title: Activity Classification Using Mobile Phone based Motion Sensing and Distributed Computing.
abstract: In this work we present a system that uses the accelerometer embedded in a mobile phone to perform activity recognition, with the purpose of continuously and pervasively monitoring the users' level of physical activity in their everyday life. Several classification algorithms are analysed and their performance measured, based for 6 different activities, namely walking, running, climbing stairs, descending stairs, sitting and standing. Feature selection has also been explored in order to minimize computational load, which is one of the main concerns given the restrictions of smartphones in terms of processor capabilities and specially battery life.
authors.affiliation: Vicomtech-IK4 Research Centre, Mikeletegi Pasealekua 57, 20009 San Sebastian, Spain.
authors.forename: Arkaitz
authors.initials: A
authors.lastname: Artetxe
authors.affiliation: Vicomtech-IK4 Research Centre, Mikeletegi Pasealekua 57, 20009 San Sebastian, Spain.
authors.forename: Andoni
authors.initials: A
authors.lastname: Beristain
authors.affiliation: Vicomtech-IK4 Research Centre, Mikeletegi Pasealekua 57, 20009 San Sebastian, Spain.
authors.forename: Luis
authors.initials: L
authors.lastname: Kabongo
mesh_terms: D061725:Accelerometry / Q000295:instrumentation / Q000379:methods; D056044:Actigraphy / Q000295:instrumentation* / Q000379:methods; D000328:Adult; D000465:Algorithms; D040421:Cell Phone / Q000295:instrumentation*; D003195:Computer Communication Networks / Q000295:instrumentation*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D011211:Electric Power Supplies; D000072936:Fitness Trackers; D006801:Humans; D000069550:Machine Learning; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software