Show simple item record  

dc.contributor.authorReichherzer, Thomasen_NZ
dc.contributor.authorTimm, Mikaylaen_NZ
dc.contributor.authorEarley, Nathanen_NZ
dc.contributor.authorReyes, Nathanielen_NZ
dc.contributor.authorKumar, Vimalen_NZ
dc.contributor.editorBossard, Antoineen_NZ
dc.contributor.editorLee, Gordonen_NZ
dc.contributor.editorMiller, Lesen_NZ
dc.coverage.spatialHonolulu, Hawaiien_NZ
dc.date.accessioned2017-06-18T22:00:11Z
dc.date.available2017en_NZ
dc.date.available2017-06-18T22:00:11Z
dc.date.issued2017en_NZ
dc.identifier.citationReichherzer, T., Timm, M., Earley, N., Reyes, N., & Kumar, V. (2017). Using machine learning techniques to track individuals & their fitness activities. In A. Bossard, G. Lee, & L. Miller (Eds.), Proceedings of 32nd International Conference on Computers and Their Applications, March, 20-22, 2017, Honolulu, Hawaii, USA (pp. 119–124). Winona, MN, USA: ISCA.en
dc.identifier.isbn978-1-5108-3666-2en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/11113
dc.description.abstractThe use of wearable devices for fitness and health tracking is on an upward curve with a range of devices now available from a number of manufacturers. The devices work with smart devices to exchange data via Bluetooth communication protocol. This paper presents the results of an initial study on the security and privacy weaknesses of wearable fitness devices. It discusses methods to 1) capture and process data sent from a wearable device to its paired smartphone during synchronization and 2) analyze the records to track individuals and make predictions. The data analysis methods use supervised machine-learning techniques to train a classifier for associating synchronization records with the individuals, their physical activities, and conditions under which they were performed. Results of the study show that the methods allow individuals and their activities to be tracked, both of which infringe on the privacy of the user. The paper also provides recommendations on improving the security of wearable devices based on the initial research results.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherISCA
dc.rightsThis is the author's accepted version. Copyright © 2017 by the International Society for Computers and Their Applications.
dc.sourceCATA 2017en_NZ
dc.subjectwearable fitness device
dc.subjectsecurity
dc.subjectprivacy
dc.subjecttracking
dc.subjectmachine-learning techniques
dc.titleUsing machine learning techniques to track individuals & their fitness activitiesen_NZ
dc.typeConference Contribution
dc.relation.isPartOfProceedings of 32nd International Conference on Computers and Their Applicationsen_NZ
pubs.begin-page119
pubs.elements-id193642
pubs.end-page124
pubs.finish-date2017-03-22en_NZ
pubs.place-of-publicationWinona, MN, USA
pubs.publication-statusPublisheden_NZ
pubs.start-date2017-03-20en_NZ


Files in this item

This item appears in the following Collection(s)

Show simple item record