Karl Kreiner

The design of a purpose-built exergame for fall prediction and prevention for older people

BackgroundFalls in older people represent a major age-related health challenge facing our society. Novel methods for delivery of falls prevention programs are required to increase effectiveness and adherence to these programs while containing costs. The primary aim of the Information and Communications Technology-based System to Predict and Prevent Falls (iStoppFalls) project was to develop innovative home-based technologies for continuous monitoring and exercise-based prevention of falls in community-dwelling older people. The aim of this paper is to describe the components of the iStoppFalls system.MethodsThe system comprised of 1) a TV, 2) a PC, 3) the Microsoft Kinect, 4) a wearable sensor and 5) an assessment and training software as the main components.ResultsThe iStoppFalls system implements existing technologies to deliver a tailored home-based exercise and education program aimed at reducing fall risk in older people. A risk assessment tool was designed to identify fall risk factors. The content and progression rules of the iStoppFalls exergames were developed from evidence-based fall prevention interventions targeting muscle strength and balance in older people.ConclusionsThe iStoppFalls fall prevention program, used in conjunction with the multifactorial fall risk assessment tool, aims to provide a comprehensive and individualised, yet novel fall risk assessment and prevention program that is feasible for widespread use to prevent falls and fall-related injuries. This work provides a new approach to engage older people in home-based exercise programs to complement or provide a potentially motivational alternative to traditional exercise to reduce the risk of falling.

Smart Environments and Context-Awareness for Lifestyle Management in a Healthy Active Ageing Framework*

Health trends of elderly in Europe motivate the need for technological solutions aimed at preventing the main causes of morbidity and premature mortality. In this framework, the DOREMI project addresses three important causes of morbidity and mortality in the elderly by devising an ICT-based home care services for aging people to contrast cognitive decline, sedentariness and unhealthy dietary habits. In this paper, we present the general architecture of DOREMI, focusing on its aspects of human activity recognition and reasoning.

Crisis management knowledge from social media

More and more crisis managers, crisis communicators and laypeople use Twitter and other social media to provide or seek crisis information. In this paper, we focus on retrospective conversion of human-safety related data to crisis management knowledge. First, we study how Twitter data can be classified into the seven categories of the United Nations Development Program Security Model (i.e., Food, Health, Politics, Economic, Personal, Community, and Environment). We conclude that these topic categories are applicable, and supplementing them with classification of individual authors into more generic sources of data (i.e., Official authorities, Media, and Laypeople) allows curating data and assessing crisis maturity. Second, we introduce automated classifiers, based on supervised learning and decision rules, for both tasks and evaluate their correctness. This evaluation uses two datasets collected during the crises of Queensland floods and NZ Earthquake in 2011. The topic classifier performs well in the major categories (i.e., 120--190 training instances) of Economic (F = 0.76) and Community (F = 0.67) while in the minor categories (i.e., 0--60 training instances) the results are more modest (F ≤ 0.41). The source classifier shows excellent results (F ≥ 0.83) in all categories.

Semantic Interoperability as Key to IoT Platform Federation

Semantic interoperability is the key technology to enable evolution of the Internet of Things (IoT) from its current state of independent vertical IoT silos to interconnected IoT platform federations. This paper analyzes the possible solution space on how to achieve semantic interoperability and presents five possible approaches in detail together with a discussion on implementation issues. It presents the H2020 symbIoTe project as an example on how semantic interoperability can be achieved using semantic mapping and SPARQL query re-writing. We conclude that the found approaches together with the proposed technologies have the potential to act as corner stone technologies for achieving semantic interoperability.

Development and Evaluation of Cognitive Games to Promote Health and Wellbeing in Elderly People with Mild Cognitive Impairment.

BACKGROUND In Europe the number of elderly people is increasing. This population growth has resulted in higher healthcare costs. The purpose of this project was to try to promote active ageing in people aged 65-80 with mild cognitive impairment through cognitive games delivered via a tablet computer. OBJECTIVES Age-appropriate cognitive games were developed targeting different aspects of cognition and then experiences of elderly people using these games were evaluated. METHODS The design of games was developed through iterative user-centered design focus groups with elderly people as participants. The experiences of participants playing the games over a 47 day period were explored through semi-structured interviews. RESULTS Four games were developed that addressed a range of cognitive functions such as perception, attention, memory, language, comprehension and executive function. The participants were able to play these games without external intervention over an extended period and reported positively on their experiences. CONCLUSION Cognitive games can be used successfully by people with mild cognitive impairment to promote active ageing.

