التفاصيل البيبلوغرافية
| العنوان: |
Artificial Intelligence Distinguishes Pathological Gait: The Analysis of Markerless Motion Capture Gait Data Acquired by an iOS Application (TDPT-GT) |
| المؤلفون: |
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 |
| المصدر: |
Sensors; Volume 23; Issue 13; Pages: 6217 |
| بيانات النشر: |
Multidisciplinary Digital Publishing Institute |
| سنة النشر: |
2023 |
| المجموعة: |
MDPI Open Access Publishing |
| مصطلحات موضوعية: |
artificial intelligence, motion tracking, markerless motion capture, quantitative gait assessment, smartphone device, neuromuscular diseases |
| الوصف: |
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 Hospital, Shiga University, and Takahata Town, patients with idiopathic normal pressure hydrocephalus (n = 48), Parkinson’s disease (n = 21), and other neuromuscular diseases (n = 45) comprised the pathological gait group (n = 114), and the control group consisted of 160 healthy volunteers. iPhone application TDPT-GT captured the subjects walking in a circular path of about 1 meter in diameter, a markerless motion capture system, with an iPhone camera, which generated the three-axis 30 frames per second (fps) relative coordinates of 27 body points. A light gradient boosting machine (Light GBM) with stratified k-fold cross-validation (k = 5) was applied for gait collection for about 1 min per person. The median ability model tested 200 frames of each person’s data for its distinction capability, which resulted in the area under a curve of 0.719. The pathological gait captured by the iPhone could be distinguished by artificial intelligence. |
| نوع الوثيقة: |
text |
| وصف الملف: |
application/pdf |
| اللغة: |
English |
| العلاقة: |
Sensors Development; https://dx.doi.org/10.3390/s23136217Test |
| DOI: |
10.3390/s23136217 |
| الإتاحة: |
https://doi.org/10.3390/s23136217Test |
| حقوق: |
https://creativecommons.org/licenses/by/4.0Test/ |
| رقم الانضمام: |
edsbas.DDEE844D |
| قاعدة البيانات: |
BASE |
