Efficiency of deep neural networks for joint angle modeling in digital gait assessment

Efficiency of deep neural networks for joint angle modeling in digital gait assessment

Abstract Reliability and user compliance of the applied sensor system are two key issues of digital healthcare and biomedical informatics. For gait assessment applications, accurate joint angle measurements are important. Inertial measurement units (IMUs) have been used in a variety of applications...

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Bibliographic Details
Main Authors: Javier Conte Alcaraz, Sanam Moghaddamnia, Jürgen Peissig
Format: Article
Language:English
Published: SpringerOpen 2021-02-01
Series:EURASIP Journal on Advances in Signal Processing
Subjects:
Deep neural network
Digital gait analysis
Machine learning
Nonlinear modeling
Inertial measurement unit
Online Access:https://doi.org/10.1186/s13634-020-00715-1

Internet

https://doi.org/10.1186/s13634-020-00715-1

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