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Vibroarthrography using Convolutional Neural Networks

: Kraft, Dimitri; Bieber, Gerald


Association for Computing Machinery -ACM-; National Science Foundation -NSF-:
PETRA 2020, 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments. Conference Proceedings : June 30 - July 3, 2020, Virtual Conference
New York: ACM, 2020
ISBN: 978-1-4503-7773-7
Art. 58, 6 pp.
International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) <13, 2020, Online>
International Workshop on Human Behaviour Monitoring, Interpretation and Understanding <5, 2020, Online>
Bundesministerium fur Wirtschaft und Energie BMWi (Deutschland)
ZIM; 16KN04913; MOREBA
Conference Paper
Fraunhofer IGD ()
Lead Topic: Individual Health; Research Line: Human computer interaction (HCI); artificial intelligence (AI); health aspects; neural networks; pattern recognition

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.