University of Oulu

P. Siirtola, H. Koskimäki and J. Röning, "Personal models for eHealth - improving user-dependent human activity recognition models using noise injection," 2016 IEEE Symposium Series on Computational Intelligence (SSCI), Athens, 2016, pp. 1-7. doi: 10.1109/SSCI.2016.7849944

Personal models for eHealth : improving user-dependent human activity recognition models using noise injection

Saved in:
Author: Siirtola, Pekka1; Koskimäki, Heli1; Röning, Juha1
Organizations: 1Biomimetics and Intelligent Systems Group, P.O. BOX 4500, FI-90014, University of Oulu, Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.1 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2016122031630
Language: English
Published: IEEE, 2016
Publish Date: 2017-03-23
Description:
Abstract—In this paper, a noise injection method to improve personal recognition models is presented. The idea of the method is to build more general recognition models for eHealth using a small original data set and by expanding the area covered by training data using noise injection. This way, it is possible to train models that are less vulnerable to changing conditions, and thus more accurate, but still the data gathering phase can be non-burdensome. The proposed method was tested using two classifiers (linear discriminant analysis and quadratic discriminant analysis) and three human activity recognition data sets collected using inertial sensors of a smartphone. Two of these data sets are open data sets. The results show that noise injection improves the true positive recognition rates. With first data set the improvement varies from 1.3 to 2.0 percentage units, with second from 1.4 to 4.5 percentage units, and with third the highest improvement was 2.5 percentage units. Moreover, the results show that the method improves precision and reduces false positive rates. In addition, experiments were made using different training set sizes to show that the improvement in true positive rate is bigger if the original training data set is small. In this study, the method is experimented using human activity data sets but it is not limited to this application area and can be used with any time series data.
see all

ISBN: 978-1-5090-4240-1
Pages: 1 - 7
DOI: 10.1109/SSCI.2016.7849944
OADOI: https://oadoi.org/10.1109/SSCI.2016.7849944
Host publication: 2016 IEEE Symposium Series on Computational Intelligence (SSCI) : Proceedings
Conference: IEEE symposium on computational intelligence and data mining
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
Subjects:
Funding: The authors would like to thank Infotech Oulu for funding this work.
Copyright information: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.