigms in new physical-digital ecosystems. In such a system the medical doctors are supported by their smart mobile medical assistants on managing their floods of data semi-automatically by following the human-in-the-loop concept. At the same time patients are supported by their health assistants to facilitate a healthier life, wellness and wellbeing.

Modern medicine and health care in all parts of our world are facing formidable challenges: exploding costs, finite resources, aging population as well as deluge of big complex, high-dimensional data sets produced by modern biomedical science, which exceeds the absorptive capacity of human minds. Consequently, the question arises about whether and to what extent the advances of machine intelligence and computational power may be utilized to mitigate the consequences. After prevailing over humans in chess and popular game shows, it is postulated that the biomedical field will be the next domain in which smart computing systems will outperform their human counterparts. In this overview we examine this hypothesis by comparing data formats, data access and heuristic methods used by both humans and computer systems in the medical decision making process. We conclude that the medical reasoning process can be significantly enhanced using emerging smart computing technologies and so-called computational intelligence. However, as humans have access to a larger spectrum of data of higher complexity and continue to perform essential components of the reasoning process more efficiently, it would be unwise to sacrifice the whole human practice of medicine to the digital world; hence a major goal is to mutually exploit the best of the two worlds: We need computational intelligence to deal with big complex data, but we nevertheless – and more than ever before – need human intelligence to interpret abstracted data and information and creatively make decisions.

This chapter outlines spatial health systems and discusses issues regarding their technical implementation and employment. This concerns in particular diseases which manifest themselves in the spatiotemporal behaviours of patients, showing patterns that enable conclusions about their underlying well-being. While a general overview is given, as an example the case of patients suffering from Alzheimer’s disease is examined more carefully in order to treat different aspects detailed enough. Especially, wearable and ambient technologies, activity recognition techniques as well as ethical aspects are discussed. The given literature review ranges from basic methods of Artificial Intelligence research to commercial products which are already available from the industry.

This paper provides an overview of current research and open problems in sensor-based mobility analysis. It is focused on geriatric assessment tests and the idea to provide easier and more objective results by using sensor technologies. A lot of research has been done in the field of measuring personal movement/mobility by technical approaches but there are few developments to measure a complete geriatric assessment test. Such automated tests can very likely offer more accurate, reliable and objective results than currently used methods. Additionally, those tests may reduce costs in public health systems as well as set standards for comparability of the tests. New sensor technologies and initiatives for data standardization in health processes offer increased possibilities in system development. This paper will highlight some open problems that still exist to bring automated mobility assessment tests into pervasive clinical and domestic use.

It is a well-known fact that exercising helps people improve their overall well-being; both physiological and psychological health. Regular moderate physical activity improves the risk of disease progression, improves the chances for successful rehabilitation, and lowers the levels of stress hormones. Physical fitness can be categorized in cardiovascular fitness, and muscular strength and endurance. A proper balance between aerobic activities and strength exercises are important to maximize the positive effects. This balance is not always easily obtained, so assistance tools are important. Hence, ambient assisted living (AAL) systems that support and motivate balanced training are desirable. This chapter presents methods to provide this, focusing on the methodologies and concepts implemented by the authors in the physical activity monitoring for aging people (PAMAP) platform. The chapter sets the stage for an architecture to provide personalized activity monitoring using a network of wearable sensors, mainly inertial measurement units (IMU). The main focus is then to describe how to do this in a personalizable way: (1) monitoring to provide an estimate of aerobic activities performed, for which a boosting based method to determine activity type, intensity, frequency, and duration is given; (2) supervise and coach strength activities. Here, methodologies are described for obtaining the parameters needed to provide real-time useful feedback to the user about how to exercise safely using the right technique.

Human body has an interesting potential to provide energy to micro-electronic systems. There are several techniques that can harvest energy from human body and convert it in energy to be used by electronic systems. Usually this energy cannot be used immediately and needs to be conditioned. This chapter summarizes about current trends of energy storage systems. Techniques for extracting energy from human body, estimations and experimental results based on previous works are discussed. The merge of all the above mentioned concepts, providing a general idea to the reader about the state of the art in energy harvesting from human bodies.

