Development and usability of a gamified app to help children manage stress: an evaluation study.

Children generally have difficulty managing stress. As a result, stress may escalate to anxiety. Informal-learning, stress prevention interventions that are easily accessible on mobile devices could be one way for children to learn how to manage stress before it reaches the levels of anxiety. There are less than a handful of stress management apps targeting children presently available and these do not combine gamification techniques with behavior change theory. This study describes the design and development of a gamified interactive storytelling mobile app to teach young children how to manage stress through relaxation exercises. It evaluates the app’s usability using learning analytics data and the SUS usability scale. The gamified app called Kids’ Stress Relief received a satisfactory usability score (73.55) and was well accepted by a sample of 71 children (5-12 years old). It may have the potential to support children in learning how to perform stress relief techniques as a stand-alone application. Instructional and design implications, of interest to developers of psychology-based apps, are drawn

A distributed framework for contaminant event detection and isolation in multi-zone intelligent buildings

International audienceAn intelligent building is required to provide safety to its occupants against any possible threat that may affect the indoor air quality, such as accidental or malicious airborne contaminant release in the building interior. In this work, we design a distributed methodology for detecting and isolating multiple contaminant events in a large-scale building. Specifically, we consider the building as a collection of interconnected subsystems and we design a contaminant event monitoring agent for each subsystem. Each monitoring agent aims to detect the contamination of the underlying subsystem and isolate the zone where the contaminant source is located, while it is allowed to exchange information with its neighboring agents. The decision logic implemented in the contaminant event monitoring agent is based on the generation of observer-based residuals and adaptive thresholds. We demonstrate our proposed formulation using a 14-zone building case study

Contaminant event monitoring in multi-zone buildings using the state-space method

The dispersion of contaminants from sources (events) inside a building can compromise the indoor air quality and influence the occupants' comfort, health, productivity and safety. Such events could be the result of an accident, faulty equipment or a planned attack. Under these safety-critical conditions, immediate event detection should be guaranteed and the proper actions should be taken to ensure the safety of the people. In this paper, we consider an event as a fault in the process that disturbs the normal system operation. This places the problem of contaminant event monitoring in the fault diagnosis framework of detection and isolation. A main contribution of this work is the development of the state-space method, based on multi-zone building models, that enables the use of advanced fault diagnosis tools for contaminant event monitoring. Specifically, in this paper, we develop estimator schemes with adaptive thresholds for the detection and isolation of a single contaminant source under conditions of noise and modeling uncertainty. We demonstrate our proposed formulation using a 2-zone illustration example and a more realistic 14-zone building setting

Contaminant event monitoring in multi-zone buildings using the state-space method

The dispersion of contaminants from sources (events) inside a building can compromise the indoor air quality and influence the occupants' comfort, health, productivity and safety. Such events could be the result of an accident, faulty equipment or a planned attack. Under these safety-critical conditions, immediate event detection should be guaranteed and the proper actions should be taken to ensure the safety of the people. In this paper, we consider an event as a fault in the process that disturbs the normal system operation. This places the problem of contaminant event monitoring in the fault diagnosis framework of detection and isolation. A main contribution of this work is the development of the state-space method, based on multi-zone building models, that enables the use of advanced fault diagnosis tools for contaminant event monitoring. Specifically, in this paper, we develop estimator schemes with adaptive thresholds for the detection and isolation of a single contaminant source under conditions of noise and modeling uncertainty. We demonstrate our proposed formulation using a 2-zone illustration example and a more realistic 14-zone building setting

A Matlab-CONTAM Toolbox for contaminant event monitoring in intelligent buildings

Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 412)An intelligent building should take all the necessary steps to provide protection against the dispersion of contaminants from sources (events) inside the building which can compromise the indoor air quality and influence the occupants' comfort, health, productivity and safety. Multi-zone models and software, such as CONTAM, have been widely used in building environmental studies for predicting airflows and the resulting contaminant transport. This paper describes a developed Matlab Toolbox that allows the creation of data sets from running multiple scenarios using CONTAM by varying the different problem parameters. The Matlab-CONTAM Toolbox is an expandable research tool which facilitates the implementation of various algorithms related to contamination event monitoring. In particular, this paper describes the implementation of state-of-the-art algorithms for detecting and isolating a contaminant source. The use of the Toolbox is demonstrated through a building case-study. The Matlab-CONTAM Toolbox is released under an open-source licence, and is available at https://github.com/KIOS-Research/matlab-contam-toolbox

An indoor contaminant sensor placement toolbox for critical infrastructure buildings

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8328)In this work, we address the problem of airborne contaminant sensor placement in high-risk buildings where critical infrastructures are managed and operated, making them possible locations for terrorist attacks (such as governmental buildings and ministries, utilities, airports and hospitals). A new software is presented based on the "Matlab-CONTAM Toolbox" and the CONTAM multi-zone simulation software, to construct multiple scenarios of contamination events and to solve the multi-objective sensor placement problem for minimizing the average and maximum impact risk with respect to the contaminant mass inhaled impact metric. The use of the software is demonstrated in a case-study using the Holmes's House benchmark. The Toolbox is released under an open-source license at https://github.com/KIOS-Research/ matlab-contam-toolbox

A Matlab-CONTAM Toolbox for contaminant event monitoring in intelligent buildings

Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 412)An intelligent building should take all the necessary steps to provide protection against the dispersion of contaminants from sources (events) inside the building which can compromise the indoor air quality and influence the occupants' comfort, health, productivity and safety. Multi-zone models and software, such as CONTAM, have been widely used in building environmental studies for predicting airflows and the resulting contaminant transport. This paper describes a developed Matlab Toolbox that allows the creation of data sets from running multiple scenarios using CONTAM by varying the different problem parameters. The Matlab-CONTAM Toolbox is an expandable research tool which facilitates the implementation of various algorithms related to contamination event monitoring. In particular, this paper describes the implementation of state-of-the-art algorithms for detecting and isolating a contaminant source. The use of the Toolbox is demonstrated through a building case-study. The Matlab-CONTAM Toolbox is released under an open-source licence, and is available at https://github.com/KIOS-Research/matlab-contam-toolbox

An indoor contaminant sensor placement toolbox for critical infrastructure buildings

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8328)In this work, we address the problem of airborne contaminant sensor placement in high-risk buildings where critical infrastructures are managed and operated, making them possible locations for terrorist attacks (such as governmental buildings and ministries, utilities, airports and hospitals). A new software is presented based on the "Matlab-CONTAM Toolbox" and the CONTAM multi-zone simulation software, to construct multiple scenarios of contamination events and to solve the multi-objective sensor placement problem for minimizing the average and maximum impact risk with respect to the contaminant mass inhaled impact metric. The use of the software is demonstrated in a case-study using the Holmes's House benchmark. The Toolbox is released under an open-source license at https://github.com/KIOS-Research/ matlab-contam-toolbox

Simple methods to test the accuracy of MRgFUS robotic systems

Background: Roboticassisted diagnostic and therapeutic modalities require ahighly accurate performance to be certified for clinical application. In this paper,three simple methods for assessing the accuracy of motion of magnetic resonanceguided focused ultrasound (MRgFUS) robotic systems are presented.Methods: The accuracy of motion of a 4 degrees of freedom robotic systemintended for preclinical use of MRgFUS was evaluated by calliperbased and mag-netic resonance imaging (MRI) methods, as well as visually by performing multipleablations on a plastic film.Results: The benchtop results confirmed a highly accurate motion in all axes ofoperation. The spatial positioning errors estimated by MRI evaluation were definedby the size of the imaging pixels. Lesions arrangement in discrete and overlappingpatterns confirmed satisfactory alignment of motion trajectories.Conclusions: We believe the methods presented here should serve as a standard forevaluating the accuracy of motion of MRgFUS robotic systems