Security-enhanced machine learning model for diagnosis of knee joint disorders using vibroarthrographic signals
Author:
Publisher
Springer Science and Business Media LLC
Subject
Geometry and Topology,Theoretical Computer Science,Software
Link
https://link.springer.com/content/pdf/10.1007/s00500-023-07934-2.pdf
Reference24 articles.
1. Alphonse B, Rajagopal V, Sengan S et al. (2021) Modeling and multi-class classification of vibroarthographic signals via time domain curvilinear divergence random forest. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-020-02869-0
2. Athavale Y, Krishnan S (2020) A telehealth system framework for assessing knee-joint conditions using vibroarthrographic signals. Biomed Signal Process Control 55:101580, ISSN 1746–8094. https://doi.org/10.1016/j.bspc.2019.101580
3. Athavale Y, Krishnan S (2018) A device-independent efficient actigraphy signal-encoding system for applications in monitoring daily human activities and health. Sensors 18:2966. https://doi.org/10.3390/s18092966
4. Balajee A, Venkatesan R (2021) Machine learning based identification and classification of disorders in the human knee joint—computational approach. Soft Comput 25:13001–13013. https://doi.org/10.1007/s00500-021-06134-0
5. Befrui N, Elsner J, Flesser A et al (2018) Vibroarthrography for early detection of knee osteoarthritis using normalized frequency features. Med Biol Eng Comput 56:1499–1514. https://doi.org/10.1007/s11517-018-1785-4
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