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Activity Recognitio...
Activity Recognition Using Biomechanical Model Based Pose Estimation
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- Reiss, Attila (author)
- German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany
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- Hendeby, Gustaf, 1978- (author)
- German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany
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- Bleser, Gabriele (author)
- German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany
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- Stricker, Didier (author)
- German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany
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(creator_code:org_t)
- Berlin, Heidelberg : Springer Berlin/Heidelberg, 2010
- 2010
- English.
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In: Smart Sensing and Context, 2010. - Berlin, Heidelberg : Springer Berlin/Heidelberg. - 9783642169816 - 9783642169823 ; , s. 42-55
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http://liu.diva-port...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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- In this paper, a novel activity recognition method based on signal-oriented and model-based features is presented. The model-based features are calculated from shoulder and elbow joint angles and torso orientation, provided by upper-body pose estimation based on a biomechanical body model. The recognition performance of signal-oriented and model-based features is compared within this paper, and the potential of improving recognition accuracy by combining the two approaches is proved: the accuracy increased by 4–6% for certain activities when adding model-based features to the signal-oriented classifier. The presented activity recognition techniques are used for recognizing 9 everyday and fitness activities, and thus can be applied for e.g., fitness applications or ‘in vivo’ monitoring of patients.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)
Publication and Content Type
- ref (subject category)
- kon (subject category)
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