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Pattern recognition and differential diagnosis of neurological gait disorders in instrument-based and clinical gait analysis

Pattern recognition and differential diagnosis of neurological gait disorders in instrument-based and clinical gait analysis - PREGAIT

Status
Recruiting
Phases
Unknown
Study type
Interventional
Source
DRKS
Registry ID
DRKS00009599
Enrollment
240
Registered
2017-03-14
Start date
2017-03-15
Completion date
Unknown
Last updated
2025-04-07

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

R26

Interventions

Group 1: gait analysis package 1: “video preferred walking speed” Description: Clinically confirmed cases of patients with gait disturbances will be recruited for this study. The patients will underg

Sponsors

Klinikum der Universität München, Campus Großhadern, Neurologische Klinik und Poliklinik und Deutsches Schwindel- und Gleichgewichtszentrum, DSGZ
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to No maximum

Inclusion criteria

Inclusion criteria: patients with confirmed gait disorder

Exclusion criteria

Exclusion criteria: unclear gait disorders

Design outcomes

Primary

MeasureTime frame
The aim of the study is to compare the outcome of clinical gait analysis and the standardized instrument-based gait analysis. Clinically confirmed cases of gait disturbances will be recruited for this study. The patients will undergo different gait measurements walking across a GAITRite® sensor carpet, while standardized gait parameters and video data are being collected. For analysis clinicians will be divided into different groups. Each group will analyze the gait data of different packages with a limited data set. Accuracy of the clinician’s diagnoses will be compared among all groups. Furthermore, an automated pattern recognition system for gait pattern classification will be used to compare the computed results with the clinician’s results.

Secondary

MeasureTime frame
identification of important gait conditions and variables, identification of physician's information, important factors and characteristics for accuracy of diagnoses, automated pattern recognition, comparison between physician's gait analysis and automated pattern recognition analysis

Countries

Germany

Contacts

Public ContactKen Möhwald

Klinikum der Universität München, Campus Großhadern, Deutsches Schwindel- und Gleichgewichtszentrum, DSGZ

Ken.Moehwald@med.uni-muenchen.de+49(0)89/4400-76951

Outcome results

None listed

Source: DRKS (via WHO ICTRP) · Data processed: Feb 4, 2026