3,395 research outputs found
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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
In silico case studies of compliant robots: AMARSI deliverable 3.3
In the deliverable 3.2 we presented how the morphological computing ap-
proach can significantly facilitate the control strategy in several scenarios,
e.g. quadruped locomotion, bipedal locomotion and reaching. In particular,
the Kitty experimental platform is an example of the use of morphological
computation to allow quadruped locomotion. In this deliverable we continue
with the simulation studies on the application of the different morphological
computation strategies to control a robotic system
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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
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Enabling the Virtual Phones to remotely sense the Real Phones in real-time: A Sensor Emulation initiative for virtualized Android-x86
Smartphones nowadays have the ground-breaking features that were only a figment of one’s imagination. For the ever-demanding cellphone users, the exhaustive list of features that a smartphone supports just keeps getting more exhaustive with time. These features aid one’s personal and professional uses as well. Extrapolating into the future the features of a present-day smartphone, the lives of us humans using smartphones are going to be unimaginably agile. With the above said emphasis on the current and future potential of a smartphone, the ability to virtualize smartphones with all their real-world features into a virtual platform, is a boon for those who want to rigorously experiment and customize the virtualized smartphone hardware without spending an extra penny. Once virtualizable independently on a larger scale, the idea of virtualized smartphones with all the virtualized pieces of hardware takes an interesting turn with the sensors being virtualized in a way that’s closer to the real-world behavior. When accessible remotely with the real-time responsiveness, the above mentioned real-world behavior will be a real dealmaker in many real-world systems, namely, the life-saving systems like the ones that instantaneously get alerts about harmful magnetic radiations in the deep mining areas, etc. And these life-saving systems would be installed on a large scale on the desktops or large servers as virtualized smartphones having the added support of virtualized sensors which remotely fetch the real hardware sensor readings from a real smartphone in real-time. Based on these readings the lives working in the affected areas can be alerted and thus saved by the people who are operating the at the desktops or large servers hosting the virtualized smartphones
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Sensing gestures for business intelligence
The combination of sensor data with analytic techniques is growing in popularity for both practitioners and researchers as an Internet of Things (IoT) offers new opportunities and insights. Organisations are trying to use sensor technologies to derive intelligence and gain a competitive edge in their industries. Obtaining data from sensors might not pose too much of a problem, however subsequent utilisation in meeting an organisation’s decision making can be more problematic. Understanding how sensor data analytics can be undertaken is the first step to deriving business intelligence from front line retail environments. This paper explores the use of the Microsoft Kinect sensor to provide intelligence by identifying and sensing gestures to better understand customer behaviour in the retail space
A Robotic Neuro-Musculoskeletal Simulator for Spine Research
An influential conceptual framework advanced by Panjabi represents the living spine as a complex neuromusculoskeletal system whose biomechanical functioning is rather finely dependent upon the interactions among and between three principal subsystems: the passive musculoskeletal subsystem (osteoligamentous spine plus passive mechanical contributions of the muscles), the active musculoskeletal subsystem (muscles and tendons), and the neural and feedback subsystem (neural control centers and feedback elements such as mechanoreceptors located in the soft tissues) [1]. The interplay between subsystems readily encourages thought experiments of how pathologic changes in one subsystem might influence another--for example, prompting one to speculate how painful arthritic changes in the facet joints might affect the neuromuscular control of spinal movement. To answer clinical questions regarding the interplay between these subsystems the proper experimental tools and techniques are required. Traditional spine biomechanical experiments are able to provide comprehensive characterization of the structural properties of the osteoligamentous spine. However, these technologies do not incorporate a simulated neural feedback from neural elements, such as mechanoreceptors and nociceptors, into the control loop. Doing so enables the study of how this feedback--including pain-related--alters spinal loading and motion patterns. The first such development of this technology was successfully completed in this study and constitutes a Neuro-Musculoskeletal Simulator. A Neuro-Musculoskeletal Simulator has the potential to reduce the gap between bench and bedside by creating a new paradigm in estimating the outcome of spine pathologies or surgeries. The traditional paradigm is unable to estimate pain and is also unable to determine how the treatment, combined with the natural pain avoidance of the patient, would transfer the load to other structures and potentially increase the risk for other problems. The novel Neuro-Musculo
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