Prediction of mobility entropy in an ambient intelligent environment

Ambient Intelligent (AmI) technology can be used to help older adults to live longer and independent lives in their own homes. Information collected from AmI environment can be used to detect and understanding human behaviour, allowing personalized care. The behaviour pattern can also be used to detect changes in behaviour and predict future trends, so that preventive action can be taken. However, due to the large number of sensors in the environment, sensor data are often complex and difficult to interpret, especially to capture behaviour trends and to detect changes over the long-term. In this paper, a model to predict the indoor mobility using binary sensors is proposed. The model utilizes weekly routine to predict the future trend. The proposed method is validated using data collected from a real home environment, and the results show that using weekly pattern helps improve indoor mobility prediction. Also, a new measurement, Mobility Entropy (ME), to measure indoor mobility based on entropy concept is proposed. The results indicate ME can be used to distinguish elders with different mobility and to see decline in mobility. The proposed work would allow detection of changes in mobility, and to foresee the future mobility trend if the current behaviour continues

Semantic-based decision support for remote care of dementia patients

This paper investigates the challenges in developing a semantic-based Dementia Care Decision Support System based on the non-intrusive monitoring of the patient's behaviour. Semantic-based approaches are well suited for modelling context-aware scenarios similar to Dementia care systems, where the patient's dynamic behaviour observations (occupants movement, equipment use) need to be analysed against the semantic knowledge about the patient's condition (illness history, medical advice, known symptoms) in an integrated knowledgebase. However, our research findings establish that the ability of semantic technologies to reason upon the complex interrelated events emanating from the behaviour monitoring sensors to infer knowledge assisting medical advice represents a major challenge. We attempt to address this problem by introducing a new approach that relies on propositional calculus modelling to segregate complex events that are amenable for semantic reasoning from events that require pre-processing outside the semantic engine before they can be reasoned upon. The event pre-processing activity also controls the timing of triggering the reasoning process in order to further improve the efficiency of the inference process. Using regression analysis, we evaluate the response-time as the number of monitored patients increases and conclude that the incurred overhead on the response time of the prototype decision support systems remains tolerable

An Ambient Assisted Living Technology Platform for Informal Carers of the Elderly - iCarer

For most families with elderly relatives, care within their own home is by far the most preferred option -both for the elderly and their carers. However, frequently these carers are the partners of the person with long-term care needs, and themselves are elderly and in need of support to cope with the burdens and stress associated with these duties. When it becomes too much for them, they may have to rely on professional care services, or even use residential care for a respite. In order to support the carers as well as the elderly person, an ambient assisted living platform has been developed. The system records information about the activities of daily living using unobtrusive sensors within the home, and allows the carers to record their own wellbeing state. By providing facilities to schedule and monitor the activities of daily care, and providing orientation and advice to improve the care given and their own wellbeing, the system helps to reduce the burden on the informal carers

An AAL collaborative system: the AAL4ALL and a mobile assistant case study

"15th IFIP WG 5.5 Working Conference on Virtual Enterprises, PRO-VE 2014, Amsterdam, The Netherlands, October 6-8, 2014"The areas of Ambient Assisted Living (AAL) and Intelligent Systems (IS) are in full development, but there are still some issues to be resolved. One issue is the myriad of user oriented solutions that are rarely built to interact or integrate with other systems available in the market. In this paper we present the AAL4ALL project and the UserAccess implementation, showing a novel approach towards virtual organizations, interoperability and certification. The aim of this project is to provide a collaborative network of services and devices that connect every user and product from other developers, building a heterogeneous ecosystem. Thus establishing an environment for collaborative care systems, which may be available to the users in from of safety services, comfort services and healthcare services.Project "AAL4ALL", co-financed by the European Community Fund FEDER, through COMPETE - Programa Operacional Factores de Competitividade (POFC). Foundation for Science and Technology (FCT), Lisbon, Portugal, through Project PEst-C/CTM/LA0025/2013 and the project PEst-OE/EEI/UI0752/2014. Project CAMCoF - Context-aware Multimodal Communication Framework fund-ed by ERDF -European Regional Development Fund through the COMPETE Pro-gramme (operational programme for competitiveness) and by National Funds through the FCT - Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project FCOMP-01-0124-FEDER-028980

