RESEARCH / PARTICIPATIONBack to the library
← Find a study
Recruiting · registry status

Detecting freezing while walking at home

Researchers study whether computer analysis can recognise episodes when people with Parkinson’s become unable to keep walking in their home environment.

Learning by observing or collecting information · Study reference: NCT07580612

Plain-language introduction written with AI from the registry; not independently checked by a clinician. Read the original details below ↓

Open the official registry record ↗

Follow this study

Looking for volunteers

Start (reported actual date)
2025-09-22
Main measurements finished (planned)
2027-06
Study finished (planned)
2027-06

Planned dates can move. A study finishing does not tell us when a paper will be published.

No results summary has been confirmed in the registry records we imported. See connected papers below; we keep checking after recruitment ends.

Changes we have recorded
  • 2026-10-11 — recruiting

These are dates we observed a change, not necessarily the dates it happened.

Papers connected to this study

No connected paper has been found yet. The tracker checks the growing library for study identifiers and registry-linked publications.

Who can join?

Age 18 years and over · Also accepts healthy volunteers

These are starting points, not the full rules. The research team can tell you whether the study is right for your situation.

Read all the rules for taking part

Sex eligibility reported by registry: all

Inclusion Criteria: For all participants * Voluntary written informed consent of the participant has been obtained prior to any study-related procedures, except the non-recorded pre-screening questions; * At least 18 years of age at the time of signing the Informed Consent Form (ICF); * Person is cognitively able to follow and understand instructions and provide voluntary written informed consent; * Person is able to walk for short distances (± 10 meters) independently, with- or without use of a walking aid; * Person does not live in a temporary or permanent care facility. For participants with PD: * Clinical diagnosis of Parkinson's disease (PD) made by a neurologist according to the Movement Disorders Society guidelines; * Person self-reports to experience daily FOG (for recruitment of freezers only); * Person is willing to temporarily delay the morning anti-Parkinsonian medication during the standardized assessment visit. Exclusion criteria: * Occurrence of any of the following within 3 months prior to informed consent: myocardial infarction, hospitalization for unstable angina, stroke, coronary artery bypass graft (CABG), percutaneous coronary intervention (PCI), implantation of a cardiac resynchronization therapy device (CRTD), active treatment for cancer or other malignant disease, uncontrolled congestive heart disease (NYHA class \>3), acute psychosis or major psychiatric disorders or continued substance abuse, other neurological (than PD) or orthopaedic impairment that significantly impacts on gait; * Participant self-reports daily falls; * Participation in another interventional study, with or without an investigational medicinal product (IMP) or device (IMD)
Full study name & original research details

Official study title

Artificial Intelligence-Driven Freezing Of Gait Detection in the Home: Investigating How Free-living Activities Affect the Algorithm

Short title used by the registry

AID-FOG: Artificial Intelligence-Driven Freezing of Gait Detection in the Home

Original description

Freezing of gait (FOG) is a debilitating symptom of Parkinson's disease increases the risk of falling. Despite being a common symptom, it is still difficult to evaluate freezing of gait quickly and accurately. Currently, the gold-standard method to determine the severity of FOG is a manual analysis of video footage by an experienced assessor, collected during standardized FOG-provoking walking tests. Because this is a very time-intensive process, where different assessors sometimes obtain different results, our team at KU Leuven have developed an artificial-intelligent (AI) algorithm trained to identify FOG episodes based on wearable inertial measurement unit (IMU) sensor data. The AI algorithm has already undergone initial validation during laboratory testing, yielding promising results. The aim of this study is to investigate whether the AI algorithm can accurately detect FOG episodes in a less controlled environment, namely the home environment. In a second phase, the investigators will also use the collected data to improve the AI algorithm for automated FOG detection in the home. Finally, the investigators want to explore whether the AI algorithm can detect FOG in real-time.

Conditions reported: Parkinson Disease, Idiopathic; Freezing of Gait; Validation; Wearable Sensors; Artifical Intelligence

Registry records for this study

Records are joined using registration identifiers. Titles alone do not establish that two studies are the same.

Study type
Observational
Interventions
Not reported
Phases
Not reported
Sponsor
KU Leuven
Start date reported by registry
2025-09-22 (actual)

Registry updated: 2026-05-12 · Status last verified by the registry submitter: 2026-05

Registry records retrieved 2026-10-11 (UTC). Individual records may have older updates. Recruitment and eligibility must be confirmed with the study team.

Contact the research team

Public study contacts supplied to the registry. Ask whether recruitment is still open and what participation involves.

No central contact is listed. Check the location contacts or the official registry record.

Study locations

Site status can differ from overall study status. “Status not reported” means local availability needs confirmation. Remote participation and travel arrangements must be checked with the team.

Department of Rehabilitation Sciences

Leuven, Belgium

Recruiting

Moran Gilat · +3216 32 94 27 · moran.gilat@kuleuven.be

Margot Genbrugge · +3216 19 44 94 · margot.genbrugge@kuleuven.be

Moran Gilat

Sports Science and Neurorehabilitation

Hamburg, Germany

Not yet recruiting

Christian Schlenstedt · +4940.361 226 43206 · christian.schlenstedt@medicalschool-hamburg.de

Center for the study of movement, cognition and mobility

Tel Aviv, Israel

Not yet recruiting

Jeffrey M Hausdorff · +972-3-6973081 · jhausdor@tlvmx.gov.il

Facility not reported

Belgium

Status not reported

Facility not reported

Germany

Status not reported

Facility not reported

Israel

Status not reported

The original descriptions and participation rules come from the registry. Participation is voluntary and does not guarantee benefit.