Situation awareness in crowdsensing for disease surveillance in crisis situations

Haddawy, Peter; Frommberger, Lutz; Kauppinen, Tomi; Felice, Giorgio De; Charkratpahu, Prae; Saengpao, Sirawaratt; Kanchanakitsakul, Phanumas

doi:10.1145/2737856.2737879

Cited by 17 publications

(10 citation statements)

References 9 publications

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“…Similarly, in case of environment-centric applications, [4] presents an approach that enables use of specialised auxiliary sensors to achieve participatory sensing for active monitoring of air quality and pollution. Authors of [8] present a locationand machine-learning-based solution towards observance of disease and disaster outbreak. Both of these approaches rely highly on participatory sensing coupled with minor automated sensing, carried out by auxiliary or specialised sensors.…”

Section: Related Workmentioning

confidence: 99%

Towards Automated Smart Mobile Crowdsensing for Tinnitus Research

Mehdi¹,

Schwager²,

Pryss³

et al. 2019

2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)

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Tinnitus is a disorder that is not entirely understood, and many of its correlations are still unknown. On the other hand, smartphones became ubiquitous. Their modern versions provide high computational capabilities, reasonable battery size, and a bunch of embedded high-quality sensors, combined with an accepted user interface and an application ecosystem. For tinnitus, as for many other health problems, there are a number of apps trying to help patients, therapists, and researchers to get insights into personal characteristics but also into scientific correlations as such. In this paper, we present the first approach to an app in this context, called TinnituSense that does automatic sensing of related characteristics and enables correlations to the current condition of the patient by a combined participatory sensing, e.g., a questionnaire. For tinnitus, there is a strong hypothesis that weather conditions have some influence. Our proof-of-concept implementation records weather-related sensor data and correlates them to the standard Tinnitus Handicap Inventory (THI) questionnaire. Thus, TinnituSense enables therapists and researchers to collect evidence for unknown facts, as this is the first opportunity to correlate weather to patient conditions on a larger scale. Our concept as such is limited neither to tinnitus nor to built-in sensors, e.g., in the tinnitus domain, we are experimenting with mobile EEG sensors. TinnituSense is faced with several challenges of which we already solved principle architecture, sensor management, and energy consumption. Index Terms-mobile crowdsensing; tinnitus; automated mobile sensing; mobile health;

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Section: Related Workmentioning

confidence: 99%

Towards Automated Smart Mobile Crowdsensing for Tinnitus Research

Mehdi¹,

Schwager²,

Pryss³

et al. 2019

2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)

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“…In the academic community, prior studies exist on situation and situation-awareness by John Mc-Carthy [26,27], Michael Bratman [28], Jon Barwise [29,30], and recently by Haddawy et al [31]. In reviewing the academic literature we often encounter the terms related to belief, desire, intention, motivation, etc., and these are terms intimately tied to humans.…”

Section: Aware Computingmentioning

confidence: 99%

Path planning for mobile robot using self-adaptive learning particle swarm optimization

Chou

2017

Sci. China Inf. Sci.

126

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In this paper, I first review the seminal work by Thomas Kuhn-The Structure of Scientific Revolutions-and elaborate my view on paradigm shifts in software engineering research and practice as it turns 50 years old in 2018. I then examine major undertakings of the computing profession since early days of modern computing, especially those done by the software engineering community as a whole. I also enumerate anomalies and crises that occurred at various stages, and the attempts to provide solutions by the software engineering professionals in the past five decades. After providing such a background, I direct readers' attention toward emerging anomalies in software engineering, at a severity level that is causing another software engineering crisis, and suggest a set of criteria for feasible solutions. The main theme of this paper is to advocate that situation analytics, equipped with necessary definitions of essential concepts including situation and intention as parts of a new computational framework, can serve as the foundation for a new software engineering paradigm named the Situation-Centric Paradigm. In this framework, situation is considered a new abstraction for computing and is clearly differentiated from the widely accepted existing abstractions, namely function and object. I argue that the software engineering professionals will inevitably move into this new paradigm, willingly or unwillingly, to empower Human-Embedded Computing (HEC) and End-User Embedded Computing (EUEC), much more than what they have done with traditional humancentered or user-centric computing altogether. In the end, I speculate that an ultimate agile method may be on the rise, and challenge readers to contemplate "what if" hundreds of thousands "end-user developers" emerge into the scene where the boundaries between end users and developers become much more blurred.

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“…This type of data acquisition involves users moving towards a particular location and being implicitly recruited to capture required data by the built-in sensors in their smart devices [80]. Crowdsensing envisions a robust data collection approach where users have the leverage and the ability to choose and report more data for experimental purposes in real time [81]. Consequently, this type of data acquisition increases the amount of data required for any purpose especially for the applications under smart health and smart cities [82].…”

Section: Data Acquisition Via Crowd-sensingmentioning

confidence: 99%

Big Sensed Data Meets Deep Learning for Smarter Health Care in Smart Cities

Obinikpo

Kantarcı

2017

JSAN

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Abstract:With the advent of the Internet of Things (IoT) concept and its integration with the smart city sensing, smart connected health systems have appeared as integral components of the smart city services. Hard sensing-based data acquisition through wearables or invasive probes, coupled with soft sensing-based acquisition such as crowd-sensing results in hidden patterns in the aggregated sensor data. Recent research aims to address this challenge through many hidden perceptron layers in the conventional artificial neural networks, namely by deep learning. In this article, we review deep learning techniques that can be applied to sensed data to improve prediction and decision making in smart health services. Furthermore, we present a comparison and taxonomy of these methodologies based on types of sensors and sensed data. We further provide thorough discussions on the open issues and research challenges in each category.

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Situation awareness in crowdsensing for disease surveillance in crisis situations

Cited by 17 publications

References 9 publications

Towards Automated Smart Mobile Crowdsensing for Tinnitus Research

Towards Automated Smart Mobile Crowdsensing for Tinnitus Research

Path planning for mobile robot using self-adaptive learning particle swarm optimization

Big Sensed Data Meets Deep Learning for Smarter Health Care in Smart Cities

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