Peer-Reviewed Journal Details
Mandatory Fields
Guiry, JJ,van de Ven, P,Nelson, J,Warmerdam, L,Riper, H
2014
June
Medical engineering & physics
Activity recognition with smartphone support
Published
()
Optional Fields
Physical activity recognition Smartphone classification Accelerometer Activities of daily living
36
670
675
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 Naive 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 1165 mm 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%. (C) 2014 IPEM. Published by Elsevier Ltd. All rights reserved.
10.1016/j.medengphy.2014.02.009
Grant Details