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Shapelet Based Visual Assessment of Cluster Tendency in Analyzing Complex Upper Limb Motion
conference contribution
posted on 2021-01-01, 00:00 authored by Shreyasi Datta, Chandan KarmakarChandan Karmakar, Punit Rathore, Marimuthu PalaniswamiThe evolution of ubiquitous sensors has led to the generation of copious amounts of waveform data. Human motion waveform analysis has found significance in clinical and home-based activity monitoring. Exploration of cluster structure in such waveform data prior to developing learning models is an important pattern recognition problem. A prominent category of algorithms in this direction, known as Visual Assessment of (cluster) Tendency (VAT), employs visual approaches to study cluster evolution through heat maps. This paper proposes shape-iVAT, a new relative of an improved VAT model, that captures local time-series characteristics through representative subsequences, known as shapelets, to identify interesting patterns in motion data. We propose an unsupervised method for shapelet extraction using maximin shape sampling and shape-based distance computation for selecting key shapelets representing characteristic motion patterns. These shapelets are used to transform waveform data into a dissimilarity matrix for VAT evaluation. We demonstrate that the proposed method outperforms standard VAT with global distance measures for identifying complex upper limb motion captured using a camera-based motion sensing device. We also show that our method has significance in efficient and interpretable cluster tendency assessment for anomaly detection and continuous motion monitoring.
History
Event
Acoustics, Speech and Signal Processing. Conference (2021 : Toronto, Ontario)Pagination
1315 - 1319Publisher
IEEELocation
Toronto, OntarioPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2021-06-06End date
2021-06-11ISBN-13
9781728176062Language
engPublication classification
E1 Full written paper - refereedTitle of proceedings
ICASSP 2021 :Proceedings of the 2021 IEEE International Conference on Acoustics, Speech and Signal ProcessingUsage metrics
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No categories selectedKeywords
Cluster tendencydistance measuresshapelettime-seriesupper limb motionScience & TechnologyTechnologyAcousticsComputer Science, Artificial IntelligenceComputer Science, Software EngineeringEngineering, Electrical & ElectronicImaging Science & Photographic TechnologyComputer ScienceEngineeringVATno CORE2020
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