The Transmission Control Protocol.

Recent developments and technological advancements in wireless communication, MicroElectroMechanical Systems (MEMS) technology and integrated circuits has enabled low-power, intelligent, miniaturized, invasive/non-invasive micro and nano-technology sensor nodes strategically placed in or around the human body to be used in various applications, such as personal health monitoring. This exciting new area of research is called Wireless Body Area Networks (WBANs) and leverages the emerging IEEE 802.15.6 and IEEE 802.15.4j standards, specifically standardized for medical WBANs. The aim of WBANs is to simplify and improve speed, accuracy, and reliability of communication of sensors/actuators within, on, and in the immediate proximity of a human body. The vast scope of challenges associated with WBANs has led to numerous publications. In this paper, we survey the current state-of-art of WBANs based on the latest standards and publications. Open issues and challenges within each area are also explored as a source of inspiration towards future developments in WBANs.

Wireless Body Area Networks: A Survey

In this Chapter, we imagine a decision maker (or a group of experts) trying to establish or examine fair procedures to combine opinions about alternatives related to different points of view.

Multiple criteria decision making

福祉関連職におけるMaslach Burnout Inventoryの因子構造の比較

The increasing use of wireless networks and the constant miniaturization of electrical devices has empowered the development of Wireless Body Area Networks (WBANs). In these networks various sensors are attached on clothing or on the body or even implanted under the skin. The wireless nature of the network and the wide variety of sensors offer numerous new, practical and innovative applications to improve health care and the Quality of Life. The sensors of a WBAN measure for example the heartbeat, the body temperature or record a prolonged electrocardiogram. Using a WBAN, the patient experiences a greater physical mobility and is no longer compelled to stay in the hospital. This paper offers a survey of the concept of Wireless Body Area Networks. First, we focus on some applications with special interest in patient monitoring. Then the communication in a WBAN and its positioning between the different technologies is discussed. An overview of the current research on the physical layer, existing MAC and network protocols is given. Further, cross layer and quality of service is discussed. As WBANs are placed on the human body and often transport private data, security is also considered. An overview of current and past projects is given. Finally, the open research issues and challenges are pointed out.

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A survey on wireless body area networks

Ubicomp/HCI researchers are increasingly using smartphones to collect human-labelled data 'in the wild'. While this allows for the collection of a wide range of interesting data in authentic settings and surroundings, humans are notoriously inconsistent in the quality of their contributions. Improving the quality of data collected with mobile devices is a largely unexplored, but highly relevant field. The primary objective of this workshop is to share insights, ideas, and discoveries on the quality of mobile human contributions. The work presented in the International Workshop on Mobile Human Contributions (MHC '18) explores methods, tools, and novel approaches towards increasing the reliability of human data submissions with mobile devices.

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MHC '18: International Workshop on Mobile Human Contributions: Opportunities and Challenges

In the estimation of selectivity, many models assume that data is uniformly distributed, which is not true for many applications. In this paper, we discuss a generalized selectivity model, the so-called lαβ-model which is independent of the data distribution. The model predicts the fraction of a relation that should be selected in order to process a query. We have evaluated this model for different data distributions in order to determine the accuracy of this model. Data distributions that have been considered are the uniform distribution, the normal distribution, the exponential distribution, Pearson’s distribution, and Zipf’s distribution. From our experiments, it appears that the lαβ-model predicts the selectivity well, especially for the skewed distributions. Applying the lαβ-model on different fragment sizes of a relation yields quite acceptable selectivity values as well.

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A selectivity model for fragmented relations: Evaluated for different standard data distributions

Ventricular arrhythmia (VA) is a leading cause of sudden death and health deterioration. Recent advances in predictive analytics and wearable technology for behavior assessment show promise but require further investigation. Yet, previous studies have only assessed other health outcomes and monitored patients for short durations (7–14 days). This study explores how behaviors reported by a consumer wearable can assist VA risk prediction. An exploratory observational study was conducted with participants who had an implantable cardioverter-defibrillator (ICD) and wore a Fitbit Alta HR consumer wearable. Fitbit reported behavioral markers for physical activity (light, fair, vigorous), sleep, and heart rate. A case-crossover analysis using conditional logistic regression assessed the effects of time-adjusted behaviors over 1–8 weeks on VA incidence. Twenty-seven patients (25 males, median age 59 years) were included. Among the participants, ICDs recorded 262 VA events during 8093 days monitored by Fitbit (median follow-up period 960 days). Longer light to fair activity durations and a higher heart rate increased the odds of a VA event (p < 0.001). In contrast, lengthier fair to vigorous activity and sleep durations decreased the odds of a VA event (p < 0.001). Future studies using consumer wearables in a larger population should prioritize these outcomes to further assess VA risk.

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Using Consumer-Wearable Activity Trackers for Risk Prediction of Life-Threatening Heart Arrhythmia in Patients with an Implantable Cardioverter-Defibrillator: An Exploratory Observational Study

There is substantial evidence on the relevant factors that motivate participation in human subject studies and the expectations of participants when sharing their health data for research. However, most human subject studies focus on participant eligibility and data collection, omitting even a rudimentary use of the factors that motivate participation. We illustrate an approach to use motivation to construct personalized stories and exemplify it by using a chatbot under development towards monitoring, analyzing, and influencing health study participation, engagement, and retention. Additionally, we discuss the new advantages, challenges, and unexplored avenues for research stemming from our approach.

Towards Personalizing Participation in Health Studies

Inevitably, healthcare goes pervasive, yet its many potential future scenarios are still to be defined. We employ foresight techniques to define some of these scenarios, as relevant for the current and future state of healthcare in Geneva, Switzerland. We teach the methodology to undergraduate business administration students- potential e.g., managers and policymakers in the future healthcare system of Geneva. Our objective is twofold: to train students at scenario building and to develop scenarios for pervasive healthcare technologies and their social implications. Results include scenarios developed by the students as well as lessons learned with respect to the power of foresight techniques employed with novices in this field.

Katarzyna Wac

Papers

MHC '18: International Workshop on Mobile Human Contributions: Opportunities and Challenges

A selectivity model for fragmented relations: Evaluated for different standard data distributions

Using Consumer-Wearable Activity Trackers for Risk Prediction of Life-Threatening Heart Arrhythmia in Patients with an Implantable Cardioverter-Defibrillator: An Exploratory Observational Study

Towards Personalizing Participation in Health Studies

A Foresight Analysis of Pervasive Healthcare Technologies