Artificial Intelligence Distinguishes Pathological Gait: The Analysis of Markerless Motion Capture Gait Data   Acquired by an iOS Application (TDPT-GT)

Distinguishing pathological gait is challenging in neurology because of the difficulty of capturing total body movement and its analysis. We aimed to obtain a convenient recording with an iPhone and establish an algorithm based on deep learning. From May 2021 to November 2022 at Yamagata University...

詳細記述

書誌詳細

出版年:	Sensors
主要な著者:	Chifumi Iseki, Tatsuya Hayasaka, Hyota Yanagawa, Yuta Komoriya, Toshiyuki Kondo, Masayuki Hoshi, Tadanori Fukami, Yoshiyuki Kobayashi, Shigeo Ueda, Kaneyuki Kawamae, Masatsune Ishikawa, Shigeki Yamada, Yukihiko Aoyagi, Yasuyuki Ohta
フォーマット:	論文
言語:	英語
出版事項:	MDPI AG 2023-07-01
主題:	artificial intelligence motion tracking markerless motion capture quantitative gait assessment smartphone device neuromuscular diseases
オンライン･アクセス:	https://www.mdpi.com/1424-8220/23/13/6217