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

Author:

Iseki Chifumi12ORCID,Hayasaka Tatsuya3ORCID,Yanagawa Hyota4,Komoriya Yuta3,Kondo Toshiyuki1,Hoshi Masayuki5ORCID,Fukami Tadanori6,Kobayashi Yoshiyuki7,Ueda Shigeo8ORCID,Kawamae Kaneyuki9,Ishikawa Masatsune1011,Yamada Shigeki111213ORCID,Aoyagi Yukihiko14,Ohta Yasuyuki1

Affiliation:

1. Division of Neurology and Clinical Neuroscience, Department of Internal Medicine III, Yamagata University School of Medicine, Yamagata 990-2331, Japan

2. Department of Behavioral Neurology and Cognitive Neuroscience, Tohoku University Graduate School of Medicine, Sendai 980-8575, Japan

3. Department of Anesthesiology, Yamagata University School of Medicine, Yamagata 990-2331, Japan

4. Department of Medicine, Yamagata University School of Medicine, Yamagata 990-2331, Japan

5. Department of Physical Therapy, Fukushima Medical University School of Health Sciences, 10-6 Sakaemachi, Fukushima 960-8516, Japan

6. Department of Informatics, Faculty of Engineering, Yamagata University, Yonezawa 992-8510, Japan

7. Human Augmentation Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Kashiwa II Campus, University of Tokyo, Kashiwa 277-0882, Japan

8. Shin-Aikai Spine Center, Katano Hospital, Katano 576-0043, Japan

9. Department of Anesthesia and Critical Care Medicine, Ohta-Nishinouti Hospital, Koriyama 963-8558, Japan

10. Rakuwa Villa Ilios, Rakuwakai Healthcare System, Kyoto 607-8062, Japan

11. Normal Pressure Hydrocephalus Center, Rakuwakai Otowa Hospital, Kyoto 607-8062, Japan

12. Department of Neurosurgery, Nagoya City University Graduate School of Medical Science, Nagoya 467-8601, Japan

13. Interfaculty Initiative in Information Studies, Institute of Industrial Science, The University of Tokyo, Tokyo 113-8654, Japan

14. Digital Standard Co., Ltd., Osaka 536-0013, Japan

Abstract

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.

Funder

Japan Society for the Promotion of Science, KAKENHI

G-7 Scholarship Foundation

Taiju Life Social Welfare Foundation

Osaka Gas Group Welfare Foundation

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

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