SPINE: A domain-specific framework for rapid prototyping of WBSN applications

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BodyCloud: a SaaS approach for community body sensor networks

Body Sensor Networks (BSNs) have been recently introduced for the remote monitoring of human activities in a broad range of application domains, such as health care, emergency management, fitness and behaviour surveillance. BSNs can be deployed in a community of people and can generate large amounts of contextual data that require a scalable approach for storage, processing and analysis. Cloud computing can provide a flexible storage and processing infrastructure to perform both online and offline analysis of data streams generated in BSNs. This paper proposes BodyCloud, a SaaS approach for community BSNs that supports the development and deployment of Cloud-assisted BSN applications. BodyCloud is a multi-tier application-level architecture that integrates a Cloud computing platform and BSN data streams middleware. BodyCloud provides programming abstractions that allow the rapid development of community BSN applications. This work describes the general architecture of the proposed approach and presents a case study for the real-time monitoring and analysis of cardiac data streams of many individuals

Cloud-assisted body area networks: state-of-the-art and future challenges

Body area networks (BANs) are emerging as enabling technology for many human-centered application domains such as health-care, sport, fitness, wellness, ergonomics, emergency, safety, security, and sociality. A BAN, which basically consists of wireless wearable sensor nodes usually coordinated by a static or mobile device, is mainly exploited to monitor single assisted livings. Data generated by a BAN can be processed in real-time by the BAN coordinator and/or transmitted to a server-side for online/offline processing and long-term storing. A network of BANs worn by a community of people produces large amount of contextual data that require a scalable and efficient approach for elaboration and storage. Cloud computing can provide a flexible storage and processing infrastructure to perform both online and offline analysis of body sensor data streams. In this paper, we motivate the introduction of Cloud-assisted BANs along with the main challenges that need to be addressed for their development and management. The current state-of-the-art is overviewed and framed according to the main requirements for effective Cloud-assisted BAN architectures. Finally, relevant open research issues in terms of efficiency, scalability, security, interoperability, prototyping, dynamic deployment and management, are discussed

An agent-based signal processing in-node environment for real-time human activity monitoring based on wireless body sensor networks

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Trauma ICU Prevalence Project: the diversity of surgical critical care.

Background:Surgical critical care is crucial to the care of trauma and surgical patients. This study was designed to provide a contemporary assessment of patient types, injuries, and conditions in intensive care units (ICU) caring for trauma patients. Methods:This was a multicenter prevalence study of the American Association for the Surgery of Trauma; data were collected on all patients present in participating centers' trauma ICU (TICU) on November 2, 2017 and April 10, 2018. Results:Forty-nine centers submitted data on 1416 patients. Median age was 58 years (IQR 41-70). Patient types included trauma (n=665, 46.9%), non-trauma surgical (n=536, 37.8%), medical (n=204, 14.4% overall), or unspecified (n=11). Surgical intensivists managed 73.1% of patients. Of ICU-specific diagnoses, 57% were pulmonary related. Multiple high-intensity diagnoses were represented (septic shock, 10.2%; multiple organ failure, 5.58%; adult respiratory distress syndrome, 4.38%). Hemorrhagic shock was seen in 11.6% of trauma patients and 6.55% of all patients. The most common traumatic injuries were rib fractures (41.6%), brain (38.8%), hemothorax/pneumothorax (30.8%), and facial fractures (23.7%). Forty-four percent were on mechanical ventilation, and 17.6% had a tracheostomy. One-third (33%) had an infection, and over half (54.3%) were on antibiotics. Operations were performed in 70.2%, with 23.7% having abdominal surgery. At 30 days, 5.4% were still in the ICU. Median ICU length of stay was 9 days (IQR 4-20). 30-day mortality was 11.2%. Conclusions:Patient acuity in TICUs in the USA is very high, as is the breadth of pathology and the interventions provided. Non-trauma patients constitute a significant proportion of TICU care. Further assessment of the global predictors of outcome is needed to inform the education, research, clinical practice, and staffing of surgical critical care providers. Level of evidence:IV, prospective observational study

