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.

/pdf/a-survey-on-wireless-body-area-networks-f3nx5ii4yv.pdf

A survey on wireless body area networks

In this paper, we present work in progress on VITAL-IN, a pervasive mobile application that aims to operationalize and assess multi-dimensional risk factors increasing a person’s chance of developing the burnout syndrome. To date, there are no conclusive scientific results of what causes burnout, yet some factors are evident. We propose VITAL-IN application, enabling the analysis of distributed, variable order, sensor input and ecological momentary self-assessment towards “just-in-time” inference of an individual’s behaviour and state, and future burnout risk prediction. Understanding the risk factors and the developmental trajectories leading to burnout could facilitate its early recognition and help to determine the most effective strategies and the most appropriate time for prevention and intervention efforts.

/pdf/towards-smartphone-based-assessment-of-burnout-195im9178x.pdf

Towards Smartphone-Based Assessment of Burnout

As mobile devices pave the way for mobile healthcare services that put long-distance patients at the helm of their own diagnoses and treatment, its important to evalue user requirements and expectations.

Long distance care

Background Heart rate (HR), especially at nighttime, is an important biomarker for cardiovascular health. It is known to be influenced by overall physical fitness, as well as daily life physical or psychological stressors like exercise, insufficient sleep, excess alcohol, certain foods, socialization, or air travel causing physiological arousal of the body. However, the exact mechanisms by which these stressors affect nighttime HR are unclear and may be highly idiographic (i.e. individual-specific). A single-case or “n-of-1” observational study (N1OS) is useful in exploring such suggested effects by examining each subject's exposure to both stressors and baseline conditions, thereby characterizing suggested effects specific to that individual. Objective Our objective was to test and generate individual-specific N1OS hypotheses of the suggested effects of daily life stressors on nighttime HR. As an N1OS, this study provides conclusions for each participant, thus not requiring a representative population. Methods We studied three healthy, nonathlete individuals, collecting the data for up to four years. Additionally, we evaluated model-twin randomization (MoTR), a novel Monte Carlo method facilitating the discovery of personalized interventions on stressors in daily life. Results We found that physical activity can increase the nighttime heart rate amplitude, whereas there were no strong conclusions about its suggested effect on total sleep time. Self-reported states such as exercise, yoga, and stress were associated with increased (for the first two) and decreased (last one) average nighttime heart rate. Conclusions This study implemented the MoTR method evaluating the suggested effects of daily stressors on nighttime heart rate, sleep time, and physical activity in an individualized way: via the N-of-1 approach. A Python implementation of MoTR is freely available.

https://journals.sagepub.com/doi/pdf/10.1177/20552076221120725

What possibly affects nighttime heart rate? Conclusions from N-of-1 observational data

Localization has been an important research issue in ubiquitous computing and wireless sensor networks (WSNs). From location-based services to autonomous mobile devices, location is prevalent in a wide range of applications. Localization methods for a mobile node vary from using GPS to localizing with respect to beacons with known locations, or using sensors like accelerometers and compasses. All of these solutions either require additional sensors in a node that reduce its battery life or require some kind of infrastructure and access to a database of anchor locations. Additionally, most solutions are for networks where nodes can communicate indiscriminately with each other. In this paper we introduce ReNLoc, a minimalistic anchor-free multilateration algorithm for 2D space (extendable to 3D space) with a centralized and a distributed version made for networks where the mobile nodes can only get range measurements to nodes with an unknown but fixed position that we call base nodes. The main assumption is that there is a minimum of three base nodes. ReNLoc takes advantage of geometric constraints that arise from the range measurements and represents them as sets on which we perform minimization over known geometric properties. We show that ReNLoc outperforms the commonly used multidimensional scaling (MDS) algorithm in a purely indirect ranging setup.

ReNLoc: An anchor-free localization algorithm for indirect ranging

Understanding own body performance and factors influencing it is a challenge, especially because the complex environments we live in influence this performance, and, when relying only on our subjective, momentary observations and feelings, we may easily draw wrong conclusions. Fortunately, nowadays we can access devices and services for self-tracking of own body and its performance in context. In this paper I present an example of own idiographic study of self-tracking of heart rate (HR) and daily life activities along a period of 3 consecutive months. I present the study challenges, as well as I discuss the lessons learned from self-tracking for self-management of my health. I discuss my aim of generalization of such a study approach towards a larger population.

/pdf/beat-by-beat-getting-fit-leveraging-pervasive-self-tracking-2zsg2ecqwy.pdf

Katarzyna Wac

Papers

Towards Smartphone-Based Assessment of Burnout

Long distance care

What possibly affects nighttime heart rate? Conclusions from N-of-1 observational data

ReNLoc: An anchor-free localization algorithm for indirect ranging

Beat-by-beat getting fit : leveraging pervasive self-tracking of heart rate in self-management of health