This paper is in the following e-collection/theme issue:

New Methods (944) Ethics, Privacy, and Legal Issues (479) Research Instruments, Questionnaires, and Tools (576)

Published on in Vol 22, No 5 (2020): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/16669, first published .
Autoencoder as a New Method for Maintaining Data Privacy While Analyzing Videos of Patients With Motor Dysfunction: Proof-of-Concept Study

Autoencoder as a New Method for Maintaining Data Privacy While Analyzing Videos of Patients With Motor Dysfunction: Proof-of-Concept Study

Autoencoder as a New Method for Maintaining Data Privacy While Analyzing Videos of Patients With Motor Dysfunction: Proof-of-Concept Study

Authors of this article:

Marcus D'Souza1 Author Orcid Image ;   Caspar E P Van Munster2 Author Orcid Image ;   Jonas F Dorn3 Author Orcid Image ;   Alexis Dorier3 Author Orcid Image ;   Christian P Kamm4, 5 Author Orcid Image ;   Saskia Steinheimer5 Author Orcid Image ;   Frank Dahlke3 Author Orcid Image ;   Bernard M J Uitdehaag2 Author Orcid Image ;   Ludwig Kappos1 Author Orcid Image ;   Matthew Johnson6 Author Orcid Image
Article Authors Cited by (3) Tweetations (4) Metrics

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  3. Zhang S, Xu T, Zhu J, Sun Y, Jin P, Shi B, Pei D. Privacy-preserving MTS anomaly detection for network devices through federated learning. Information Sciences 2025;690:121590 View