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Elvas, L., Calé, D., Ferreira, J. C. & Madureira, A. (2021). Remote Monitor System for Alzheimer disease. In Abraham, A., Madureira, A. M., Kaklauskas, A., Gandhi N., Bajaj, A., Muda, A. K., Kriksciuniene, D., and Ferreira, J. C. (Ed.), Innovations in Bio-Inspired Computing and Applications. Lecture Notes in Networks and Systems. (pp. 251-260). Virtual, Online: Springer Cham.

L. M. Elvas et al.,  "Remote Monitor System for Alzheimer disease", in Innovations in Bio-Inspired Computing and Applications. Lecture Notes in Networks and Systems, Abraham, A., Madureira, A. M., Kaklauskas, A., Gandhi N., Bajaj, A., Muda, A. K., Kriksciuniene, D., and Ferreira, J. C., Ed., Virtual, Online, Springer Cham, 2021, vol. 419, pp. 251-260

@inproceedings{elvas2021_1732205263742,
	author = "Elvas, L. and Calé, D. and Ferreira, J. C. and Madureira, A.",
	title = "Remote Monitor System for Alzheimer disease",
	booktitle = "Innovations in Bio-Inspired Computing and Applications. Lecture Notes in Networks and Systems",
	year = "2021",
	editor = "Abraham, A., Madureira, A. M., Kaklauskas, A., Gandhi N., Bajaj, A., Muda, A. K., Kriksciuniene, D., and Ferreira, J. C.",
	volume = "419",
	number = "",
	series = "",
	doi = "10.1007/978-3-030-96299-9_24",
	pages = "251-260",
	publisher = "Springer Cham",
	address = "Virtual, Online",
	organization = "",
	url = "https://link.springer.com/book/10.1007/978-3-030-96299-9"
}

TY  - CPAPER
TI  - Remote Monitor System for Alzheimer disease
T2  - Innovations in Bio-Inspired Computing and Applications. Lecture Notes in Networks and Systems
VL  - 419
AU  - Elvas, L.
AU  - Calé, D.
AU  - Ferreira, J. C.
AU  - Madureira, A.
PY  - 2021
SP  - 251-260
SN  - 2367-3370
DO  - 10.1007/978-3-030-96299-9_24
CY  - Virtual, Online
UR  - https://link.springer.com/book/10.1007/978-3-030-96299-9
AB  - Health Remote Monitoring Systems (HRMS) offer the ability to address health-care human resource concerns. In developing nations, where pervasive mobile networks and device access are linking people like never before, HRMS are of special relevance. A fundamental aim of this research work is the realization of technological-based solution to triage and follow-up people living with dementias so as to reduce pressure on busy staff while doing this from home so as to avoid all unnecessary visits to hospital facilities, increasingly perceived as dangerous due to COVID-19 but also raising nosocomial infections, raising alerts for abnormal values. Sensing approaches are complemented by advanced predictive models based on Machine Learning (ML) and Artificial Intelligence (AI), thus being able to explore novel ways of demonstrating patient-centered predictive measures. Low-cost IoT devices composing a network of sensors and actuators aggregated to create a digital experience that will be used and exposure to people to simultaneously conduct several tests and obtain health data that can allow screening of early onset dementia and to aid in the follow-up of selected cases. The best ML for predicting AD was logistic regression with an accuracy of 86.9%. This application as demonstrated to be essential for caregivers once they can monitor multiple patients in real-time and actuate when abnormal values occur.
ER  -