4,151 research outputs found

    Fall prevention intervention technologies: A conceptual framework and survey of the state of the art

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    In recent years, an ever increasing range of technology-based applications have been developed with the goal of assisting in the delivery of more effective and efficient fall prevention interventions. Whilst there have been a number of studies that have surveyed technologies for a particular sub-domain of fall prevention, there is no existing research which surveys the full spectrum of falls prevention interventions and characterises the range of technologies that have augmented this landscape. This study presents a conceptual framework and survey of the state of the art of technology-based fall prevention systems which is derived from a systematic template analysis of studies presented in contemporary research literature. The framework proposes four broad categories of fall prevention intervention system: Pre-fall prevention; Post-fall prevention; Fall injury prevention; Cross-fall prevention. Other categories include, Application type, Technology deployment platform, Information sources, Deployment environment, User interface type, and Collaborative function. After presenting the conceptual framework, a detailed survey of the state of the art is presented as a function of the proposed framework. A number of research challenges emerge as a result of surveying the research literature, which include a need for: new systems that focus on overcoming extrinsic falls risk factors; systems that support the environmental risk assessment process; systems that enable patients and practitioners to develop more collaborative relationships and engage in shared decision making during falls risk assessment and prevention activities. In response to these challenges, recommendations and future research directions are proposed to overcome each respective challenge.The Royal Society, grant Ref: RG13082

    Virtual Reality Games for Motor Rehabilitation

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    This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

    Development of a neurofeedback-based virtual reality environment

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    Recent technology has continuously expanded the reaching spectre of psychotherapy. In the latest years, the development of digital environments, coupled with the evolution of sensorial hardware, has demonstrated usefulness and effectiveness in some areas of psychotherapy such as phobia treatment and attention deficit hyperactivity disorder management through neurofeedback training. However, the generality of these equipments is very expensive. In this project, an audiovisual stimuli virtual reality environment was developed, capable of displaying signals provided by an electroencephalography-based brain-computer interface. This environment has the objective of providing its user with neurofeedback training and being suited for affordable hardware equipments. Development of the aforementioned environment took place in the Unity3D ® game engine version 5.3.0f4, using C# scripting developed in Microsoft ® Visual Studio 2015 TM. As for the virtual reality display, an Oculus Rift ® development kit 1 was used for testing, together with the Oculus runtime for Windows ®, version 0.8.0.0. The used brain-computer interface was Neurosky’s Mindband TM, a research tool with a single electroencephalography channel, mediated through the ThinkGear Connector, version 3.1.8.0. The creation of this environment as an application directed towards neurofeedback training and compatible with affordable equipments is a contribution towards a reality where virtual reality is more synchronized with our society

    Ontological support for managing non-functional requirements in pervasive healthcare

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    We designed and implemented an ontological solution which makes provisions for choosing adequate devices/sensors for remote monitoring of patients who are suffering from post-stroke health complications. We argue that non-functional requirements in pervasive healthcare systems can be elicited and managed through semantics stored in ontological models and reasoning created upon them. Our contribution is twofold: we enrich the elicitation process and specification of non-functional requirements within the requirements engineering discipline and we address the pervasiveness of healthcare software systems through the way of choosing devices embedded in them and users expectations in terms of having access to pervasive services personalized to their needs

    A Review on the Role of Nano-Communication in Future Healthcare Systems: A Big Data Analytics Perspective

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    This paper presents a first-time review of the open literature focused on the significance of big data generated within nano-sensors and nano-communication networks intended for future healthcare and biomedical applications. It is aimed towards the development of modern smart healthcare systems enabled with P4, i.e. predictive, preventive, personalized and participatory capabilities to perform diagnostics, monitoring, and treatment. The analytical capabilities that can be produced from the substantial amount of data gathered in such networks will aid in exploiting the practical intelligence and learning capabilities that could be further integrated with conventional medical and health data leading to more efficient decision making. We have also proposed a big data analytics framework for gathering intelligence, form the healthcare big data, required by futuristic smart healthcare to address relevant problems and exploit possible opportunities in future applications. Finally, the open challenges, future directions for researchers in the evolving healthcare domain, are presented
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