Evidence map›Paper›PMID 35600314›Full record

ArticleFrontiers in physiology2022

Foot Trajectory Features in Gait of Parkinson's Disease Patients.

Taiki Ogata, Hironori Hashiguchi, Koyu Hori, Yuki Hirobe, Yumi Ono, Hiroyuki Sawada, Akira Inaba, Satoshi Orimo, Yoshihiro Miyake

Open access · goldAbstract read
In one paragraph

Article in Frontiers in physiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.9field-weighted citation impact, top 26% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 9 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 2 institutions in 1 country.

Taiki OgataDepartment of Computer Science, Tokyo Institute of Technology, Yokohama, Japan.
Hironori HashiguchiDepartment of Computational Intelligence and System Science, Tokyo Institute of Technology, Yokohama, Japan.
Koyu HoriDepartment of Computational Intelligence and System Science, Tokyo Institute of Technology, Yokohama, Japan.
Yuki HirobeDepartment of Computer Science, Tokyo Institute of Technology, Yokohama, Japan.
Yumi OnoDepartment of Computer Science, Tokyo Institute of Technology, Yokohama, Japan.
Hiroyuki SawadaDepartment of Neurology, Kanto Central Hospital, Tokyo, Japan.
Akira InabaDepartment of Neurology, Kanto Central Hospital, Tokyo, Japan.
Satoshi OrimoDepartment of Neurology, Kanto Central Hospital, Tokyo, Japan.
Yoshihiro MiyakeDepartment of Computer Science, Tokyo Institute of Technology, Yokohama, Japan.
Tokyo Institute of Technology · JPKanto Central Hospital · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

foot trajectoryforward displacementgaitinertial measurement unitParkinson’s diseasevertical displacement

Identifiers

PMID35600314
PMCPMC9114796
OpenAlexW4225390284

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.