Detecting Changes in the Variance of Multi-Sensory Accelerometer Data Using MCMC
Ahrabian, Alireza, Elsaleh, Tarek, Fathy, Yasmin and Barnaghi, Payam (2017) Detecting Changes in the Variance of Multi-Sensory Accelerometer Data Using MCMC In: IEEE Sensors 2017, 29 Oct - 01 Nov 2017, Glasgow, Scotland.
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Detecting Changes in the Variance of Multi-Sensory Accelerometer Data Using MCMC.pdf - Accepted version Manuscript Download (369kB) | Preview |
Abstract
An important field in exploratory sensory data analysis is the segmentation of time-series data to identify activities of interest. In this work, we analyse the performance of univariate and multi-sensor Bayesian change detection algorithms in segmenting accelerometer data. In particular, we provide theoretical analysis and also performance evaluation on synthetic data and real-world data. The results illustrate the advantages of using multi-sensory variance change detection in the segmentation of dynamic data (e.g. accelerometer data).
Item Type: | Conference or Workshop Item (Conference Paper) | |||||||||||||||
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Divisions : | Faculty of Engineering and Physical Sciences > Electronic Engineering | |||||||||||||||
Authors : |
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Date : | 1 November 2017 | |||||||||||||||
Copyright Disclaimer : | © 2017 IEEE. | |||||||||||||||
Uncontrolled Keywords : | Variance Change Detection; Multivariate Change Detection | |||||||||||||||
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Depositing User : | Clive Harris | |||||||||||||||
Date Deposited : | 30 Aug 2017 10:14 | |||||||||||||||
Last Modified : | 23 Feb 2018 08:54 | |||||||||||||||
URI: | http://epubs.surrey.ac.uk/id/eprint/842080 |
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