In the framework of Ambient Assisted Living, home automation may be a solution for helping elderly people living alone at home. This study is part of the Sweet-Home project which aims at developing a new home automation system based on voice command to improve support and well-being of people in loss of autonomy. The goal of the study is vocal order recognition with a focus on two aspects: distance speech recognition and sentence spotting. Several ASR techniques were evaluated on a realistic corpus acquired in a 4-room flat equipped with microphones set in the ceiling. This distant speech French corpus was recorded with 21 speakers who acted scenarios of activities of daily living. Techniques acting at the decoding stage, such as our novel approach called Driven Decoding Algorithm (DDA), gave better speech recognition results than the baseline and other approaches. This solution which uses the two best SNR channels and a priori knowledge (voice commands and distress sentences) has demonstrated an increase in recognition rate without introducing false alarms. Generally speaking, a short overview allows then to outline the research challenges that speech technologies must take up for Ambient Assisted Living and Augmentative and Alternative Communication, and the current reseach avenues in this domain.

Our goal is to develop a system for coaching human motions (e.g., for rehabilitation and daily health maintenance). This paper focuses on how to coach a user so that his/her motion gets closer to the good template of a target motion. It is important to efficiently advise the user to emulate the crucial features that define the good template. The proposed system (1) automatically mines the crucial features of any kind of motion from a set of motion features and (2) gives the user feedback about how to modify the motion through an intuitive interface. The crucial features are mined by feature sparsification through binary classification between the samples of good and other motions. An interface for motion coaching is designed to give feedback via different channels (e.g., visually, aurally), depending on the type of error. To use the total system, all the user must do is just move and then get feedback on the motion. Following experimental results, open problems for future work are discussed.

The application of Knowledge Discovery and Data Mining approaches forms the basis of realizing the vision of Smart Hospitals. For instance, the automated creation of high-quality knowledge bases from clinical reports is important to facilitate decision making processes for clinical doctors. A subtask of creating such structured knowledge is entity disambiguation that establishes links by identifying the correct semantic meaning from a set of candidate meanings to a text fragment. This paper provides a short, concise overview of entity disambiguation in the biomedical domain, with a focus on annotated corpora (e.g. CalbC), term disambiguation algorithms (e.g. abbreviation disambiguation) as well as gene and protein disambiguation algorithms (e.g. inter-species gene name disambiguation). Finally, we provide some open problems and future challenges that we expect future research will take into account.

Diabetes mellitus (DM) is a growing global disease which highly affects the individual patient and represents a global health burden with financial impact on national health care systems. Type 1 DM can only be treated with insulin, whereas for patients with type 2 DM a wide range of therapeutic options are available. These options include lifestyle changes such as change of diet and an increase of physical activity, but also administration of oral or injectable antidiabetic drugs. The diabetes therapy, especially with insulin, is complex. Therapy decisions include various medical and life-style related information. Computerized decision support systems (CDSS) aim to improve the treatment process in patient’s self-management but also in institutional care. Therefore, the personalization of the patient’s diabetes treatment is possible at different levels. It can provide medication support and therapy control, which aid to correctly estimate the personal medication requirements and improves the adherence to therapy goals. It also supports long-term disease management, aiming to develop a personalization of care according to the patient’s risk stratification. Personalization of therapy is also facilitated by using new therapy aids like food and activity recognition systems, lifestyle support tools and pattern recognition for insulin therapy optimization. In this work we cover relevant parameters to personalize diabetes therapy, how CDSS can support the therapy process and the role of machine learning in this context. Moreover, we identify open problems and challenges for the personalization of diabetes therapy with focus on decision support systems and machine learning technology.

Biobanks are essential for the realization of P4-medicine, hence indispensable for smart health. One of the grand challenges in biobank research is to close the research cycle in such a way that all the data generated by one research study can be consistently associated to the original samples, therefore data and knowledge can be reused in other studies. A catalogue must provide the information hub connecting all relevant information sources. The key knowledge embedded in a biobank catalogue is the availability and quality of proper samples to perform a research project. Depending on the study type, the samples can reflect a healthy reference population, a cross sectional representation of a certain group of people (healthy or with various diseases) or a certain disease type or stage. To overview and compare collections from different catalogues, we introduce visual analytics techniques, especially glyph based visualization techniques, which were successfully applied for knowledge discovery of single biobank catalogues. In this paper, we describe the state-of-the art in the integration of biobank catalogues addressing the challenge of combining heterogeneous data sources in a unified and meaningful way, consequently enabling the discovery and visualization of data from different sources. Finally we present open questions both in data integration and visualization of unified catalogues and propose future research in data integration with a linked data approach and the fusion of multi level glyph and network visualization.