Cross-Modal Health State Estimation

Individuals create and consume more diverse data about themselves today than any time in history. Sources of this data include wearable devices, images, social media, geospatial information and more. A tremendous opportunity rests within cross-modal data analysis that leverages existing domain knowledge methods to understand and guide human health. Especially in chronic diseases, current medical practice uses a combination of sparse hospital based biological metrics (blood tests, expensive imaging, etc.) to understand the evolving health status of an individual. Future health systems must integrate data created at the individual level to better understand health status perpetually, especially in a cybernetic framework. In this work we fuse multiple user created and open source data streams along with established biomedical domain knowledge to give two types of quantitative state estimates of cardiovascular health. First, we use wearable devices to calculate cardiorespiratory fitness (CRF), a known quantitative leading predictor of heart disease which is not routinely collected in clinical settings. Second, we estimate inherent genetic traits, living environmental risks, circadian rhythm, and biological metrics from a diverse dataset. Our experimental results on 24 subjects demonstrate how multi-modal data can provide personalized health insight. Understanding the dynamic nature of health status will pave the way for better health based recommendation engines, better clinical decision making and positive lifestyle changes.Comment: Accepted to ACM Multimedia 2018 Conference - Brave New Ideas, Seoul, Korea, ACM ISBN 978-1-4503-5665-7/18/1

Ontology-Based Reasoning for Educational Assistance in Noncommunicable Chronic Diseases

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MobiHealth-Innovative 2.5/3G mobile services and applications for health care

MobiHealth aims at introducing new mobile value added services in the area of healthcare, based on 2.5 (GPRS) and 3G (UMTS) technologies, thus promoting the use and deployment of GPRS and UMTS. This will be achieved by the integration of sensors and actuators to a Wireless Body Area Network (BAN). These sensors and actuators will continuously measure and transmit vital constants along with audio and video to health service providers and brokers, improving on one side the life of patients and allowing on the other side the introduction of new value-added services in the areas of disease prevention and diagnostic, remote assistance, para-health services, physical state monitoring (sports) and even clinical research. Furthermore, the MobiHealth BAN system will support the fast and reliable application of remote assistance in case of accidents by allowing the paramedics to send reliable vital constants data as well as audio and video directly from the accident site

A journey from Cure to Care- Wellness management for healthy lifestyle: Diabetes management a case study

Smart ubiquitous computing has a vital role to avoid and indicate the preventable lifestyle-based chronic diseases. It is focusing to adopt a healthy lifestyle by converging science and technology in this digital world for improving health and quality of life. From the last decade, the development of wellness applications has supported personalization and self-quantification. These applications facilitate the users through activity tracking and monitoring, based on the raw sensory data to adopt healthy behavior. The challenge of behavior change is not only to indicate the issues but also provides step-by-step coaching and guidance at real time. The realization of behavior change theories through digital technology has revolutionized the lifestyle change in a systematic and measurable manner. We have proposed a methodology to understand the behavior for generating just-in-time intervention for adopting a healthy lifestyle. Wellness platform based behavior analysis is performed using unbiased life-log and questionnaire for qualitative assessment of behavior. Behavior stage wise intervention is provided to adapt behavior for enhancing the quality of life and boost the socio-economic conditions. Personalized education is provided to understand the importance of healthy behavior and motivate the users, whereas just-in-time context-based recommendations have supported the stage-wise adaptation of unhealthy behavior. These capabilities require status evaluation of the activities and an efficient way to portray the comprehensive index of lifestyle habits. The real focus is to correlate the primarily linked habits in appropriate proportion through healthy behavior index (HBI) for personalized wellness support services. The healthy behavior index and behavior change theories through smart technologies

A Survey on Ubiquitous Healthcare Service Demand among Diabetic Patients

BackgroundAdvanced information technology can be used when developing diagnostic and treatment strategies to provide better care for diabetic patients. However, the levels of need and demand for the use of technological advances have not been investigated in diabetic patients. We proposed and developed an individualized, ubiquitous (U)-healthcare service using advanced information technology for more effective glucose control. Prior to our service initiation, we surveyed patient needs and other pertinent information.MethodsDuring August 2009, we conducted a 34-item questionnaire survey among patients with diabetes who were older than 40 years in two certain hospitals in Korea.ResultsThe mean age of the 228 participants was 61.2±9 years, and males made up 49.1% of the sample. Seventy-one percent replied that they wanted individualized healthcare service, and they also wanted their health information to be delivered through mobile devices such as a cellular phone or a personal digital assistant (40.4%). Most patients had never heard of U-healthcare services (81.1%); however, after explaining the concept, 71.1% of participants responded that they would use the service if it was provided. Despite their willingness, participants were concerned about technical difficulty in using the service (26.3%) as well as the cost of the service (29.8%).ConclusionThe current study suggests that more than 70% of diabetic patients are interested in using U-healthcare services. To encourage widespread use, the application program or device of U-healthcare services should be simple, easy to use and affordable while also including a policy for the protection of private information

Strategies Towards Chronic Disease Management via Medication Compliance

In the wake of the 21st century, healthcare systems around the globe are faced with an exponential rise in expenses, heavy utilization of services associated with a steep rise in aging population, and limited financial as well as human resources to manage the growing healthcare needs. A large percentage of chronic diseases deteriorate to the point where a crisis is reached resulting in unnecessary long-term hospitalization at massive cost to the healthcare sector. A critical inference drawn from epidemiological data and past studies is that preventing occurrences of acute episodes holds the key to providing quality healthcare, reducing incidences of prolonged hospitalizations and resultant healthcare expenses. This research is a work-in-progress that seeks to explore innovative strategies towards promoting medication compliance among chronic patients. This paper discusses the need for medication compliance and the cost of non-compliance to the healthcare sector. Salient behavioral, organizational, and technical research issues, opportunities, and challenges associated with promoting medication compliance via communication, computing, and sensing technologies are discussed. Potential benefits and costs of deploying innovative IT based medication compliance are also presented

Medical data processing and analysis for remote health and activities monitoring

Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions