title: Human Activity Recognition in AAL Environments Using Random Projections.
abstract: Automatic human activity recognition systems aim to capture the state of the user and its environment by exploiting heterogeneous sensors attached to the subject's body and permit continuous monitoring of numerous physiological signals reflecting the state of human actions. Successful identification of human activities can be immensely useful in healthcare applications for Ambient Assisted Living (AAL), for automatic and intelligent activity monitoring systems developed for elderly and disabled people. In this paper, we propose the method for activity recognition and subject identification based on random projections from high-dimensional feature space to low-dimensional projection space, where the classes are separated using the Jaccard distance between probability density functions of projected data. Two HAR domain tasks are considered: activity identification and subject identification. The experimental results using the proposed method with Human Activity Dataset (HAD) data are presented.
authors.affiliation: Department of Software Engineering, Kaunas University of Technology, LT-51368 Kaunas, Lithuania.
authors.forename: Robertas
authors.initials: R
authors.lastname: Damaševičius
authors.affiliation: Department of Software Engineering, Kaunas University of Technology, LT-51368 Kaunas, Lithuania.
authors.forename: Mindaugas
authors.initials: M
authors.lastname: Vasiljevas
authors.affiliation: Department of Software Engineering, Kaunas University of Technology, LT-51368 Kaunas, Lithuania.
authors.forename: Justas
authors.initials: J
authors.lastname: Šalkevičius
authors.affiliation: Institute of Mathematics, Faculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland.
authors.forename: Marcin
authors.initials: M
authors.lastname: Woźniak
mesh_terms: D061725:Accelerometry; D000203:Activities of Daily Living; D000328:Adult; D000368:Aged; D000465:Algorithms; D006233:Persons with Disabilities; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D018670:Monitoring, Ambulatory / Q000295:instrumentation / Q000379:methods*; D010363:Pattern Recognition, Automated; D025341:Principal Component Analysis; D012372:ROC Curve; D012656:Self-Help Devices; D013223:Statistics as Topic; D055815:Young Adult