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Lookup NU author(s): Dr Giacomo BergamiORCiD, Emma Packer, Dr Kirsty ScottORCiD, Dr Silvia Del DinORCiD
This is the authors' accepted manuscript of a conference proceedings (inc. abstract) that has been published in its final definitive form by Springer Verlag, 2024.
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While monitoring Parkinsonian patients with wearable sensors and tracking their drug assumption patterns, we want to differentiate the behaviours distinguishing periods of relative well-being from dyskinetic events. This requires solving a novel problem, where an entire multivariate time series (MTS) has its class label varying in time, thus leading to a generalised formulation of multivariate time series classification (MTSC). To achieve explainability, we premier the composition of data trend (DT) analysis with DECLAREd, a log temporal declarative language, to derive human-readable correlations across different MTS dimensions' trends. This is mediated by a novel temporal data representation, polyadic logs, supporting both MTS raw data and concurrent activity-labelled durative activities (constituents) for representing event-based classes and concurrent DTs across MTS dimensions. Our validation over a real patient dataset shows that our MTCS algorithm, EMeriTAte, outperforms state-of-the-art MTSC for a novel patient classification task.
Author(s): Bergami G, Packer E, Scott K, Del Din S
Editor(s): Chbeir R; Ilarri S; Manolopoulos Y; Revesz PZ; Bernardino J; Leung CK;
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: The 28th International Database Engineered Applications Symposium IDEAS 2024
Year of Conference: 2024
Pages: 49-62
Print publication date: 16/03/2025
Online publication date: 28/08/2024
Acceptance date: 30/07/2024
Date deposited: 04/10/2024
ISSN: 0302-9743
Publisher: Springer Verlag
URL: https://doi.org/10.1007/978-3-031-83472-1_4
DOI: 10.1007/978-3-031-83472-1_4
ePrints DOI: 10.57711/6y6g-cw67
Notes: 9783031834721 ebook ISBN.
Library holdings: Search Newcastle University Library for this item
Series Title: Lecture Notes in Computer Science
ISBN: 9783031834714