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.

Preview

Text 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)
Divisions :	Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :	Name Email ORCID Ahrabian, Alireza a.ahrabian@surrey.ac.uk Elsaleh, Tarek T.Elsaleh@surrey.ac.uk Fathy, Yasmin y.fathy@surrey.ac.uk Barnaghi, Payam P.Barnaghi@surrey.ac.uk
Date :	25 December 2017
DOI :	10.1109/ICSENS.2017.8234260
Copyright Disclaimer :	© 2017 IEEE.
Uncontrolled Keywords :	Variance Change Detection; Multivariate Change Detection
Related URLs :	Organisation
Depositing User :	Clive Harris
Date Deposited :	30 Aug 2017 10:14
Last Modified :	18 Oct 2018 10:58
URI:	http://epubs.surrey.ac.uk/id/eprint/842080

Actions (login required)

View Item

Downloads