Activity monitoring and automatic alarm generation in AAL-enabled homes
In this work, novel contributions towards the emerging field of Ambient Assisted Living (AAL) are introduced. AAL is a concept envisioned in the early 2000s by the European Commission, aiming at supporting specifically senior people by means of technology and thus helping them to lead independent and self-determined lives in their accustomed surroundings as long as possible. Modern home automation technology is believed to be the key to providing various services in the fields of health, safety, comfort, and communication. In the framework of this thesis, health monitoring aspects are of particular interest. Inactivity monitoring is a very promising approach thereto since it allows the detection of potential health threats or cases of emergency without being overly privacy intrusive. Deriving condensed and dependable inactivity profiles representing typical user behaviour is a pivotal prerequisite for automatic emergency monitoring. Several methodologies for computing such patterns are introduced. Based on those inactivity profiles, various alarming criteria (i.e., permissible inactivity thresholds) are utilised to trigger alarms automatically if the users' inactivity levels exceed individual, user-dependant limits. Since false alarms are inevitable in automatic alarming systems, a procedure of handling them is introduced as well. Finally, the real-world application of the devised AAL system is illustrated.
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AMBIENT ASSISTED LIVING AAL
THE PROCESS OF AAL RESEARCH AND DEVELOPMENT
SENSOR DATA COLLECTION AND PROCESSING
ACTIVITY VS INACTIVITY APPROACH
ALARM GENERATION BASED UPON INACTIVITY ANALYSIS
28 days AAL solution AAL system AAL technology alarm threshold alarming criteria alarming scheme alarms in flat algorithms Ambient Ambient Intelligence approach automatic average bathroom bedroom box plots buttons call centre criterion day Fig day-time detection diagram door sensors duration of inactivity emergency EnOcean functionalities genuine false alarms graph graphical user interface home automation components implemented inactivity duration inactivity level inactivity monitoring inactivity patterns individual installed Kaiserslautern project KNX bus KNX standard long-term patterns malfunctions maximum mean value-based MITs months Moreover motion detectors mSLATs MTFA 15 multi-day patterns needs number of false Nutzer observed OMIP outlier-free outliers overall PAUL periods of inactivity potential presence FSM proof tests raising alarms result roller blinds safety sensor data shown in Fig singular events SLAT sleep Spellerberg summer Table telegrams tenant three longest inactivity triggered University of Kaiserslautern user behaviour user’s wall switches waterfall model winter wireless