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Variants of Dynamic Time Warping and their Performance in Human Movement Assessment
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). (DISA;DISA-IDP;DISTA)ORCID iD: 0000-0002-8591-1035
Softwerk AB.
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). (DISA;DISA-IDP;DISTA)ORCID iD: 0000-0001-9062-1609
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). (DISA;DISA-IDP;DISTA)ORCID iD: 0000-0002-7565-3714
2019 (English)In: 21st International Conference on Artificial Intelligence (ICAI'19: July 29 - August 1, 2019, las Vegas, USA), CSREA Press, 2019Conference paper, Published paper (Refereed)
Abstract [en]

The advent of commodity 3D sensor technology enabled, amongst other things, the efficient and effective assessment of human movements. Statistical and machine learning approaches map recorded movement instances to expert scores to train models for the automated assessment of new movements. However, there are many variations in selecting the approaches and setting the parameters for achieving good performance, i.e., high scoring accuracy and low response time. The present paper researches the design space and the impact of sequence alignment on accuracy and response time. More specifically, we introduce variants of Dynamic Time Warping (DTW) for aligning the phases of slow and fast movement instances and assess their effect on the scoring accuracy and response time. Results show that an automated stripping of leading and trailing frames not belonging to the movement (using one DTW variant) followed by an alignment of selected frames in the movements (based on another DTW variant) outperforms the original DTW and other suggested variants thereof. Since these results are independent of the selected learning approach and do not rely on the movement specifics, the results can help improving the performance of automated human movement assessment, in general.

Place, publisher, year, edition, pages
CSREA Press, 2019.
Keywords [en]
Dynamic Time Warping variants, human movement assessment
National Category
Computer Sciences
Research subject
Computer and Information Sciences Computer Science
Identifiers
URN: urn:nbn:se:lnu:diva-89181OAI: oai:DiVA.org:lnu-89181DiVA, id: diva2:1352268
Conference
21st International Conference on Artificial Intelligence, ICAI'19: July 29 - August 1, 2019, las Vegas, USA
Available from: 2019-09-18 Created: 2019-09-18 Last updated: 2019-09-18

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Hagelbäck, JohanLincke, AlisaLöwe, Welf

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
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  • Other locale
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Output format
  • html
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  • asciidoc
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