2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC) 2017
DOI: 10.1109/pimrc.2017.8292272
|View full text |Cite
|
Sign up to set email alerts
|
Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

0
23
0

Year Published

2019
2019
2023
2023

Publication Types

Select...
5
4

Relationship

2
7

Authors

Journals

citations
Cited by 39 publications
(23 citation statements)
references
References 12 publications
0
23
0
Order By: Relevance
“…Our approach is to authenticate a user based on coarse-grained (i.e., one sample per minute instead of multiple samples per second or millisecond) processed (i.e., not raw) biometric data. We train and test different authentication models with different feature sets using a Support Vector Machine (SVM) classifier, which was found to be the most accurate in our previous work [5]. Our analysis using data from over 400 Fitbit users shows that our multi-biometric-based implicit approach is able to authenticate subjects with an average accuracy of about .93 (sedentary) and .90 (non-sedentary).…”
Section: Introductionmentioning
confidence: 97%
“…Our approach is to authenticate a user based on coarse-grained (i.e., one sample per minute instead of multiple samples per second or millisecond) processed (i.e., not raw) biometric data. We train and test different authentication models with different feature sets using a Support Vector Machine (SVM) classifier, which was found to be the most accurate in our previous work [5]. Our analysis using data from over 400 Fitbit users shows that our multi-biometric-based implicit approach is able to authenticate subjects with an average accuracy of about .93 (sedentary) and .90 (non-sedentary).…”
Section: Introductionmentioning
confidence: 97%
“…The NetHealth study [9], [13], [14], [32], [33], [34], [35] began at the University of Notre Dame in 2015 with the purpose of investigating the impacts of "always-on connectivity" on the health habits, emotional wellness, and social ties of college students, over a multi-year period. In this work, we analyzed smartphone and Fitbit data from a set of 467 iPhone users residing in on-campus dormitories during the academic year.…”
Section: Nethealth Study Datasetmentioning
confidence: 99%
“…We then extract the sleep and battery charging status as well as the step count data from the same user during gaps between consecutive sp-clusters and perform a cluster merging technique referred to as Opportunistic Stay Point Clustering, which is fully described in Section 3.3 of this paper. Further, in Section 5, we compare our proposed technique to state-of-the-art solutions using smartphone and Fitbit data collected from a mobile crowd sensing study (called "NetHealth" [14]), where data from over 450 subjects over a 2-year period were collected. Our analysis shows that the proposed approach reduces the cluster count on average by about 64% and increases the cluster time by about 15%, compared to previous solutions.…”
Section: Introductionmentioning
confidence: 99%
“…In [18], the authors have presented a continuous user authentication technique. They have developed an android application for collecting physical activity data from smart bands and smartwatches.…”
Section: Related Workmentioning
confidence: 99%