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

Interoperable services based on activity monitoring in ambient assisted living environments

Ambient Assisted Living (AAL) is considered as the main technological solution that will enable the aged and people in recovery to maintain their independence and a consequent high quality of life for a longer period of time than would otherwise be the case. This goal is achieved by monitoring human’s activities and deploying the appropriate collection of services to set environmental features and satisfy user preferences in a given context. However, both human monitoring and services deployment are particularly hard to accomplish due to the uncertainty and ambiguity characterising human actions, and heterogeneity of hardware devices composed in an AAL system. This research addresses both the aforementioned challenges by introducing 1) an innovative system, based on Self Organising Feature Map (SOFM), for automatically classifying the resting location of a moving object in an indoor environment and 2) a strategy able to generate context-aware based Fuzzy Markup Language (FML) services in order to maximize the users’ comfort and hardware interoperability level. The overall system runs on a distributed embedded platform with a specialised ceiling- mounted video sensor for intelligent activity monitoring. The system has the ability to learn resting locations, to measure overall activity levels, to detect specific events such as potential falls and to deploy the right sequence of fuzzy services modelled through FML for supporting people in that particular context. Experimental results show less than 20% classification error in monitoring human activities and providing the right set of services, showing the robustness of our approach over others in literature with minimal power consumption

Designing a gamified social platform for people living with dementia and their live-in family caregivers

In the current paper, a social gamified platform for people living with dementia and their live-in family caregivers, integrating a broader diagnostic approach and interactive interventions is presented. The CAREGIVERSPRO-MMD (C-MMD) platform constitutes a support tool for the patient and the informal caregiver - also referred to as the dyad - that strengthens self-care, and builds community capacity and engagement at the point of care. The platform is implemented to improve social collaboration, adherence to treatment guidelines through gamification, recognition of progress indicators and measures to guide management of patients with dementia, and strategies and tools to improve treatment interventions and medication adherence. Moreover, particular attention was provided on guidelines, considerations and user requirements for the design of a User-Centered Design (UCD) platform. The design of the platform has been based on a deep understanding of users, tasks and contexts in order to improve platform usability, and provide adaptive and intuitive User Interfaces with high accessibility. In this paper, the architecture and services of the C-MMD platform are presented, and specifically the gamification aspects. © 2018 Association for Computing Machinery.Peer ReviewedPostprint (author's final draft

How will the Internet of Things enable Augmented Personalized Health?

Internet-of-Things (IoT) is profoundly redefining the way we create, consume, and share information. Health aficionados and citizens are increasingly using IoT technologies to track their sleep, food intake, activity, vital body signals, and other physiological observations. This is complemented by IoT systems that continuously collect health-related data from the environment and inside the living quarters. Together, these have created an opportunity for a new generation of healthcare solutions. However, interpreting data to understand an individual's health is challenging. It is usually necessary to look at that individual's clinical record and behavioral information, as well as social and environmental information affecting that individual. Interpreting how well a patient is doing also requires looking at his adherence to respective health objectives, application of relevant clinical knowledge and the desired outcomes. We resort to the vision of Augmented Personalized Healthcare (APH) to exploit the extensive variety of relevant data and medical knowledge using Artificial Intelligence (AI) techniques to extend and enhance human health to presents various stages of augmented health management strategies: self-monitoring, self-appraisal, self-management, intervention, and disease progress tracking and prediction. kHealth technology, a specific incarnation of APH, and its application to Asthma and other diseases are used to provide illustrations and discuss alternatives for technology-assisted health management. Several prominent efforts involving IoT and patient-generated health data (PGHD) with respect converting multimodal data into actionable information (big data to smart data) are also identified. Roles of three components in an evidence-based semantic perception approach- Contextualization, Abstraction, and Personalization are discussed

Sensing behaviour in healthcare design

We are entering an era of distributed healthcare that should fit and respond to individual needs, behaviour and lifestyles. Designing such systems is a challenging task that requires continuous information about human behaviour on a large scale, for which pervasive sensing (e.g. using smartphones and wearables) presents exciting opportunities. While mobile sensing approaches are fuelling research in many areas, their use in engineering design remains limited. In this work, we present a collection of common behavioural measures from literature that can be used for a broad range of applications. We focus specifically on activity and location data that can easily be obtained from smartphones or wearables. We further demonstrate how these are applied in healthcare design using an example from dementia care. Comparing a current and proposed scenario exemplifies how integrating sensor-derived information about user behaviour can support the healthcare design goals of personalisation, adaptability and scalability, while emphasising patient quality of life

