TY - GEN
T1 - A COTS (UHF) RFID Floor for Device-Free Ambient Assisted Living Monitoring
AU - Smith, Ronnie
AU - Ding, Yuan
AU - Goussetis, George
AU - Dragone, Mauro
PY - 2020
Y1 - 2020
N2 - The complexity and the intrusiveness of current proposals for AAL monitoring negatively impact end-user acceptability, and ultimately still hinder widespread adoption by key stakeholders (e.g. public and private sector care providers) who seek to balance system usefulness with upfront installation and long-term configuration and maintenance costs. We present the results of our experiments with a device-free wireless sensing (DFWS) approach utilising commercial off-the-shelf (COTS) Ultra High Frequency (UHF) Radio Frequency Identification (RFID) equipment. Our system is based on antennas above the ceiling and a dense deployment of passive RFID tags under the floor. We provide baseline performance of state of the art machine learning techniques applied to a region-level localisation task. We describe the dataset, which we collected in a realistic testbed, and which we share with the community. Contrary to past work with similar systems, our dataset was collected in a realistic domestic environment over a number of days. The data highlights the potential but also the problems that need to be solved before RFID DFWS approaches can be used for long-term AAL monitoring.
AB - The complexity and the intrusiveness of current proposals for AAL monitoring negatively impact end-user acceptability, and ultimately still hinder widespread adoption by key stakeholders (e.g. public and private sector care providers) who seek to balance system usefulness with upfront installation and long-term configuration and maintenance costs. We present the results of our experiments with a device-free wireless sensing (DFWS) approach utilising commercial off-the-shelf (COTS) Ultra High Frequency (UHF) Radio Frequency Identification (RFID) equipment. Our system is based on antennas above the ceiling and a dense deployment of passive RFID tags under the floor. We provide baseline performance of state of the art machine learning techniques applied to a region-level localisation task. We describe the dataset, which we collected in a realistic testbed, and which we share with the community. Contrary to past work with similar systems, our dataset was collected in a realistic domestic environment over a number of days. The data highlights the potential but also the problems that need to be solved before RFID DFWS approaches can be used for long-term AAL monitoring.
KW - Ambient Assisted Living (AAL)
KW - Device free wireless sensing
KW - Fingerprinting
KW - Healthcare monitoring
KW - Indoor localisation
KW - Radio-frequency identification (RFID)
KW - Region-level tracking
UR - http://www.scopus.com/inward/record.url?scp=85091530558&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-58356-9_13
DO - 10.1007/978-3-030-58356-9_13
M3 - Conference contribution
SN - 9783030583552
T3 - Advances in Intelligent Systems and Computing
SP - 127
EP - 136
BT - Ambient Intelligence – Software and Applications. ISAmI 2020
A2 - Novais, Paulo
A2 - Vercelli, Gianni
A2 - Larriba-Pey, Josep L.
A2 - Herrera, Francisco
A2 - Chamoso, Pablo
PB - Springer
ER -