LivingCare - An autonomously learning, human centered home automation system: Collection and preliminary analysis of a large dataset of real living situations
Publikation: Beiträge in Sammelwerken › Aufsätze in Sammelwerken › Forschung › begutachtet
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Ambient Assisted Living: 9. AAL-Kongress, Frankfurt/M, Germany, April 20 - 21, 2016. Hrsg. / Reiner Wichert; Beate Mand. Cham: Springer International Publishing AG, 2017. S. 55-72 (Advanced Technologies and Societal Change).
Publikation: Beiträge in Sammelwerken › Aufsätze in Sammelwerken › Forschung › begutachtet
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TY - CHAP
T1 - LivingCare - An autonomously learning, human centered home automation system
T2 - Collection and preliminary analysis of a large dataset of real living situations
AU - Eckert, Ralf
AU - Müller, Sebastian
AU - Glende, Sebastian
AU - Gerka, Alexander
AU - Hein, Andreas
AU - Welge, Ralph
PY - 2017
Y1 - 2017
N2 - Within the scope of LivingCare, a BMBF funded research project, a real senior residence was equipped with a large amount of home automation sensors. More than sixty sensors and actuators were installed in this apartment. This automa-tion system is working totally passive in the background. It doesn't perform any actions. All actions performed by hu-mans like switching light on or off, setting the temperature and the usage of electric devices like TVs will only be recorded as data and not performed by the system. This data is collected over a period of 18 months. Thus, one of the largest mobility and characteristics datasets based on home automation sensors will be acquired. This data will be the foundation for developing autonomously learning algorithms. During the second project phase these algorithms will start to control functions of the home automation system. The projects objective is to develop an autonomously learning home automation system that automatically adapts to the resident's behavior. The system will be able to grow with the users needs. With all the possible data collected it will be able to support daily actions, recognize behavior changes over timer and will be able to call help in emergency situations.
AB - Within the scope of LivingCare, a BMBF funded research project, a real senior residence was equipped with a large amount of home automation sensors. More than sixty sensors and actuators were installed in this apartment. This automa-tion system is working totally passive in the background. It doesn't perform any actions. All actions performed by hu-mans like switching light on or off, setting the temperature and the usage of electric devices like TVs will only be recorded as data and not performed by the system. This data is collected over a period of 18 months. Thus, one of the largest mobility and characteristics datasets based on home automation sensors will be acquired. This data will be the foundation for developing autonomously learning algorithms. During the second project phase these algorithms will start to control functions of the home automation system. The projects objective is to develop an autonomously learning home automation system that automatically adapts to the resident's behavior. The system will be able to grow with the users needs. With all the possible data collected it will be able to support daily actions, recognize behavior changes over timer and will be able to call help in emergency situations.
KW - Informatics
KW - home automation
KW - Internet of Things
KW - IoT
KW - reinforcement learning
KW - autonomously learning
KW - real life data
KW - field trail
KW - Gait speed
KW - Test Person
KW - Sensor Event
KW - Movement Sensor
KW - Occupation Time
U2 - 10.1007/978-3-319-52322-4_4
DO - 10.1007/978-3-319-52322-4_4
M3 - Contributions to collected editions/anthologies
SN - 978-3-319-52321-7
T3 - Advanced Technologies and Societal Change
SP - 55
EP - 72
BT - Ambient Assisted Living
A2 - Wichert, Reiner
A2 - Mand, Beate
PB - Springer International Publishing AG
CY - Cham
ER -