title: A computationally light classification method for mobile wellness platforms.
abstract: The core of activity recognition in mobile wellness devices is a classification engine which maps observations from sensors to estimated classes. There exists a vast number of different classification algorithms that can be used for this purpose in the machine learning literature. Unfortunately, the computational and space requirements of these methods are often too high for the current mobile devices. In this paper we study a simple linear classifier and find, automatically with SFS and SFFS feature selection methods, a suitable set of features to be used with the classification method. The results show that the simple classifier performs comparable to more complex nonlinear k-Nearest Neighbor Classifier. This depicts great potential in implementing the classifier in small mobile wellness devices.
authors.affiliation: VTT Technical Research Centre of Finland, P.O. Box 1100, FI-90571 Oulu, Finland.
authors.forename: Ville
authors.initials: V
authors.lastname: Könönen
authors.forename: Jani
authors.initials: J
authors.lastname: Mäntyjärvi
authors.forename: Heidi
authors.initials: H
authors.lastname: Similä
authors.forename: Juha
authors.initials: J
authors.lastname: Pärkkä
authors.forename: Miikka
authors.initials: M
authors.lastname: Ermes
mesh_terms: D000465:Algorithms; D020000:Decision Support Systems, Clinical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006293:Health Promotion / Q000379:methods*; D006801:Humans; D018670:Monitoring, Ambulatory / Q000379:methods*; D009043:Motor Activity; D010363:Pattern Recognition, Automated / Q000379:methods*