24,280 research outputs found

    Healthcare PANs: Personal Area Networks for trauma care and home care

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    The first hour following the trauma is of crucial importance in trauma care. The sooner treatment begins, the better the ultimate outcome for the patient. Generally the initial treatment is handled by paramedical personnel arriving at the site of the accident with an ambulance. There is evidence to show that if the expertise of the on-site paramedic team can be supported by immediate and continuous access to and communication with the expert medical team at the hospital, patient outcomes can be improved. After care also influences the ultimate recovery of the patient. After-treatment follow up often occurs in-hospital in spite of the fact that care at home can offer more advantages and can accelerate recovery. Based on emerging and future wireless communication technologies, in a previous paper [1] we presented an initial vision of two future healthcare settings, supported by applications which we call Virtual Trauma Team and Virtual Homecare Team. The Virtual Trauma Team application involves high quality wireless multimedia communications between ambulance paramedics and the hospital facilitated by paramedic Body Area Networks (BANs) [2] and an ambulance-based Vehicle Area Network (VAN). The VAN supports bi-directional streaming audio and video communication between the ambulance and the hospital even when moving at speed. The clinical motivation for Virtual Trauma Team is to increase survival rates in trauma care. The Virtual Homecare Team application enables homecare coordinated by home nursing services and supported by the patient's PAN which consists of a patient BAN in combination with an ambient intelligent home environment. The homecare PAN provides intelligent monitoring and support functions and the possibility to ad hoc network to the visiting health professionals’ own BANs as well as high quality multimedia communication links to remote members of the virtual team. The motivation for Virtual Homecare Team is to improve quality of life and independence for patients by supporting care at home; the economic motivation is to replace expensive hospital-based care with homecare by virtual teams using wireless technology to support the patient and the carers. In this paper we develop the vision further and focus in particular on the concepts of personal and body area networks

    Smart Computing and Sensing Technologies for Animal Welfare: A Systematic Review

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    Animals play a profoundly important and intricate role in our lives today. Dogs have been human companions for thousands of years, but they now work closely with us to assist the disabled, and in combat and search and rescue situations. Farm animals are a critical part of the global food supply chain, and there is increasing consumer interest in organically fed and humanely raised livestock, and how it impacts our health and environmental footprint. Wild animals are threatened with extinction by human induced factors, and shrinking and compromised habitat. This review sets the goal to systematically survey the existing literature in smart computing and sensing technologies for domestic, farm and wild animal welfare. We use the notion of \emph{animal welfare} in broad terms, to review the technologies for assessing whether animals are healthy, free of pain and suffering, and also positively stimulated in their environment. Also the notion of \emph{smart computing and sensing} is used in broad terms, to refer to computing and sensing systems that are not isolated but interconnected with communication networks, and capable of remote data collection, processing, exchange and analysis. We review smart technologies for domestic animals, indoor and outdoor animal farming, as well as animals in the wild and zoos. The findings of this review are expected to motivate future research and contribute to data, information and communication management as well as policy for animal welfare

    Semi-automatic semantic enrichment of raw sensor data

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    One of the more recent sources of large volumes of generated data is sensor devices, where dedicated sensing equipment is used to monitor events and happenings in a wide range of domains, including monitoring human biometrics. In recent trials to examine the effects that key moments in movies have on the human body, we fitted fitted with a number of biometric sensor devices and monitored them as they watched a range of dierent movies in groups. The purpose of these experiments was to examine the correlation between humans' highlights in movies as observed from biometric sensors, and highlights in the same movies as identified by our automatic movie analysis techniques. However,the problem with this type of experiment is that both the analysis of the video stream and the sensor data readings are not directly usable in their raw form because of the sheer volume of low-level data values generated both from the sensors and from the movie analysis. This work describes the semi-automated enrichment of both video analysis and sensor data and the mechanism used to query the data in both centralised environments, and in a peer-to-peer architecture when the number of sensor devices grows to large numbers. We present and validate a scalable means of semi-automating the semantic enrichment of sensor data, thereby providing a means of large-scale sensor management
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