Segmentation of Human Motion Acceleration with Probabilistic Classifier
Result description
This paper describes a method for signal segmentation in human motion analysis. Proposed method uses a probabilistic change point estimator combined with a Trigg’s tracking signal for detection of changes in a signal variation and segmentation to the subsections by these change points. Main usage of this method is in fields of sport training or health condition monitoring but it can be also used in technical monitoring.
Keywords
Signal segmentationChange point detectionMotion analysisTrigg’s tracking signal
The result's identifiers
Result code in IS VaVaI
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Segmentation of Human Motion Acceleration with Probabilistic Classifier
Original language description
This paper describes a method for signal segmentation in human motion analysis. Proposed method uses a probabilistic change point estimator combined with a Trigg’s tracking signal for detection of changes in a signal variation and segmentation to the subsections by these change points. Main usage of this method is in fields of sport training or health condition monitoring but it can be also used in technical monitoring.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20201 - Electrical and electronic engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
ENGINEERING MECHANICS 2017
ISBN
978-80-214-5497-2
ISSN
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e-ISSN
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Number of pages
1150
Pages from-to
566-569
Publisher name
Brno University of Technology
Place of publication
Brno
Event location
Svratka
Event date
May 15, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000411657600131
Basic information
Result type
D - Article in proceedings
OECD FORD
Electrical and electronic engineering
Year of implementation
2017