Ahrabian, A;
Elsaleh, T;
Fathy, Y;
Barnaghi, P;
(2017)
Detecting Changes in the Variance of Multi-Sensory Accelerometer Data Using MCMC.
In:
2017 IEEE SENSORS.
(pp. pp. 1152-1154).
IEEE
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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).
| Type: | Proceedings paper |
|---|---|
| Title: | Detecting Changes in the Variance of Multi-Sensory Accelerometer Data Using MCMC |
| Event: | 16th IEEE SENSORS Conference, 29 October - 1 November 2017, Glasgow, UK |
| Location: | Glasgow, SCOTLAND |
| Dates: | 29 October 2017 - 01 November 2017 |
| ISBN-13: | 9781509010127 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/ICSENS.2017.8234260 |
| Publisher version: | https://doi.org/10.1109/ICSENS.2017.8234260 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Variance Change Detection, Multivariate Change Detection, Sensors, Analytical models, Accelerometers, Data models, Detection algorithms, Indexes, Bayes methods |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10058859 |
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