Citizen Engineering: Methods for "Crowdsourcing" Highly Trustworthy Results

Zhai, Zhi; Hachen, David; Kijewski-Correa, Tracy; Shen, Feng; Madey, Gregory R.

doi:10.1109/hicss.2012.151

Cited by 15 publications

(3 citation statements)

References 14 publications

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“…For instance, software engineering platforms use crowdsourcing as an access to technological progress [ 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. Moreover, a wide range of research areas have benefitted from sourcing the crowd for environmental [ 30 , 31 ], geospatial [ 32 , 33 , 34 ], seismicity [ 35 ], and finally SHM studies [ 36 ]. In spite of the advantages offered by crowdsourcing, the data quality and accuracy need to be validated [ 37 , 38 ].…”

Section: Introductionmentioning

confidence: 99%

Citizen Sensors for SHM: Towards a Crowdsourcing Platform

Özer

Feng

2015

Sensors

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This paper presents an innovative structural health monitoring (SHM) platform in terms of how it integrates smartphone sensors, the web, and crowdsourcing. The ubiquity of smartphones has provided an opportunity to create low-cost sensor networks for SHM. Crowdsourcing has given rise to citizen initiatives becoming a vast source of inexpensive, valuable but heterogeneous data. Previously, the authors have investigated the reliability of smartphone accelerometers for vibration-based SHM. This paper takes a step further to integrate mobile sensing and web-based computing for a prospective crowdsourcing-based SHM platform. An iOS application was developed to enable citizens to measure structural vibration and upload the data to a server with smartphones. A web-based platform was developed to collect and process the data automatically and store the processed data, such as modal properties of the structure, for long-term SHM purposes. Finally, the integrated mobile and web-based platforms were tested to collect the low-amplitude ambient vibration data of a bridge structure. Possible sources of uncertainties related to citizens were investigated, including the phone location, coupling conditions, and sampling duration. The field test results showed that the vibration data acquired by smartphones operated by citizens without expertise are useful for identifying structural modal properties with high accuracy. This platform can be further developed into an automated, smart, sustainable, cost-free system for long-term monitoring of structural integrity of spatially distributed urban infrastructure. Citizen Sensors for SHM will be a novel participatory sensing platform in the way that it offers hybrid solutions to transitional crowdsourcing parameters.

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Section: Introductionmentioning

confidence: 99%

Citizen Sensors for SHM: Towards a Crowdsourcing Platform

Özer

Feng

2015

Sensors

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“…Although crowdsourcing-based parking availability information systems are intriguing, trustworthy crowdsourcing [5], [18] presents several challenges -how can we obtain high quality data from unreliable crowds of mobile users who can be selfish, erroneous or even malicious? Incentives are generally introduced for the purpose of user participation as well as high quality data [9][18] [13].…”

Section: Introductionmentioning

confidence: 99%

TruCentive: A game-theoretic incentive platform for trustworthy mobile crowdsourcing parking services

Hoh

Yan

Ganesan

et al. 2012

2012 15th International IEEE Conference on Intelligent Transportation Systems

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The shortage of parking in crowded urban areas causes severe societal problems such as traffic congestion, environmental pollution, and many others. Recently, crowdsourced parking, where smartphone users are exploited to collect realtime parking availability information, has attracted significant attention. However, existing crowdsourced parking information systems suffer from low user participation rate and data quality due to the lack of carefully designed incentive schemes.In this paper, we address the incentive problem of trustworthy crowdsourced parking information systems by presenting an incentive platform named TruCentive, where high utility parking data can be obtained from unreliable crowds of mobile users. Our contribution is three-fold. First, we provide hierarchical incentives to stimulate the participation of mobile users for contributing parking information. Second, by introducing utility-related incentives, our platform encourages participants to contribute high utility data and thereby enhances the quality of collected data. Third, our active confirmation scheme validates the parking information utility by game-theoretically formulated incentive protocols. The active confirming not only validates the utility of contributed data but re-sells the high utility data as well. Our evaluation through user study on Amazon Mechanical Turk and simulation study demonstrate the feasibility and stability of TruCentive incentive platform.

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“…The difference between actual meal weight (the gold-standard) and total meal weight estimated by different sized crowds and ratings by a dietician were compared to each other. To establish group consensus crowd estimates were weighted by majority agreement ( 5 ) . Bland-Altman analysis assessed agreement with actual meal weight.…”

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confidence: 99%

FoodFinder: developing a rapid low-cost crowdsourcing approach for obtaining data on meal size from meal photos

et al. 2016

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Capturing meal images using mobile phone cameras is a promising alternative to traditional dietary assessment methods. Acquiring photos is reasonably simple but nutritional content analysis of images is a major challenge. Automated food identification and portion size assessment is computationally and participant intensive; relying on participant feedback for accuracy (1) . Dietitian analysis of photos is accurate but time-consuming and expensive (2) . Crowdsourcing could offer a rapid low-cost alternative by utilising the life-long experience that all humans have in food identification. Previous crowdsourcing methods include the Eatery app, which produces a simple 11-point 'healthiness' scale for each meal (3) and the PlateMate system, which creates a list of all individual foods with portion sizes, energy and macronutrient content (4) . While the Eatery produces limited and subjective data on meal content, PlateMate represents a complex integrated system of multiple tasks requiring on average 25 workers, costing £2·75 and taking 90 min per image. For feasible data-capture in a large-scale longitudinal studies, crowdsourcing data from meal photos needs to be cheaper and quicker. We aimed to develop a simpler task and tested it's feasibility for crowdsourcing dietary data.FoodFinder, a single task for identifying food groups and portion sizes, developed using Qualtrics (www.qualtrics.com/), and linked to the Prolific Academic (https://prolific.ac/) crowdsourcing platform for recruitment and reimbursement of a UK crowd. Thirty meal photos with measured total meal weight (grams) were analysed by a dietitian and crowds ranging in size from 5 to 50 people. The difference between actual meal weight (the gold-standard) and total meal weight estimated by different sized crowds and ratings by a dietician were compared to each other. To establish group consensus crowd estimates were weighted by majority agreement (5) . Bland-Altman analysis assessed agreement with actual meal weight.A crowd of 5 people underestimated true meal weight by 63 g, equating to 15 % of actual meal weight with limits of agreement (LOA) from −299 to 174 g. In comparison experts overestimated by 28 g equating to 9 % of actual meal weight with LOA −158, 214 g. With a crowd of 5 people, crowdsourcing cost £3·35 and took a mean 2 mins 55 sec (SD 2 min 6 sec) per image. A crowd of 50 had similar accuracy and limits of agreement (−65 g LOA −278, 149 g) but was more expensive. Further development of FoodFinder is required to make rapid low-cost analysis of meal photos via crowdsourcing a feasible method for assessing diet.

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Citizen Engineering: Methods for "Crowdsourcing" Highly Trustworthy Results

Cited by 15 publications

References 14 publications

Citizen Sensors for SHM: Towards a Crowdsourcing Platform

Citizen Sensors for SHM: Towards a Crowdsourcing Platform

TruCentive: A game-theoretic incentive platform for trustworthy mobile crowdsourcing parking services

FoodFinder: developing a rapid low-cost crowdsourcing approach for obtaining data on meal size from meal photos

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