A Java-Based Agent Platform for Programming Wireless Sensor Networks

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Using cloud-assisted body area networks to track people physical activity in mobility

This paper describes a novel BSN-based integrated system for detecting, monitoring, and securely recording human physical activities using wearable sensors, a personal mobile device, and a Cloud-computing infrastructure supported by the BodyCloud platform. An integration with a smart-wheelchair system is also presented. BSNs are a key enabling technology for the revolution of personal-health services and their integration with Cloud infrastructure can effectively supports the diffusion of such services in our daily life. Many of these personal-health systems - regardless of their final aim - are based, use or are supported by contextual information on user's physical activity (body posture, movement or action) being performed. This work, hence, aims at providing a basic physical activity service that is capable of supporting personal, mobile-Health applications with real-time activity recognition and labeling both on the personal mobile device and on the Cloud

A framework for cloud-based healthcare services to monitor noncommunicable diseases patient

Monitoring patients who have noncommunicable diseases is a big challenge. These illnesses require a continuous monitoring that leads to high cost for patients\u27 healthcare. Several solutions proposed reducing the impact of these diseases in terms of economic with respect to quality of services. One of the best solutions is mobile healthcare, where patients do not need to be hospitalized under supervision of caregivers. This paper presents a new hybrid framework based on mobile multimedia cloud that is scalable and efficient and provides cost-effective monitoring solution for noncommunicable disease patient. In order to validate the effectiveness of the framework, we also propose a novel evaluation model based on Analytical Hierarchy Process (AHP), which incorporates some criteria from multiple decision makers in the context of healthcare monitoring applications. Using the proposed evaluation model, we analyzed three possible frameworks (proposed hybrid framework, mobile, and multimedia frameworks) in terms of their applicability in the real healthcare environment

A pervasive body sensor network for monitoring post-operative recovery

Over the past decade, miniaturisation and cost reduction brought about by the semiconductor industry has led to computers smaller in size than a pin head, powerful enough to carry out the processing required, and affordable enough to be disposable. Similar technological advances in wireless communication, sensor design, and energy storage have resulted in the development of wireless “Body Sensor Network (BSN) platforms comprising of tiny integrated micro sensors with onboard processing and wireless data transfer capability, offering the prospect of pervasive and continuous home health monitoring. In surgery, the reduced trauma of minimally invasive interventions combined with initiatives to reduce length of hospital stay and a socioeconomic drive to reduce hospitalisation costs, have all resulted in a trend towards earlier discharge from hospital. There is now a real need for objective, pervasive, and continuous post-operative home recovery monitoring systems. Surgical recovery is a multi-faceted and dynamic process involving biological, physiological, functional, and psychological components. Functional recovery (physical independence, activities of daily living, and mobility) is recognised as a good global indicator of a patient’s post-operative course, but has traditionally been difficult to objectively quantify. This thesis outlines the development of a pervasive wireless BSN system to objectively monitor the functional recovery of post-operative patients at home. Biomechanical markers were identified as surrogate measures for activities of daily living and mobility impairment, and an ear-worn activity recognition (e-AR) sensor containing a three-axis accelerometer and a pulse oximeter was used to collect this data. A simulated home environment was created to test a Bayesian classifier framework with multivariate Gaussians to model activity classes. A real-time activity index was used to provide information on the intensity of activity being performed. Mobility impairment was simulated with bracing systems and a multiresolution wavelet analysis and margin-based feature selection framework was used to detect impaired mobility. The e-AR sensor was tested in a home environment before its clinical use in monitoring post-operative home recovery of real patients who have undergone surgery. Such a system may eventually form part of an objective pervasive home recovery monitoring system tailored to the needs of today’s post-operative patient.Open acces