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  4. Vibroarthrography using Convolutional Neural Networks
 
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2020
Conference Paper
Title

Vibroarthrography using Convolutional Neural Networks

Abstract
Knees, hip, and other human joints generate noise and vibration while they move. The vibration and sound pattern is characteristic not only for the type of joint but also for the condition. The pattern vary due to abrasion, damage, injury, and other causes. Therefore, the vibration and sound analysis, also known as vibroarthrography (VAG), provides information and possible conclusions about the joint condition, age and health state. The analysis of the pattern is very sophisticated and complex and so approaches of machine learning techniques were applied before. In this paper, we are using convolutional neural networks for the analysis of vibroarthrographic signals and compare the results with already known machine learning techniques.
Author(s)
Kraft, Dimitri  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Bieber, Gerald  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Mainwork
PETRA 2020, 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments. Conference Proceedings  
Project(s)
MOREBA
Funder
Bundesministerium für Wirtschaft und Energie BMWi (Deutschland)  
Conference
International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) 2020  
International Workshop on Human Behaviour Monitoring, Interpretation and Understanding 2020  
DOI
10.1145/3389189.3397993
Language
English
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Keyword(s)
  • Lead Topic: Individual Health

  • Research Line: Human computer interaction (HCI)

  • artificial intelligence (AI)

  • health aspects

  • neural networks

  • pattern recognition

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