The BCD Triage Sieve outperforms all existing major incident triage tools:comparative analysis using the UK national trauma registry population

BACKGROUND: Natural disasters, conflict, and terrorism are major global causes of death and disability. Central to the healthcare response is triage, vital to ensure the right care is provided to the right patient at the right time. The ideal triage tool has high sensitivity for the highest priority (P1) patients with acceptably low over-triage. This study compared the performance of major incident triage tools in predicting P1 casualty status in adults in the prospective UK Trauma Audit and Research Network (TARN) registry. METHODS: TARN patients aged 16+ years (January 2008-December 2017) were included. Ten existing triage tools were applied using patients’ first recorded pre-hospital physiology. Patients were subsequently assigned triage categories (P1, P2, P3, Expectant or Dead) based on pre-defined, intervention-based criteria. Tool performance was assessed by comparing tool-predicted and intervention-based priority status. FINDINGS: 195,709 patients were included; mortality was 7·0% (n=13,601); median Injury Severity Score (ISS) was 9 (IQR 9–17); 97·1% sustained blunt injuries. 22,144 (11·3%) patients fulfilled intervention-based criteria for P1 status, exhibiting higher mortality (12·8% vs. 5·0%, p<0.001), increased intensive care requirement (52·4% vs 5·0%, p<0.001), and more severe injuries (median ISS 21 vs 9, p<0.001) compared with P2 patients. In 16–64 year olds, the highest performing tool was the Battlefield Casualty Drills (BCD) Triage Sieve (Prediction of P1 status: 70·4% sensitivity, over-triage 70·9%, area under the receiver operating curve (AUC) 0·068 [95%CI 0·676–0·684]). The UK National Ambulance Resilience Unit (NARU) Triage Sieve had sensitivity of 44·9%; over-triage 56·4%; AUC 0·666 (95%CI 0·662–0·670). All tools performed poorly amongst the elderly (65+ years). INTERPRETATION: The BCD Triage Sieve performed best in this nationally representative population; we recommend it supersede the NARU Triage Sieve as the UK primary major incident triage tool. Validated triage category definitions are recommended for appraising future major incidents. FUNDING: This study is funded by the National Institute for Health Research (NIHR) Surgical Reconstruction and Microbiology Research Centre. GVG also acknowledges support from the MRC Heath Data Research UK (HDRUK/CFC/01). The views expressed are those of the authors and not necessarily those of the NIHR, the Department of Health and Social Care, or the Ministry of Defence

Redefining β-blocker response in heart failure patients with sinus rhythm and atrial fibrillation: a machine learning cluster analysis

