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Foot Trajectory Features in Gait of Parkinson's Disease Patients.
Ogata, Taiki; Hashiguchi, Hironori; Hori, Koyu; Hirobe, Yuki; Ono, Yumi; Sawada, Hiroyuki; Inaba, Akira; Orimo, Satoshi; Miyake, Yoshihiro.
Affiliation
  • Ogata T; Department of Computer Science, Tokyo Institute of Technology, Yokohama, Japan.
  • Hashiguchi H; Department of Computational Intelligence and System Science, Tokyo Institute of Technology, Yokohama, Japan.
  • Hori K; Department of Computational Intelligence and System Science, Tokyo Institute of Technology, Yokohama, Japan.
  • Hirobe Y; Department of Computer Science, Tokyo Institute of Technology, Yokohama, Japan.
  • Ono Y; Department of Computer Science, Tokyo Institute of Technology, Yokohama, Japan.
  • Sawada H; Department of Neurology, Kanto Central Hospital, Tokyo, Japan.
  • Inaba A; Department of Neurology, Kanto Central Hospital, Tokyo, Japan.
  • Orimo S; Department of Neurology, Kanto Central Hospital, Tokyo, Japan.
  • Miyake Y; Department of Computer Science, Tokyo Institute of Technology, Yokohama, Japan.
Front Physiol ; 13: 726677, 2022.
Article in En | MEDLINE | ID: mdl-35600314
Parkinson's disease (PD) is a progressive neurological disorder characterized by movement disorders, such as gait instability. This study investigated whether certain spatial features of foot trajectory are characteristic of patients with PD. The foot trajectory of patients with mild and advanced PD in on-state and healthy older and young individuals was estimated from acceleration and angular velocity measured by inertial measurement units placed on the subject's shanks, just above the ankles. We selected six spatial variables in the foot trajectory: forward and vertical displacements from heel strike to toe-off, maximum clearance, and change in supporting leg (F1 to F3 and V1 to V3, respectively). Healthy young individuals had the greatest F2 and F3 values, followed by healthy older individuals, and then mild PD patients. Conversely, the vertical displacements of mild PD patients were larger than the healthy older individuals. Still, those of healthy older individuals were smaller than the healthy young individuals except for V3. All six displacements of the advanced PD patients were smaller than the mild PD patients. To investigate features in foot trajectories in detail, a principal components analysis and soft-margin kernel support vector machine was used in machine learning. The accuracy in distinguishing between mild PD patients and healthy older individuals and between mild and advanced PD patients was 96.3 and 84.2%, respectively. The vertical and forward displacements in the foot trajectory was the main contributor. These results reveal that large vertical displacements and small forward ones characterize mild and advanced PD patients, respectively.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Front Physiol Year: 2022 Document type: Article Affiliation country: Japan Country of publication: Switzerland

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Front Physiol Year: 2022 Document type: Article Affiliation country: Japan Country of publication: Switzerland