title: Activity recognition with smartphone support.
abstract: In this paper, the authors describe a method of accurately detecting human activity using a smartphone accelerometer paired with a dedicated chest sensor. The design, implementation, testing and validation of a custom mobility classifier are also presented. Offline analysis was carried out to compare this custom classifier to de-facto machine learning algorithms, including C4.5, CART, SVM, Multi-Layer Perceptrons, and Naïve Bayes. A series of trials were carried out in Ireland, initially involving N=6 individuals to test the feasibility of the system, before a final trial with N=24 subjects took place in the Netherlands. The protocol used and analysis of 1165min of recorded activities from these trials are described in detail in this paper. Analysis of collected data indicate that accelerometers placed in these locations, are capable of recognizing activities including sitting, standing, lying, walking, running and cycling with accuracies as high as 98%.
authors.affiliation: Department of Electronic & Computer Engineering, University of Limerick, Limerick, Ireland. Electronic address: JohnJ.Guiry@ul.ie.
authors.forename: John J
authors.initials: JJ
authors.lastname: Guiry
authors.affiliation: Department of Electronic & Computer Engineering, University of Limerick, Limerick, Ireland.
authors.forename: Pepijn
authors.initials: P
authors.lastname: van de Ven
authors.affiliation: Department of Electronic & Computer Engineering, University of Limerick, Limerick, Ireland.
authors.forename: John
authors.initials: J
authors.lastname: Nelson
authors.affiliation: Department of Clinical Psychology, VU University, Amsterdam, Netherlands.
authors.forename: Lisanne
authors.initials: L
authors.lastname: Warmerdam
authors.affiliation: Department of Clinical Psychology, VU University, Amsterdam, Netherlands.
authors.forename: Heleen
authors.initials: H
authors.lastname: Riper
mesh_terms: D061725:Accelerometry / Q000295:instrumentation* / Q000379:methods*; D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D001642:Bicycling / Q000502:physiology; D040421:Cell Phone; D004867:Equipment Design; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D008297:Male; D018670:Monitoring, Ambulatory / Q000295:instrumentation* / Q000379:methods*; D009068:Movement / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods; D011187:Posture / Q000502:physiology; D012420:Running / Q000502:physiology; D013909:Thorax; D016138:Walking / Q000502:physiology
keywords: Accelerometer; Activities of daily living; Physical activity recognition; Smartphone classification