You Are Sensing, but Are You Biased?
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Publication Date
2018-03-26Journal Title
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
ISSN
2474-9567
Publisher
Association for Computing Machinery
Volume
2
Issue
1
Pages
1-26
Language
en
Type
Article
This Version
AM
Metadata
Show full item recordCitation
Grammenos, A., Mascolo, C., & Crowcroft, J. (2018). You Are Sensing, but Are You Biased?. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2 (1), 1-26. https://doi.org/10.1145/3191743
Abstract
Mobile devices are becoming pervasive to our daily lives: they follow us everywhere and we use them for much more than
just communication. These devices are also equipped with a myriad of different sensors that have the potential to allow the
tracking of human activities, user patterns, location, direction and much more. Following this direction, many movements
including sports, quantified self, and mobile health ones are starting to heavily rely on this technology, making it pivotal that
the sensors offer high accuracy.
However, heterogeneity in hardware manufacturing, slight substrate differences, electronic interference as well as external
disturbances are just few of the reasons that limit sensor output accuracy which in turn hinders sensor usage in applications
which need very high granularity and precision, such as quantified-self applications. Although, calibration of sensors is a
widely studied topic existing methods applicable to mobile devices not only require user interaction but they are also not
adaptive to changes. Additionally, alternative approaches for performing more granular and accurate sensing exploit body-
wide sensor networks using mobile phones and additional sensors; as one can imagine these techniques can be bulky, tedious
and not particularly user friendly. Moreover, existing techniques for performing data corrections post-acquisition can produce
inconsistent results as they miss important context from the device itself; which when used, has been shown to produce better
results.
In this paper we introduce a novel approach that exploits machine learning techniques to performan adaptive auto-calibration
scheme for sensors with which achieves high output sensor accuracy when compared to state of the art techniques without
requiring any user interaction or special equipment beyond device itself. Moreover, the energy costs associated with our
approach are lower than the alternatives (such as Kalman filter based solutions) thus enabling our technique to be used efficiently
on a wide variety of devices Finally, our evaluation illustrates that calibrated signals offer a tangible benefit in classification
accuracy, ranging from 3 to 10%, over uncalibrated ones when using state of the art classifiers; we showthat for similar activities
which are hard to distinguish otherwise, we reach an accuracy of > 95% where uncalibrated data classification only reaches
85%. This can be a make or break factor in the use of accelerometer data in health applications.
Sponsorship
Thisworkwas supported by The Alan Turing Institute under grants: TU/C/000003, TU/B/000069, and EP/N510129/1.
Funder references
Alan Turing Institute (unknown)
Alan Turing Institute (unknown)
Identifiers
External DOI: https://doi.org/10.1145/3191743
This record's URL: https://www.repository.cam.ac.uk/handle/1810/275931
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