Online Change Detection for Timely Solicitation of User Interaction

Timothy Patterson; Sally McClean; CD Nugent; Shuai Zhang; Leo Galway; Ian Cleland

doi:10.1007/978-3-319-13102-3_21

Online Change Detection for Timely Solicitation of User Interaction

Timothy Patterson, Sally McClean, CD Nugent, Shuai Zhang, Leo Galway, Ian Cleland

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

151 Downloads (Pure)

Abstract

The accurate detection of changes has the potential to form a fundamental component of systems which autonomously solicit user interaction based on transitions within an input stream, for example accelerometry data obtained from a mobile device. This solicited interaction may be utilized for diverse scenarios such as responding to changes in a patient's vital signs within a medical domain or requesting activity labels for generating real-world labelled datasets. Within this paper a change detection algorithm is presented which does not require knowledge of the underlying distributions, can run in online scenarios and considers multivariate datastreams. Results are presented demonstrating practicable potential with 99.81% accuracy and 60% precision for real-world accelerometry data.

Original language	English
Title of host publication	Unknown Host Publication
Publisher	Springer
Pages	116-123
Number of pages	8
Volume	8867
ISBN (Print)	978-3-319-13102-3
DOIs	https://doi.org/10.1007/978-3-319-13102-3_21
Publication status	Published (in print/issue) - 5 Dec 2014
Event	8th International Conference on Ubiquitous Computing & Ambient Intelligence - Belfast, Northern Ireland Duration: 5 Dec 2014 → …

Conference

Conference	8th International Conference on Ubiquitous Computing & Ambient Intelligence
Period	5/12/14 → …

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/978-3-319-13102-3_21

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title = "Online Change Detection for Timely Solicitation of User Interaction",

abstract = "The accurate detection of changes has the potential to form a fundamental component of systems which autonomously solicit user interaction based on transitions within an input stream, for example accelerometry data obtained from a mobile device. This solicited interaction may be utilized for diverse scenarios such as responding to changes in a patient's vital signs within a medical domain or requesting activity labels for generating real-world labelled datasets. Within this paper a change detection algorithm is presented which does not require knowledge of the underlying distributions, can run in online scenarios and considers multivariate datastreams. Results are presented demonstrating practicable potential with 99.81% accuracy and 60% precision for real-world accelerometry data.",

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AU - McClean, Sally

AU - Nugent, CD

AU - Zhang, Shuai

AU - Galway, Leo

AU - Cleland, Ian

PY - 2014/12/5

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N2 - The accurate detection of changes has the potential to form a fundamental component of systems which autonomously solicit user interaction based on transitions within an input stream, for example accelerometry data obtained from a mobile device. This solicited interaction may be utilized for diverse scenarios such as responding to changes in a patient's vital signs within a medical domain or requesting activity labels for generating real-world labelled datasets. Within this paper a change detection algorithm is presented which does not require knowledge of the underlying distributions, can run in online scenarios and considers multivariate datastreams. Results are presented demonstrating practicable potential with 99.81% accuracy and 60% precision for real-world accelerometry data.

AB - The accurate detection of changes has the potential to form a fundamental component of systems which autonomously solicit user interaction based on transitions within an input stream, for example accelerometry data obtained from a mobile device. This solicited interaction may be utilized for diverse scenarios such as responding to changes in a patient's vital signs within a medical domain or requesting activity labels for generating real-world labelled datasets. Within this paper a change detection algorithm is presented which does not require knowledge of the underlying distributions, can run in online scenarios and considers multivariate datastreams. Results are presented demonstrating practicable potential with 99.81% accuracy and 60% precision for real-world accelerometry data.

U2 - 10.1007/978-3-319-13102-3_21

DO - 10.1007/978-3-319-13102-3_21

M3 - Conference contribution

SN - 978-3-319-13102-3

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PB - Springer

T2 - 8th International Conference on Ubiquitous Computing & Ambient Intelligence

Y2 - 5 December 2014

ER -

Online Change Detection for Timely Solicitation of User Interaction

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UN SDGs

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