TY - GEN
T1 - Adaptive tracking model in the framework of medical nursing home using infrared sensors
AU - Carvalho, Caio M.A.
AU - Rodrigues, Christiano A.P.
AU - Aguilar, Paulo A.C.
AU - De Castro, Miguel F.
AU - Andrade, Rossana M.C.
AU - Boudy, Jerome
AU - Istrate, Dan
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/1/1
Y1 - 2015/1/1
N2 - On Internet of Things (IoT), everything can be accessed anytime, anywhere, and works without human intervention. In IoT everything collaborates to deliver services and applications to users, Ambient Assisted Living (AAL) being one of these applications. Some AAL smart homes uses infrared sensors to recognize some activities of daily living and to track people along the environment. Location tracking is vital in Ambient Assisted living and can be a useful information to improve AAL systems. A common problem in such systems is that each tracking model is based on a specific sensor's placing architecture. In order to assure that the system will work properly, the model has to be fitted by an expert. Modeling is usually costly and it relies on a specific architecture. In our previous work, the tracking model needed to be fitted manually. In order to introduce adaptability, this work proposes an approach to automatically fit the model avoiding the need of an expert to fit a different model for each kind of sensor's placing architecture. The proposed approach was evaluated using real data from a set of pyroelectric infrared sensors and a set of scenarios performed in a simulated apartment.
AB - On Internet of Things (IoT), everything can be accessed anytime, anywhere, and works without human intervention. In IoT everything collaborates to deliver services and applications to users, Ambient Assisted Living (AAL) being one of these applications. Some AAL smart homes uses infrared sensors to recognize some activities of daily living and to track people along the environment. Location tracking is vital in Ambient Assisted living and can be a useful information to improve AAL systems. A common problem in such systems is that each tracking model is based on a specific sensor's placing architecture. In order to assure that the system will work properly, the model has to be fitted by an expert. Modeling is usually costly and it relies on a specific architecture. In our previous work, the tracking model needed to be fitted manually. In order to introduce adaptability, this work proposes an approach to automatically fit the model avoiding the need of an expert to fit a different model for each kind of sensor's placing architecture. The proposed approach was evaluated using real data from a set of pyroelectric infrared sensors and a set of scenarios performed in a simulated apartment.
KW - AAL
KW - Health Smart Homes
KW - Health Telemonitoring
KW - Infrared Sensor
KW - IoT
KW - Tracking
U2 - 10.1109/GLOCOMW.2015.7414030
DO - 10.1109/GLOCOMW.2015.7414030
M3 - Conference contribution
AN - SCOPUS:84971247544
T3 - 2015 IEEE Globecom Workshops, GC Wkshps 2015 - Proceedings
BT - 2015 IEEE Globecom Workshops, GC Wkshps 2015 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE Globecom Workshops, GC Wkshps 2015
Y2 - 6 December 2015 through 10 December 2015
ER -