Risk Management through Real-Time Wearable Services

Fugini, Mariagrazia; Maggiolini, Piercarlo; Raibulet, Claudia; Ubezio, Luigi

doi:10.1109/icsea.2009.33

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…Considering the technology available for risk management, we have categorized technologies and their capabilities of addressing problems as follows: Health monitoring and wearable objects: Technologies for users’ health have seen many progresses through wearable objects , such as shoes, gloves, or clothes, which constantly monitor vital parameters (blood pressure or heart beating), evidencing anomalies and generating alarms in case of illness. Applied to industrial environments, the technology inserted in wearable objects permits an addition of functions that make the user perceive a higher usefulness and protection level.…”

Section: Engineering Issues For Risk Managementmentioning

confidence: 99%

“…To wrap up, in the prototype, for each use case, we have evaluated: The quality of the generated events and alarms in terms of precision, correctness of response times and of addressed workers; The volume of data generated by the simulator to detect and to react to the dangerous event; this allowed us to estimate the quantity of data that need to be stored in the RMS repository for reporting and off‐line analysis (we consider that the system is able to support risk analysis, as explained in our previous research work ); The correctness of the model, to understand what elements need to be considered in detail (e.g., the definition of user roles and profiles), and which elements have been neglected. For instance, the topology of the work environment is fundamental to fine tune the action, such as an escape action, to be undertaken in the face of a risk event.…”

Section: The Prototypementioning

confidence: 99%

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Risk assessment in work environments: modeling and simulation

Fugini

Raibulet

Ubezio

2012

Concurrency and Computation

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The paper proposes a characterization of risks and a service-oriented prototype to face risky situations in work environments, such as in industrial plants or building construction areas. A risk is the overture of emergencies that produce human and/or material damages. Therefore, it is particularly critical to identify and manage risks to avoid their evolution into emergencies. In this paper, we outline the technological features of a risk environment and propose a risk model and a service-based simulation prototype aimed to improve safety in work environments. We discuss engineering issues concerning risk modeling and management. Furthermore, we propose a risk management system solution and its related implemented prototype composed of services able to detect and also to prevent the occurrence of risk conditions

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Section: Engineering Issues For Risk Managementmentioning

confidence: 99%

Section: The Prototypementioning

confidence: 99%

Risk assessment in work environments: modeling and simulation

Fugini

Raibulet

Ubezio

2012

Concurrency and Computation

Self Cite

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“…Military applications might also use a sensor network in order to identify ground targets [8] or hidden snipers in a combat area [24]. Workers may also be localised in order to warn them that they are trespassing a dangerous area in a factory [9].The common aspect among all these scenarios is that they all need precise measurements of the target position. Indeed, this is possible only if the calibration of the heterogeneous sensors is known and accurate.…”

Section: Introductionmentioning

confidence: 99%

A Closed Form Solution for the Self-Calibration of Heterogeneous Sensors

Crocco

Barbosa

Murino

2012

Procedings of the British Machine Vision Conference 2012

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We present a novel closed-form solution for the joint self-calibration of video and range sensors. The approach single assumption is the availability of synchronous time of flight (i.e., range distances) measurements and visual position of the target on images acquired by a set of cameras. In such case, we make explicit a rank constraint that is valid for both image and range data. This rank property is used to find an initial and affine solution via bilinear factorization, which is then corrected by enforcing the metric constraints characteristic for both sensor modalities (i.e., camera and anchors constraints). The output of the algorithm is the identification of the target/range sensor position and the calibration of the cameras. The application extent of our approach is broad and versatile. In fact, with the same framework, we can deal with, but not restricted to, two very different applications. The first is aimed at calibrating cameras and microphones deployed in an unknown environment. The second uses a RGB-D device to reconstruct the 3D position of a set of keypoints using the camera and depth map images. Synthetic and real tests show the algorithm performance under different levels of noise and configurations of target locations, number of sensors and cameras.

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