Change point detection using multivariate exponentially weighted moving average (MEWMA) for optimal parameter in online activity monitoring

Naveed Khan; Sally McClean; Shuai Zhang; Chris Nugent

doi:10.1007/978-3-319-48746-5_16

Change point detection using multivariate exponentially weighted moving average (MEWMA) for optimal parameter in online activity monitoring

Naveed Khan^*, Sally McClean, Shuai Zhang, Chris Nugent

^*Corresponding author for this work

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

Abstract

In recent years, wearable sensors are integrating frequently and rapidly into our daily life day by day. Such smart sensors have attracted a lot of interest due to their small sizes and reasonable computational power. For example, body worn sensors are widely used to monitor daily life activities and identify meaningful events. Hence, the capability to detect, adapt and respond to change performs a key role in various domains. A change in activities is signaled by a change in the data distribution within a time window. This change marks the start of a transition from an ongoing activity to a new one. In this paper, we evaluate the proposed algorithm’s scalability on identifying multiple changes in different user activities from real sensor data collected from various subjects. The Genetic algorithm (GA) is used to identify the optimal parameter set for Multivariate Exponentially Weighted Moving Average (MEWMA) approach to detect change points in sensor data. Results have been evaluated using a real dataset of 8 different activities for five different users with a high accuracy from 99.2 % to 99.95 % and G-means from 67.26 % to 83.20 %.

Original language	English
Title of host publication	Ubiquitous Computing and Ambient Intelligence - 10th International Conference, UCAmI 2016, Proceedings
Editors	Pino Caballero-Gil, Carmelo R. García, Alexis Quesada-Arencibia, Mike Burmester
Publisher	Springer Verlag
Pages	156-165
Number of pages	10
ISBN (Print)	9783319487458
DOIs	https://doi.org/10.1007/978-3-319-48746-5_16
Publication status	Published - 02 Nov 2016
Externally published	Yes
Event	10th International Conference on Ubiquitous Computing and Ambient Intelligence, UCAmI 2016 - San Bartolomde Tirajana, Gran Canaria, Spain Duration: 29 Nov 2016 → 02 Dec 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10069 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	10th International Conference on Ubiquitous Computing and Ambient Intelligence, UCAmI 2016
Country	Spain
City	San Bartolomde Tirajana, Gran Canaria
Period	29/11/2016 → 02/12/2016

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2016.

Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.

Keywords

Accelerometer
Activity monitoring
Genetic algorithm
Multiple change points

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-319-48746-5_16Licence: Unspecified

Link to publication in Scopus

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Khan, N., McClean, S., Zhang, S., & Nugent, C. (2016). Change point detection using multivariate exponentially weighted moving average (MEWMA) for optimal parameter in online activity monitoring. In P. Caballero-Gil, C. R. García, A. Quesada-Arencibia, & M. Burmester (Eds.), Ubiquitous Computing and Ambient Intelligence - 10th International Conference, UCAmI 2016, Proceedings (pp. 156-165). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10069 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-48746-5_16

Khan, Naveed ; McClean, Sally ; Zhang, Shuai ; Nugent, Chris. / Change point detection using multivariate exponentially weighted moving average (MEWMA) for optimal parameter in online activity monitoring. Ubiquitous Computing and Ambient Intelligence - 10th International Conference, UCAmI 2016, Proceedings. editor / Pino Caballero-Gil ; Carmelo R. García ; Alexis Quesada-Arencibia ; Mike Burmester. Springer Verlag, 2016. pp. 156-165 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{f20f09883d694f90b8a1c268f3771e92,

title = "Change point detection using multivariate exponentially weighted moving average (MEWMA) for optimal parameter in online activity monitoring",

abstract = "In recent years, wearable sensors are integrating frequently and rapidly into our daily life day by day. Such smart sensors have attracted a lot of interest due to their small sizes and reasonable computational power. For example, body worn sensors are widely used to monitor daily life activities and identify meaningful events. Hence, the capability to detect, adapt and respond to change performs a key role in various domains. A change in activities is signaled by a change in the data distribution within a time window. This change marks the start of a transition from an ongoing activity to a new one. In this paper, we evaluate the proposed algorithm{\textquoteright}s scalability on identifying multiple changes in different user activities from real sensor data collected from various subjects. The Genetic algorithm (GA) is used to identify the optimal parameter set for Multivariate Exponentially Weighted Moving Average (MEWMA) approach to detect change points in sensor data. Results have been evaluated using a real dataset of 8 different activities for five different users with a high accuracy from 99.2 % to 99.95 % and G-means from 67.26 % to 83.20 %.",

