Quantitative Gait Feature Assessment on Two-Dimensional Body Axis Projection Planes Converted from Three-Dimensional Coordinates Estimated with a Deep Learning Smartphone App

Quantitative Gait Feature Assessment on Two-Dimensional Body Axis Projection Planes Converted from Three-Dimensional Coordinates Estimated with a Deep Learning Smartphone App

To assess pathological gaits quantitatively, three-dimensional coordinates estimated with a deep learning model were converted into body axis plane projections. First, 15 healthy volunteers performed four gait patterns; that is, normal, shuffling, short-stepped, and wide-based gaits, with the Three-...

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Bibliographic Details
Main Authors: Shigeki Yamada, Yukihiko Aoyagi, Chifumi Iseki, Toshiyuki Kondo, Yoshiyuki Kobayashi, Shigeo Ueda, Keisuke Mori, Tadanori Fukami, Motoki Tanikawa, Mitsuhito Mase, Minoru Hoshimaru, Masatsune Ishikawa, Yasuyuki Ohta
Format: Article
Language:English
Published: MDPI AG 2023-01-01
Series:Sensors
Subjects:
deep learning
motion tracking
markerless motion capture
quantitative gait assessment
smartphone device
Online Access:https://www.mdpi.com/1424-8220/23/2/617

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