Continuous Classification of Spatio-temporal Data Streams Using Liquid State Machines

Autor:	Stefan Schliebs, Doug Hunt
Rok vydání:	2012
Předmět:	Activity recognition Liquid state machine Computer science Problem domain Data mining computer.software_genre computer Classifier (UML) Temporal database
Zdroj:	Neural Information Processing ISBN: 9783642344770 ICONIP (4)
DOI:	10.1007/978-3-642-34478-7_76
Popis:	This paper proposes to use a Liquid State Machine (LSM) to classify inertial sensor data collected from horse riders into activities of interest. LSM was shown to be an effective classifier for spatio-temporal data and efficient hardware implementations on custom chips have been presented in literature that would enable relative easy integration into wearable technologies. We explore here the general method of applying LSM technology to domain constrained activity recognition using a synthetic data set. The aim of this study is to provide a proof of concept illustrating the applicability of LSM for the chosen problem domain.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::74022c77cdaad36402c7b8c714996a25 https://doi.org/10.1007/978-3-642-34478-7_76 Zobrazit plný text záznamu