Towards Crowd-Sourced Air Quality and Physical Activity Monitoring by a Low-Cost Mobile Platform

Yang, Bian; Castell, Núria; Pei, Junjie; Du, Yang; Gebremedhin, Alemayehu; Kirkevold, Øyvind

doi:10.1007/978-3-319-39601-9_41

Cited by 11 publications

(9 citation statements)

References 3 publications

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“…Typical examples of the use of mobile sensors for the measurement of air quality over the past few years include the work of B. Yang et al (), where a low‐cost mobile platform was designed and implemented to measure air quality. Munasinghe et al () demonstrated how a miniature microcontroller‐based handheld device was developed to collect hazardous gas levels (CO, SO 2 , NO 2 ) using semiconductor sensors.…”

Section: Review Of Crowdsourcing Data Acquisition Methods Usedmentioning

confidence: 99%

Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions

Zheng

Tao

Maier

et al. 2018

Reviews of Geophysics

110

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Data are essential in all areas of geophysics. They are used to better understand and manage systems, either directly or via models. Given the complexity and spatiotemporal variability of geophysical systems (e.g., precipitation), a lack of sufficient data is a perennial problem, which is exacerbated by various drivers, such as climate change and urbanization. In recent years, crowdsourcing has become increasingly prominent as a means of supplementing data obtained from more traditional sources, particularly due to its relatively low implementation cost and ability to increase the spatial and/or temporal resolution of data significantly. Given the proliferation of different crowdsourcing methods in geophysics and the promise they have shown, it is timely to assess the state of the art in this field, to identify potential issues and map out a way forward. In this paper, crowdsourcing‐based data acquisition methods that have been used in seven domains of geophysics, including weather, precipitation, air pollution, geography, ecology, surface water, and natural hazard management, are discussed based on a review of 162 papers. In addition, a novel framework for categorizing these methods is introduced and applied to the methods used in the seven domains of geophysics considered in this review. This paper also features a review of 93 papers dealing with issues that are common to data acquisition methods in different domains of geophysics, including the management of crowdsourcing projects, data quality, data processing, and data privacy. In each of these areas, the current status is discussed and challenges and future directions are outlined.

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Section: Review Of Crowdsourcing Data Acquisition Methods Usedmentioning

confidence: 99%

Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions

Zheng

Tao

Maier

et al. 2018

Reviews of Geophysics

110

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“…Finally, only two studies monitored PM only outdoors. In [ 117 ], a correlation was found between air quality indicators, participants’ subjective feelings about air quality, physical activity status measured with wearable sensors, and reported health symptoms. Of particular note is the result of [ 118 ].…”

Section: Systematic Reviewmentioning

confidence: 99%

Wearable Devices for Environmental Monitoring in the Built Environment: A Systematic Review

Salamone

Masullo

Sibilio

2021

Sensors

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The so-called Internet of Things (IoT), which is rapidly increasing the number of network-connected and interconnected objects, could have a far-reaching impact in identifying the link between human health, well-being, and environmental concerns. In line with the IoT concept, many commercial wearables have been introduced in recent years, which differ from the usual devices in that they use the term “smart” alongside the terms “watches”, “glasses”, and “jewellery”. Commercially available wearables aim to enhance smartphone functionality by enabling payment for commercial items or monitoring physical activity. However, what is the trend of scientific production about the concept of wearables regarding environmental monitoring issues? What are the main areas of interest covered by scientific production? What are the main findings and limitations of the developed solution in this field? The methodology used to answer the above questions is based on a systematic review. The data were acquired following a reproducible methodology. The main result is that, among the thermal, visual, acoustic, and air quality environmental factors, the last one is the most considered when using wearables even though in combination with some others. Another relevant finding is that of the acquired studies; in only one, the authors shared their wearables as an open-source device, and it will probably be necessary to encourage researchers to consider open-source as a means to promote scalability and proliferation of new wearables customized to cover different domains.

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“…Sentiment analysis can be applied in social data from social networks such as Twitter [32]. Similar results can also be investigated from public surveys [65]. Regression analysis or correlations can then be applied on the two kinds of data.…”

Section: Environmental Monitoringmentioning

confidence: 99%

“…It also enables APIs for data access in eXtensible Markup Language (XML) and JavaScript Object Notation (JSON) formats according to different sensing sites or sensing species. Due to the provenance of the sensed data and known static context, fixed sensing has been applied for numerous sensing applications, such as air quality sensing [32,65], weather conditions [72], water distribution data [71] and traffic data [56,72]. Different supporting frameworks are also proposed [18,40,56].…”

Section: Physical Sensor Deploymentsmentioning

confidence: 99%

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Cyber–Physical–Social Frameworks for Urban Big Data Systems: A Survey

Zhou

Abad³

et al. 2017

Applied Sciences

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Abstract:The integration of things' data on the Web and Web linking for things' description and discovery is leading the way towards smart Cyber-Physical Systems (CPS). The data generated in CPS represents observations gathered by sensor devices about the ambient environment that can be manipulated by computational processes of the cyber world. Alongside this, the growing use of social networks offers near real-time citizen sensing capabilities as a complementary information source. The resulting Cyber-Physical-Social System (CPSS) can help to understand the real world and provide proactive services to users. The nature of CPSS data brings new requirements and challenges to different stages of data manipulation, including identification of data sources, processing and fusion of different types and scales of data. To gain an understanding of the existing methods and techniques which can be useful for a data-oriented CPSS implementation, this paper presents a survey of the existing research and commercial solutions. We define a conceptual framework for a data-oriented CPSS and detail the various solutions for building human-machine intelligence.

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Towards Crowd-Sourced Air Quality and Physical Activity Monitoring by a Low-Cost Mobile Platform

Cited by 11 publications

References 3 publications

Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions

Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions

Wearable Devices for Environmental Monitoring in the Built Environment: A Systematic Review

Cyber–Physical–Social Frameworks for Urban Big Data Systems: A Survey

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