Predictive analytics for data driven decision support in health and care

Abstract Due to an ever-increasing amount of data generated in healthcare each day, healthcare professionals are more and more challenged with information. Predictive models based on machine learning algorithms can help to quickly identify patterns in clinical data. Requirements for data driven decision support systems for health and care (DS4H) are similar in many ways to applications in other domains. However, there are also various challenges which are specific to health and care settings. The present paper describes a) healthcare specific requirements for DS4H and b) how they were addressed in our Predictive Analytics Toolset for Health and care (PATH). PATH supports the following process: objective definition, data cleaning and pre-processing, feature engineering, evaluation, result visualization, interpretation and validation and deployment. The current state of the toolset already allows the user to switch between the various involved levels, i. e. raw data (ECG), pre-processed data (averaged heartbeat), extracted features (QT time), built models (to classify the ECG into a certain rhythm abnormality class) and outcome evaluation (e. g. a false positive case) and to assess the relevance of a given feature in the currently evaluated model as a whole and for the individual decision. This allows us to gain insights as a basis for improvements in the various steps from raw data to decisions.

Development of and adherence to a computer-based gamified environment designed to promote health and wellbeing in older people with mild cognitive impairment.

BACKGROUND The older population of Europe is increasing and there has been a corresponding increase in long term care costs. This project sought to promote active ageing by delivering tasks via a tablet computer to participants aged 65-80 with mild cognitive impairment. OBJECTIVES An age-appropriate gamified environment was developed and adherence to this solution was assessed through an intervention. METHODS The gamified environment was developed through focus groups. Mixed methods were used in the intervention with the time spent engaging with applications recorded supplemented by participant interviews to gauge adherence. There were two groups of participants: one living in a retirement village and the other living separately across a city. RESULTS The retirement village participants engaged in more than three times the number of game sessions compared to the other group possibly because of different social arrangements between the groups. CONCLUSION A gamified environment can help older people engage in computer-based applications. However, social community factors influence adherence in a longer term intervention.

Reasoning and Data Representation in a Health and Lifestyle Support System

Case-based reasoning and data interpretation is an artificial intelligence approach that capitalizes on past experience to solve current problems and this can be used as a method for practical intelligent systems. Case-based data reasoning is able to provide decision support for experts and clinicians in health systems as well as lifestyle systems. In this project we were focusing on developing a solution for healthy ageing considering daily activities, nutrition as well as cognitive activities. The data analysis of the reasoner followed state of the art guidelines from clinical practice. Guidelines provide a general framework to guide clinicians, and require consequent background knowledge to become operational, which is precisely the kind of information recorded in practice cases; cases complement guidelines very well and helps to interpret them. It is expected that the interest in case-based reasoning systems in the health.

Development of Multivariable Models to Predict and Benchmark Transfusion in Elective Surgery Supporting Patient Blood Management

BACKGROUND Blood transfusion is a highly prevalent procedure in hospitalized patients and in some clinical scenarios it has lifesaving potential. However, in most cases transfusion is administered to hemodynamically stable patients with no benefit, but increased odds of adverse patient outcomes and substantial direct and indirect cost. Therefore, the concept of Patient Blood Management has increasingly gained importance to pre-empt and reduce transfusion and to identify the optimal transfusion volume for an individual patient when transfusion is indicated. OBJECTIVES It was our aim to describe, how predictive modeling and machine learning tools applied on pre-operative data can be used to predict the amount of red blood cells to be transfused during surgery and to prospectively optimize blood ordering schedules. In addition, the data derived from the predictive models should be used to benchmark different hospitals concerning their blood transfusion patterns. METHODS 6,530 case records obtained for elective surgeries from 16 centers taking part in two studies conducted in 2004-2005 and 2009-2010 were analyzed. Transfused red blood cell volume was predicted using random forests. Separate models were trained for overall data, for each center and for each of the two studies. Important characteristics of different models were compared with one another. RESULTS Our results indicate that predictive modeling applied prior surgery can predict the transfused volume of red blood cells more accurately (correlation coefficient cc = 0.61) than state of the art algorithms (cc = 0.39). We found significantly different patterns of feature importance a) in different hospitals and b) between study 1 and study 2. CONCLUSION We conclude that predictive modeling can be used to benchmark the importance of different features on the models derived with data from different hospitals. This might help to optimize crucial processes in a specific hospital, even in other scenarios beyond Patient Blood Management.

Next Generation ICT Platform to Harmonize Medical, Care and Lifestyle Services

The key to active and healthy living/aging in the 21st century is to establish an individualized everyday-living environment that supports positive health behaviour and sustainable healthy lifestyle by means of applied ICT technology. Next generation ICT platforms have to support health-service as well as care and life-style service in a uniform as well as standardised way to enable integrated, scalable, and thus cost efficient solutions for the society.