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Variants of Dynamic Time Warping and their Performance in Human Movement Assessment
Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM). (DISA;DISA-IDP;DISTA)ORCID-id: 0000-0002-8591-1035
Softwerk AB.
Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM). (DISA;DISA-IDP;DISTA)ORCID-id: 0000-0001-9062-1609
Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM). (DISA;DISA-IDP;DISTA)ORCID-id: 0000-0002-7565-3714
2019 (Engelska)Ingår i: 21st International Conference on Artificial Intelligence (ICAI'19: July 29 - August 1, 2019, las Vegas, USA), CSREA Press, 2019Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Ort, förlag, år, upplaga, sidor
CSREA Press, 2019.
Nyckelord [en]
Dynamic Time Warping variants, human movement assessment
Nationell ämneskategori
Datavetenskap (datalogi)
Forskningsämne
Data- och informationsvetenskap
Identifikatorer
URN: urn:nbn:se:lnu:diva-89181OAI: oai:DiVA.org:lnu-89181DiVA, id: diva2:1352268
Konferens
21st International Conference on Artificial Intelligence, ICAI'19: July 29 - August 1, 2019, las Vegas, USA
Tillgänglig från: 2019-09-18 Skapad: 2019-09-18 Senast uppdaterad: 2019-09-18

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Hagelbäck, JohanLincke, AlisaLöwe, Welf

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Hagelbäck, JohanLincke, AlisaLöwe, Welf
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