Background: Mortality remains unacceptably high in patients with heart failure and reduced left ventricular ejection fraction (LVEF) despite advances in therapeutics. We hypothesised that a novel artificial intelligence approach could better assess multiple and higher-dimension interactions of comorbidities, and define clusters of β-blocker efficacy in patients with sinus rhythm and atrial fibrillation. Methods: Neural network-based variational autoencoders and hierarchical clustering were applied to pooled individual patient data from nine double-blind, randomised, placebo-controlled trials of β blockers. All-cause mortality during median 1·3 years of follow-up was assessed by intention to treat, stratified by electrocardiographic heart rhythm. The number of clusters and dimensions was determined objectively, with results validated using a leave-one-trial-out approach. This study was prospectively registered with ClinicalTrials.gov (NCT00832442) and the PROSPERO database of systematic reviews (CRD42014010012). Findings: 15 659 patients with heart failure and LVEF of less than 50% were included, with median age 65 years (IQR 56–72) and LVEF 27% (IQR 21–33). 3708 (24%) patients were women. In sinus rhythm (n=12 822), most clusters demonstrated a consistent overall mortality benefit from β blockers, with odds ratios (ORs) ranging from 0·54 to 0·74. One cluster in sinus rhythm of older patients with less severe symptoms showed no significant efficacy (OR 0·86, 95% CI 0·67–1·10; p=0·22). In atrial fibrillation (n=2837), four of five clusters were consistent with the overall neutral effect of β blockers versus placebo (OR 0·92, 0·77–1·10; p=0·37). One cluster of younger atrial fibrillation patients at lower mortality risk but similar LVEF to average had a statistically significant reduction in mortality with β blockers (OR 0·57, 0·35–0·93; p=0·023). The robustness and consistency of clustering was confirmed for all models (p<0·0001 vs random), and cluster membership was externally validated across the nine independent trials. Interpretation: An artificial intelligence-based clustering approach was able to distinguish prognostic response from β blockers in patients with heart failure and reduced LVEF. This included patients in sinus rhythm with suboptimal efficacy, as well as a cluster of patients with atrial fibrillation where β blockers did reduce mortality

Zoo Application of RFIDTechnology: A Case Study of Chiang Mai Zoo, Thailand

The research aims to build a technology system that improves service and operation for a zoo using RFID application. A problem investigation has been conducted with a case study at Chiang Mai Zoo, Thailand to propose and develop a prototype solution to overcome some practical issues. The proposed framework utilises RFID technology to store data and deal with other information technologies, there are two major subsystems: e-Ticket system and mobile tracking system. An implementation of the proposed system should provide significant benefits to the zoo as well as other similar businesses

Document Management System using Wireless RFID Technology for Intelligent Healthcare Operations

It has been estimated that in the UK National Health Service (NHS) 33% of nurses spend at least an hour in a shift on non-productive activities, equals 40 hours a month and costs the NHS approximately £900 million per year. This includes searching for equipment and patient records, a situation which will become more acute as the ageing population is expected to rise to 18.5% in the UK by 2020. The paper presents the use of emerging Radio Frequency Identification (RFID) technology for application in tracking and tracing document such as patient records in a hospital environment. The system developed uses low-cost passive tags to provide real time non intervention information using a TCP/IP protocol allow monitored across the organisation to track and trace and reduce un-productive time and prevent loss of documentation. This information can be linked to intelligent systems such as work flow management, simulation and data mining techniques etc. to provide intelligent decision making and what-if analysis. The paper discusses the development of Electronic Patient Record (EPR) and its issues and what our proposed system can offer and enhance EPR system

A new way to build multifacetted ontologies for elderly care

The care of the elderly is complex, with multiple agencies and individuals involved. We propose a new way of devel- oping an ontology to re ect these aspects within a real-time home monitoring system so that it captures real-life circum- stances and interactions. Our new methodology incorpo- rates iterative and evaluative stages to ensure the ontology captures implementable interactions and concepts. We have applied it to the iCarer project which was developed to as- sist informal carers with the activities of daily care for the elderly

Virtual carer: personalized support for informal caregivers of elderly

Due to the progressive ageing of the world population, new care models are required to maintain elderly's quality of life. These models should include the informal carer (IC) who usually lacks of skills and knowledge to develop assistance tasks. Therefore, support in decision making and informal carer empowerment are crucial to prevent and reduce the burden and stress suffered in the elderly care provided. This paper describes the Virtual Carer system aimed at supporting IC with a set of recommendations adapted to problems suffered in the activities of daily care developed or in daily activities of the older adult. The activities are detected by a set of sensors deployed in older adult's home or a questionnaires to be filled by the IC. The recommendation are sent to IC by means of text messages or email as well as learning videos accessible from e-learning system. As example, a particular use case of the Virtual Carer has been presented to show the information process of problems in IC's sleep patterns