Development of a ubiquitous healthcare monitoring system combined with non-conscious and ambulatory physiological measurements and its application to medical care

The demand for ubiquitous healthcare monitoring has been increasingly raised for prevention of lifestyle-related diseases, acute life support or chronic therapies for inpatients and/or outpatients having chronic disorder and home medical care. From these view points, we developed a non-conscious healthcare monitoring system without any attachment of biological sensors and operations of devices, and an ambulatory postural changes and activities monitoring system. Furthermore in this study, in order to investigate those applicability to the ubiquitous healthcare monitoring, we have developed a new healthcare monitoring system combined with the non-conscious and the ambulatory measurements developed by us. In patients with chronic cardiovascular disease or stroke, the daily health conditions such as pulse, respiration, activities and so on, could be continuously measured in the hospital, the rehabilitation room and subject\u27s own home, using the present system. The results demonstrated that the system appears useful for the ubiquitous healthcare monitoring not only at medical facility, but also during daily living at home. © 2011 IEEE

Development of a ubiquitous healthcare monitoring system combined with non-conscious and ambulatory physiological measurements and its application to medical care.

The demand for ubiquitous healthcare monitoring has been increasingly raised for prevention of lifestyle-related diseases, acute life support or chronic therapies for inpatients and/or outpatients having chronic disorder and home medical care. From these view points, we developed a non-conscious healthcare monitoring system without any attachment of biological sensors and operations of devices, and an ambulatory postural changes and activities monitoring system. Furthermore in this study, in order to investigate those applicability to the ubiquitous healthcare monitoring, we have developed a new healthcare monitoring system combined with the non-conscious and the ambulatory measurements developed by us. In patients with chronic cardiovascular disease or stroke, the daily health conditions such as pulse, respiration, activities and so on, could be continuously measured in the hospital, the rehabilitation room and subject\u27s own home, using the present system. The results demonstrated that the system appears useful for the ubiquitous healthcare monitoring not only at medical facility, but also during daily living at home

In the spotlight: Bioinstrumentation

金沢大学理工研究域機械工学

Distributed Computing and Monitoring Technologies for Older Patients

This book summarizes various approaches for the automatic detection of health threats to older patients at home living alone. The text begins by briefly describing those who would most benefit from healthcare supervision. The book then summarizes possible scenarios for monitoring an older patient at home, deriving the common functional requirements for monitoring technology. Next, the work identifies the state of the art of technological monitoring approaches that are practically applicable to geriatric patients. A survey is presented on a range of such interdisciplinary fields as smart homes, telemonitoring, ambient intelligence, ambient assisted living, gerontechnology, and aging-in-place technology. The book discusses relevant experimental studies, highlighting the application of sensor fusion, signal processing and machine learning techniques. Finally, the text discusses future challenges, offering a number of suggestions for further research directions

Wearables for independent living in older adults: Gait and falls

Solutions are needed to satisfy care demands of older adults to live independently. Wearable technology (wearables) is one approach that offers a viable means for ubiquitous, sustainable and scalable monitoring of the health of older adults in habitual free-living environments. Gait has been presented as a relevant (bio)marker in ageing and pathological studies, with objective assessment achievable by inertial-based wearables. Commercial wearables have struggled to provide accurate analytics and have been limited by non-clinically oriented gait outcomes. Moreover, some research-grade wearables also fail to provide transparent functionality due to limitations in proprietary software. Innovation within this field is often sporadic, with large heterogeneity of wearable types and algorithms for gait outcomes leading to a lack of pragmatic use. This review provides a summary of the recent literature on gait assessment through the use of wearables, focusing on the need for an algorithm fusion approach to measurement, culminating in the ability to better detect and classify falls. A brief presentation of wearables in one pathological group is presented, identifying appropriate work for researchers in other cohorts to utilise. Suggestions for how this domain needs to progress are also summarised

Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

Enhancing physical activity coaching through personalized motivational strategies and self-adaptive goal-setting: development of self-adaptive processes in a monitoring and coaching smartphone application

Dissertação para obtenção do Grau de Mestre em Engenharia BiomédicaThe rising age of the European population brings increased costs in healthcare mainly related to the management of chronic diseases. Regular physical activity has been shown to help in the prevention and control of disease risk. Mobile phones have provided promising and emergent ways of promoting healthy lifestyles, allowing real-time monitoring and coaching to be delivered at any time and any place. The presented work adds new features to the Activity Coach, an ambulatory feedback system that aims to encourage physical activity. The Integral of the Modulus of Body Acceleration (IMA) is the unit used as an estimate for energy expenditure. Although previous research demonstrated the potential of this system, results also showed that adherence drops after a few weeks. The primary goal of this research was to design, implement, and test adaptive goal-setting and personalized feedback strategies in order to encourage physical activity. Regarding the self-adaptive goal-setting feature, the goal for each day is defined automatically based on the physical activity performed at that day of the week since the beginning of the intervention. Hence, the intention is to help the user to increase or maintain his level of physical activity taking his daily routine as a reference. The second element added to the system regards motivational feedback messages, a key factor in interventions aiming at behavior change. Based on the levels of self-efficacy, stage-of-change, and daily activity, the user is assigned to one of the six pre-defined feedback strategies in the system. The content of the motivational cues depends on the selected feedback strategy. The evaluation of the system focused on providing better understandable and more accurate feedback to the user. To do so, we evaluated the challenge and attainability of the goals provided to the user with (1) data acquired during previous studies, and (2) newly gathered data from a single-subject study. As part of the evaluation, we translated IMA counts into ‘steps’, a commonly understandable measure for physical activity, comparing the data acquired from the Activity Coach sensor with a Fitbit, a commercially available pedometer. Although further tests with more subjects and different activities should be performed, we suggest that the default values set to the system are in agreement with the Goal-Setting Theory providing challenging and attainable goals. The results from this research will be used in future experiments using the Activity Coach and can be adapted to other ambulatory feedback systems regarding promotion of physical activity

