TY - GEN
T1 - Sleeping Movement Detection towards Mental Health Indicators - A Review
AU - Luis-Ferreira, Fernando
AU - Giao, Joao
AU - Sarraipa, Joao
AU - Jardim-Goncalves, Ricardo
AU - McManus, Gary
AU - O'Brien, Philip
N1 - Funding Information:
The research leading to these results has received funding from the European Union H2020 Program under grant agreement No. 875358 “Big data and Artificial Intelligence for monitoring Health status and quality of life after the cancer treatment” (FAITH: a Federated Artificial Intelligence solution for moniToring mental Health status after cancer treatment) and research project CARELINK, AAL-CALL-2016-049 funded by AAL JP, and co-funded by the European Commission and National Funding Authorities of Ireland, Belgium, Portugal and Switzerland.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/6
Y1 - 2020/6
N2 - Sleep is a natural requirement of human beings that is common across most mammal species. The duration of sleep periods each day is a key factor to health status and, in general, to a person's health and wellbeing. In face of those facts, it is important to have a measurement of sleep duration and bodily activity during this sleep. In the last years some fitness devices in the market have been designed to track activity and provide indications about calorie expenditure as well as intake, along with specific sleep indicators. Such measures lack precision, as most of those are wrist worn devices and thus providing intelligence about upper arm movement while lacking in information about the steadiness of the human body as a whole. The present work explores some existing static solutions in the market and provides a feasible alternative for sleep movement detection. The research aims to study the sleep patterns of cancer patients that have undergone treatment cycles, thus providing indicators about their mental health status.
AB - Sleep is a natural requirement of human beings that is common across most mammal species. The duration of sleep periods each day is a key factor to health status and, in general, to a person's health and wellbeing. In face of those facts, it is important to have a measurement of sleep duration and bodily activity during this sleep. In the last years some fitness devices in the market have been designed to track activity and provide indications about calorie expenditure as well as intake, along with specific sleep indicators. Such measures lack precision, as most of those are wrist worn devices and thus providing intelligence about upper arm movement while lacking in information about the steadiness of the human body as a whole. The present work explores some existing static solutions in the market and provides a feasible alternative for sleep movement detection. The research aims to study the sleep patterns of cancer patients that have undergone treatment cycles, thus providing indicators about their mental health status.
KW - IoT
KW - Mental health indicators
KW - Sleep monitoring devices
UR - http://www.scopus.com/inward/record.url?scp=85093070969&partnerID=8YFLogxK
U2 - 10.1109/ICE/ITMC49519.2020.9198640
DO - 10.1109/ICE/ITMC49519.2020.9198640
M3 - Conference contribution
AN - SCOPUS:85093070969
T3 - Proceedings - 2020 IEEE International Conference on Engineering, Technology and Innovation, ICE/ITMC 2020
BT - Proceedings - 2020 IEEE International Conference on Engineering, Technology and Innovation, ICE/ITMC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Conference on Engineering, Technology and Innovation, ICE/ITMC 2020
Y2 - 15 June 2020 through 17 June 2020
ER -
