A Classification Framework of Mobile Health CrowdSensing Research

Tokosi, Temitope Oluwaseyi; Scholtz, Brenda

doi:10.1145/3351108.3351113

Cited by 10 publications

(10 citation statements)

References 45 publications

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“…Its use cases include data collection in clinical and health/psychological trials (Pryss et al, 2015;Schobel et al, 2015), environmental monitoring and pollution measurement like noise pollution (Schweizer et al, 2011;Zappatore et al, 2017) or air pollution (Mun et al, 2009), public health (Wesolowski et al, 2012), and personal well-being (Consolvo et al, 2006). Although various mobile applications and solutions have been proposed, less works exist that cover reference settings to build generic solutions (Tokosi and Scholtz, 2019). In addition, few works are based on comprehensive experiences that are gained through various long-running projects (Tokosi and Scholtz, 2019).…”

Section: Mobile Crowdsensing (Mcs)mentioning

confidence: 99%

“…Although various mobile applications and solutions have been proposed, less works exist that cover reference settings to build generic solutions (Tokosi and Scholtz, 2019). In addition, few works are based on comprehensive experiences that are gained through various long-running projects (Tokosi and Scholtz, 2019).…”

Section: Mobile Crowdsensing (Mcs)mentioning

confidence: 99%

“…Finally, the experiences gained with TrackYourTinnitus and the projects shown in Table 2 are discussed in the light of their general contribution and their generalizability. A recent review of mobile health crowdsensing research (Tokosi and Scholtz, 2019) shows that the projects shown in Table 2 and the related papers are heavily recognized by their selected key terms of existing works. Tokosi and Scholtz (2019) also shows that although more and more research is pursued in this context, less experiences are reported that were gained over multiple large-scale and longrunning projects.…”

Section: Lessons Learned From the Trackyourtinnitus Projectmentioning

confidence: 99%

“…A recent review of mobile health crowdsensing research (Tokosi and Scholtz, 2019) shows that the projects shown in Table 2 and the related papers are heavily recognized by their selected key terms of existing works. Tokosi and Scholtz (2019) also shows that although more and more research is pursued in this context, less experiences are reported that were gained over multiple large-scale and longrunning projects. Therefore, we consider our experiences as a proper starting point to conceive a reference architecture that incorporates aspects that are relevant on one hand.…”

Section: Lessons Learned From the Trackyourtinnitus Projectmentioning

confidence: 99%

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Combining Mobile Crowdsensing and Ecological Momentary Assessments in the Healthcare Domain

et al. 2020

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The increasing prevalence of smart mobile devices (e.g., smartphones) enables the combined use of mobile crowdsensing (MCS) and ecological momentary assessments (EMA) in the healthcare domain. By correlating qualitative longitudinal and ecologically valid EMA assessment data sets with sensor measurements in mobile apps, new valuable insights about patients (e.g., humans who suffer from chronic diseases) can be gained. However, there are numerous conceptual, architectural and technical, as well as legal challenges when implementing a respective software solution. Therefore, the work at hand (1) identifies these challenges, (2) derives respective recommendations, and (3) proposes a reference architecture for a MCS-EMA-platform addressing the defined recommendations. The required insights to propose the reference architecture were gained in several large-scale mHealth crowdsensing studies running for many years and different healthcare questions. To mention only two examples, we are running crowdsensing studies on questions for the tinnitus chronic disorder or psychological stress. We consider the proposed reference architecture and the identified challenges and recommendations as a contribution in two respects. First, they enable other researchers to align our practical studies with a baseline setting that can satisfy the variously revealed insights. Second, they are a proper basis to better compare data that was gathered using MCS and EMA. In addition, the combined use of MCS and EMA increasingly requires suitable architectures and associated digital solutions for the healthcare domain.

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Section: Mobile Crowdsensing (Mcs)mentioning

confidence: 99%

Section: Mobile Crowdsensing (Mcs)mentioning

confidence: 99%

Section: Lessons Learned From the Trackyourtinnitus Projectmentioning

confidence: 99%

Section: Lessons Learned From the Trackyourtinnitus Projectmentioning

confidence: 99%

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Combining Mobile Crowdsensing and Ecological Momentary Assessments in the Healthcare Domain

et al. 2020

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“…Contrary to crowdsourcing, the initial crowdsensing typology included environmental, infrastructural, and social applications [95], with the health issues brought into the focus only recently. Despite the late arrival, a survey of the healthcare crowdsensing articles has already appeared [96], with 1705 articles found according to the title and abstract, but with 13 papers included in the study due to the rigid exclusion criteria.…”

Section: Crowdsourcing/crowdsensing For Healthcare Applicationsmentioning

confidence: 99%

Emerging Wireless Sensor Networks and Internet of Things Technologies—Foundations of Smart Healthcare

Gardasevic

Katzis

Bajić

et al. 2020

Sensors

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Future smart healthcare systems—often referred to as Internet of Medical Things (IoMT) – will combine a plethora of wireless devices and applications that use wireless communication technologies to enable the exchange of healthcare data. Smart healthcare requires sufficient bandwidth, reliable and secure communication links, energy-efficient operations, and Quality of Service (QoS) support. The integration of Internet of Things (IoT) solutions into healthcare systems can significantly increase intelligence, flexibility, and interoperability. This work provides an extensive survey on emerging IoT communication standards and technologies suitable for smart healthcare applications. A particular emphasis has been given to low-power wireless technologies as a key enabler for energy-efficient IoT-based healthcare systems. Major challenges in privacy and security are also discussed. A particular attention is devoted to crowdsourcing/crowdsensing, envisaged as tools for the rapid collection of massive quantities of medical data. Finally, open research challenges and future perspectives of IoMT are presented.

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