Mobile Health Technologies

Mobile Health Technologies, also known as mHealth technologies, have emerged, amongst healthcare providers, as the ultimate Technologies-of-Choice for the 21st century in delivering not only transformative change in healthcare delivery, but also critical health information to different communities of practice in integrated healthcare information systems. mHealth technologies nurture seamless platforms and pragmatic tools for managing pertinent health information across the continuum of different healthcare providers. mHealth technologies commonly utilize mobile medical devices, monitoring and wireless devices, and/or telemedicine in healthcare delivery and health research. Today, mHealth technologies provide opportunities to record and monitor conditions of patients with chronic diseases such as asthma, Chronic Obstructive Pulmonary Diseases (COPD) and diabetes mellitus. The intent of this book is to enlighten readers about the theories and applications of mHealth technologies in the healthcare domain

Non-invasive wearable sensing systems for continuous health monitoring and long-term behavior modeling

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2006.Includes bibliographical references (p. 212-228).Deploying new healthcare technologies for proactive health and elder care will become a major priority over the next decade, as medical care systems worldwide become strained by the aging populations. This thesis presents LiveNet, a distributed mobile system based on low-cost commodity hardware that can be deployed for a variety of healthcare applications. LiveNet embodies a flexible infrastructure platform intended for long-term ambulatory health monitoring with real-time data streaming and context classification capabilities. Using LiveNet, we are able to continuously monitor a wide range of physiological signals together with the user's activity and context, to develop a personalized, data-rich health profile of a user over time. Most clinical sensing technologies that exist have focused on accuracy and reliability, at the expense of cost-effectiveness, burden on the patient, and portability. Future proactive health technologies, on the other hand, must be affordable, unobtrusive, and non-invasive if the general population is going to adopt them.(cont.) In this thesis, we focus on the potential of using features derived from minimally invasive physiological and contextual sensors such as motion, speech, heart rate, skin conductance, and temperature/heat flux that can be used in combination with mobile technology to create powerful context-aware systems that are transparent to the user. In many cases, these non-invasive sensing technologies can completely replace more invasive diagnostic sensing for applications in long-term monitoring, behavior and physiology trending, and real-time proactive feedback and alert systems. Non-invasive sensing technologies are particularly important in ambulatory and continuous monitoring applications, where more cumbersome sensing equipment that is typically found in medical and clinical research settings is not usable. The research in this thesis demonstrates that it is possible to use simple non-invasive physiological and contextual sensing using the LiveNet system to accurately classify a variety of physiological conditions. We demonstrate that non-invasive sensing can be correlated to a variety of important physiological and behavioral phenomenon, and thus can serve as substitutes to more invasive and unwieldy forms of medical monitoring devices while still providing a high level of diagnostic power.(cont.) From this foundation, the LiveNet system is deployed in a number of studies to quantify physiological and contextual state. First, a number of classifiers for important health and general contextual cues such as activity state and stress level are developed from basic non-invasive physiological sensing. We then demonstrate that the LiveNet system can be used to develop systems that can classify clinically significant physiological and pathological conditions and that are robust in the presence of noise, motion artifacts, and other adverse conditions found in real-world situations. This is highlighted in a cold exposure and core body temperature study in collaboration with the U.S. Army Research Institute of Environmental Medicine. In this study, we show that it is possible to develop real-time implementations of these classifiers for proactive health monitors that can provide instantaneous feedback relevant in soldier monitoring applications. This thesis also demonstrates that the LiveNet platform can be used for long-term continuous monitoring applications to study physiological trends that vary slowly with time.(cont.) In a clinical study with the Psychiatry Department at the Massachusetts General Hospital, the LiveNet platform is used to continuously monitor clinically depressed patients during their stays on an in-patient ward for treatment. We show that we can accurately correlate physiology and behavior to depression state, as well as to track changes in depression state over time through the course of treatment. This study demonstrates how long-term physiology and behavioral changes can be captured to objectively measure medical treatment and medication efficacy. In another long-term monitoring study, the LiveNet platform is used to collect data on people's everyday behavior as they go through daily life. By collecting long-term behavioral data, we demonstrate the possibility of modeling and predicting high-level behavior using simple physiologic and contextual information derived solely from ambulatory mobile sensing technology.by Michael Sung.Ph.D