keywords = "Accelerometer, Activity monitoring, Genetic algorithm, Multiple change points",

author = "Naveed Khan and Sally McClean and Shuai Zhang and Chris Nugent",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016. Copyright: Copyright 2018 Elsevier B.V., All rights reserved.; 10th International Conference on Ubiquitous Computing and Ambient Intelligence, UCAmI 2016 ; Conference date: 29-11-2016 Through 02-12-2016",

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Khan, N, McClean, S, Zhang, S & Nugent, C 2016, Change point detection using multivariate exponentially weighted moving average (MEWMA) for optimal parameter in online activity monitoring. in P Caballero-Gil, CR García, A Quesada-Arencibia & M Burmester (eds), Ubiquitous Computing and Ambient Intelligence - 10th International Conference, UCAmI 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10069 LNCS, Springer Verlag, pp. 156-165, 10th International Conference on Ubiquitous Computing and Ambient Intelligence, UCAmI 2016, San Bartolomde Tirajana, Gran Canaria, Spain, 29/11/2016. https://doi.org/10.1007/978-3-319-48746-5_16

Change point detection using multivariate exponentially weighted moving average (MEWMA) for optimal parameter in online activity monitoring. / Khan, Naveed; McClean, Sally; Zhang, Shuai; Nugent, Chris.

Ubiquitous Computing and Ambient Intelligence - 10th International Conference, UCAmI 2016, Proceedings. ed. / Pino Caballero-Gil; Carmelo R. García; Alexis Quesada-Arencibia; Mike Burmester. Springer Verlag, 2016. p. 156-165 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10069 LNCS).

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

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AU - McClean, Sally

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AU - Nugent, Chris

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N2 - In recent years, wearable sensors are integrating frequently and rapidly into our daily life day by day. Such smart sensors have attracted a lot of interest due to their small sizes and reasonable computational power. For example, body worn sensors are widely used to monitor daily life activities and identify meaningful events. Hence, the capability to detect, adapt and respond to change performs a key role in various domains. A change in activities is signaled by a change in the data distribution within a time window. This change marks the start of a transition from an ongoing activity to a new one. In this paper, we evaluate the proposed algorithm’s scalability on identifying multiple changes in different user activities from real sensor data collected from various subjects. The Genetic algorithm (GA) is used to identify the optimal parameter set for Multivariate Exponentially Weighted Moving Average (MEWMA) approach to detect change points in sensor data. Results have been evaluated using a real dataset of 8 different activities for five different users with a high accuracy from 99.2 % to 99.95 % and G-means from 67.26 % to 83.20 %.

AB - In recent years, wearable sensors are integrating frequently and rapidly into our daily life day by day. Such smart sensors have attracted a lot of interest due to their small sizes and reasonable computational power. For example, body worn sensors are widely used to monitor daily life activities and identify meaningful events. Hence, the capability to detect, adapt and respond to change performs a key role in various domains. A change in activities is signaled by a change in the data distribution within a time window. This change marks the start of a transition from an ongoing activity to a new one. In this paper, we evaluate the proposed algorithm’s scalability on identifying multiple changes in different user activities from real sensor data collected from various subjects. The Genetic algorithm (GA) is used to identify the optimal parameter set for Multivariate Exponentially Weighted Moving Average (MEWMA) approach to detect change points in sensor data. Results have been evaluated using a real dataset of 8 different activities for five different users with a high accuracy from 99.2 % to 99.95 % and G-means from 67.26 % to 83.20 %.

KW - Accelerometer

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KW - Genetic algorithm

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Khan N, McClean S, Zhang S, Nugent C. Change point detection using multivariate exponentially weighted moving average (MEWMA) for optimal parameter in online activity monitoring. In Caballero-Gil P, García CR, Quesada-Arencibia A, Burmester M, editors, Ubiquitous Computing and Ambient Intelligence - 10th International Conference, UCAmI 2016, Proceedings. Springer Verlag. 2016. p. 156-165. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-48746-5_16