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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA)
Carvalho, L. I. & Sofia, R. C. (2020). A review on scaling mobile snsing platformsfor human activity recognition: challenges and recommendations for future research. IoT. 1 (2), 451-473
Exportar Referência (IEEE)
L. I. Carvalho and H. R. Sofia,  "A review on scaling mobile snsing platformsfor human activity recognition: challenges and recommendations for future research", in IoT, vol. 1, no. 2, pp. 451-473, 2020
Exportar BibTeX
@article{carvalho2020_1618572776683,
	author = "Carvalho, L. I. and Sofia, R. C.",
	title = "A review on scaling mobile snsing platformsfor human activity recognition: challenges and recommendations for future research",
	journal = "IoT",
	year = "2020",
	volume = "1",
	number = "2",
	doi = "10.3390/iot1020025",
	pages = "451-473",
	url = "https://www.mdpi.com/journal/IoT"
}
Exportar RIS
TY  - JOUR
TI  - A review on scaling mobile snsing platformsfor human activity recognition: challenges and recommendations for future research
T2  - IoT
VL  - 1
IS  - 2
AU  - Carvalho, L. I.
AU  - Sofia, R. C.
PY  - 2020
SP  - 451-473
SN  - 2624-831X
DO  - 10.3390/iot1020025
UR  - https://www.mdpi.com/journal/IoT
AB  - Mobile sensing has been gaining ground due to the increasing capabilities of mobileand personal devices that are carried around by citizens, giving access to a large variety of dataand  services  based  on  the  way  humans  interact.   Mobile  sensing  brings  several  advantages  interms of the richness of available data, particularly for human activity recognition.  Nevertheless,the infrastructure required to support large-scale mobile sensing requires an interoperable design,which is still hard to achieve today. This review paper contributes to raising awareness of challengesfaced today by mobile sensing platforms that perform learning and behavior inference with respect tohuman routines: how current solutions perform activity recognition, which classification models theyconsider, and which types of behavior inferences can be seamlessly provided. The paper providesa set of guidelines that contribute to a better functional design of mobile sensing infrastructures,keeping scalability as well as interoperability in mind.
ER  -