East Midlands Research into Ageing Network (EMRAN) Discussion Paper Series

Academic geriatric medicine in Leicester . There has never been a better time to consider joining us. We have recently appointed a Professor in Geriatric Medicine, alongside Tom Robinson in stroke and Victoria Haunton, who has just joined as a Senior Lecturer in Geriatric Medicine. We have fantastic opportunities to support students in their academic pursuits through a well-established intercalated BSc programme, and routes on through such as ACF posts, and a successful track-record in delivering higher degrees leading to ACL post. We collaborate strongly with Health Sciences, including academic primary care. See below for more detail on our existing academic set-up. Leicester Academy for the Study of Ageing We are also collaborating on a grander scale, through a joint academic venture focusing on ageing, the ‘Leicester Academy for the Study of Ageing’ (LASA), which involves the local health service providers (acute and community), De Montfort University; University of Leicester; Leicester City Council; Leicestershire County Council and Leicester Age UK. Professors Jayne Brown and Simon Conroy jointly Chair LASA and have recently been joined by two further Chairs, Professors Kay de Vries and Bertha Ochieng. Karen Harrison Dening has also recently been appointed an Honorary Chair. LASA aims to improve outcomes for older people and those that care for them that takes a person-centred, whole system perspective. Our research will take a global perspective, but will seek to maximise benefits for the people of Leicester, Leicestershire and Rutland, including building capacity. We are undertaking applied, translational, interdisciplinary research, focused on older people, which will deliver research outcomes that address domains from: physical/medical; functional ability, cognitive/psychological; social or environmental factors. LASA also seeks to support commissioners and providers alike for advice on how to improve care for older people, whether by research, education or service delivery. Examples of recent research projects include: ‘Local History Café’ project specifically undertaking an evaluation on loneliness and social isolation; ‘Better Visits’ project focused on improving visiting for family members of people with dementia resident in care homes; and a study on health issues for older LGBT people in Leicester. Clinical Geriatric Medicine in Leicester We have developed a service which recognises the complexity of managing frail older people at the interface (acute care, emergency care and links with community services). There are presently 17 consultant geriatricians supported by existing multidisciplinary teams, including the largest complement of Advance Nurse Practitioners in the country. Together we deliver Comprehensive Geriatric Assessment to frail older people with urgent care needs in acute and community settings. The acute and emergency frailty units – Leicester Royal Infirmary This development aims at delivering Comprehensive Geriatric Assessment to frail older people in the acute setting. Patients are screened for frailty in the Emergency Department and then undergo a multidisciplinary assessment including a consultant geriatrician, before being triaged to the most appropriate setting. This might include admission to in-patient care in the acute or community setting, intermediate care (residential or home based), or occasionally other specialist care (e.g. cardiorespiratory). Our new emergency department is the county’s first frail friendly build and includes fantastic facilities aimed at promoting early recovering and reducing the risk of hospital associated harms. There is also a daily liaison service jointly run with the psychogeriatricians (FOPAL); we have been examining geriatric outreach to oncology and surgery as part of an NIHR funded study. We are home to the Acute Frailty Network, and those interested in service developments at the national scale would be welcome to get involved. Orthogeriatrics There are now dedicated hip fracture wards and joint care with anaesthetists, orthopaedic surgeons and geriatricians. There are also consultants in metabolic bone disease that run clinics. Community work Community work will consist of reviewing patients in clinic who have been triaged to return to the community setting following an acute assessment described above. Additionally, primary care colleagues refer to outpatients for sub-acute reviews. You will work closely with local GPs with support from consultants to deliver post-acute, subacute, intermediate and rehabilitation care services. Stroke Medicine 24/7 thrombolysis and TIA services. The latter is considered one of the best in the UK and along with the high standard of vascular surgery locally means one of the best performances regarding carotid intervention

Future bathroom: A study of user-centred design principles affecting usability, safety and satisfaction in bathrooms for people living with disabilities

Research and development work relating to assistive technology 2010-11 (Department of Health) Presented to Parliament pursuant to Section 22 of the Chronically Sick and Disabled Persons Act 197

Multimodal Wearable Intelligence for Dementia Care in Healthcare 4.0: A Survey

As a new revolution of Ubiquitous Computing and Internet of Things, multimodal wearable intelligence technique is rapidly becoming a new research topic in both academic and industrial fields. Owning to the rapid spread of wearable and mobile devices, this technique is evolving healthcare from traditional hub-based systems to more personalised healthcare systems. This trend is well-aligned with recent Healthcare 4.0 which is a continuous process of transforming the entire healthcare value chain to be preventive, precise, predictive and personalised, with significant benefits to elder care. But empowering the utility of multimodal wearable intelligence technique for elderly care like people with dementia is significantly challenging considering many issues, such as shortage of cost-effective wearable sensors, heterogeneity of wearable devices connected, high demand for interoperability, etc. Focusing on these challenges, this paper gives a systematic review of advanced multimodal wearable intelligence technologies for dementia care in Healthcare 4.0. One framework is proposed for reviewing the current research of wearable intelligence, and key enabling technologies, major applications, and successful case studies in dementia care, and finally points out future research trends and challenges in Healthcare 4.0

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems

Discovering affect-laden requirements to achieve system acceptance

Novel envisioned systems face the risk of rejection by their target user community and the requirements engineer must be sensitive to the factors that will determine acceptance or rejection. Conventionally, technology acceptance is determined by perceived usefulness and ease-of-use, but in some domains other factors play an important role. In healthcare systems, particularly, ethical and emotional factors can be crucial. In this paper we describe an approach to requirements discovery that we developed for such systems. We describe how we have applied our approach to a novel system to passively monitor users for signs of cognitive decline consistent with the onset of dementia. A key challenge was eliciting users’ reactions to emotionally charged events never before experienced by them at first hand. Our goal was to understand the range of users’ emotional responses and their values and motivations, and from these formulate requirements that would maximise the likelihood of acceptance of the system. The problem was heightened by the fact that the key stakeholders were elderly people who represent a poorly studied user constituency. We discuss the elicitation and analysis methodologies used, and our experience with tool support. We conclude by reflecting on the affect issues for RE and for technology acceptance