NCT07580612

Brief Summary

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.

Trial Health

80
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
126

participants targeted

Target at P50-P75 for all trials

Timeline
11mo left

Started Sep 2025

Geographic Reach
3 countries

3 active sites

Status
recruiting

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

Study Progress51%
Sep 2025Jun 2027

Study Start

First participant enrolled

September 22, 2025

Completed
6 months until next milestone

First Submitted

Initial submission to the registry

April 2, 2026

Completed
1 month until next milestone

First Posted

Study publicly available on registry

May 12, 2026

Completed
1.1 years until next milestone

Primary Completion

Last participant's last visit for primary outcome

June 1, 2027

Expected
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

June 1, 2027

Last Updated

May 12, 2026

Status Verified

May 1, 2026

Enrollment Period

1.7 years

First QC Date

April 2, 2026

Last Update Submit

May 5, 2026

Conditions

ValidationWearable SensorsArtifical IntelligenceParkinson Disease, IdiopathicFreezing of Gait

Outcome Measures

Primary Outcomes (1)

  • Comparing the agreement between AID-FOG and gold-standard expert annotation to detect the percentage of time spent with freezing of gait (FOG) in relation to total time duration (%TF).

    The primary outcome (percentage of time spent with FOG in relation to total task duration = %TF) will be established by manual annotations of video footage by an experienced assessor (=gold-standard reference) and by the automated AID-FOG algorithm v1.0 applied post-hoc (i.e. offline) to IMU data collected during the same walking tasks. This will be calculated for standardized walking tasks on which the AID-FOG algorithm has been trained, standardized walking tasks on which the AID-FOG algorithm was not trained, and a free-living walking condition on which the AID-FOG algorithm was not trained.

    T0=test day 1: free-living gait assessment (5 hours), T1=test day 2: free-living gait (5 hours) and T2= test day 3: standardized gait (4 hours)

Secondary Outcomes (8)

  • F1-score

    T0=test day 1: free-living gait assessment (5 hours), T1=test day 2: free-living gait (5 hours) and T2= test day 3: standardized gait (4 hours)

  • Number of FOG episodes

    T0: free-living gait (5 hours), T1: free-living gait (5 hours) and T2: standardized gait (4 hours)

  • The performance of the AID-FOG algorithm to differentiate between the FOG manifestations.

    T0=test day 1: free-living gait assessment (5 hours), T1=test day 2: free-living gait (5 hours) and T2= test day 3: standardized gait (4 hours)

  • Comparing performance of AID-FOG to detect freezing in OFF and ON medication states.

    T0=test day 1: free-living gait assessment (5 hours), T1=test day 2: free-living gait (5 hours) and T2= test day 3: standardized gait (4 hours)

  • Consistency of FOG detection with AID-FOG compared between two free-living assessments

    T0= test day 1: free-living gait (5 hours) and T1= test day 2: free-living gait (5 hours)

  • +3 more secondary outcomes

Other Outcomes (16)

  • AID-FOG version 2.0 (percentage TF)

    T0=test day 1: free-living gait assessment (5 hours), T1=test day 2: free-living gait (5 hours) and T2= test day 3: standardized gait (4 hours)

  • AID-FOG version 2.0 (F1-score)

    T0=test day 1: free-living gait assessment (5 hours), T1=test day 2: free-living gait (5 hours) and T2= test day 3: standardized gait (4 hours)

  • AID-FOG version 2.0 (Number of FOG episodes)

    T0=test day 1: free-living gait assessment (5 hours), T1=test day 2: free-living gait (5 hours) and T2= test day 3: standardized gait (4 hours)

  • +13 more other outcomes

Study Arms (3)

Freezers

Patients with Parkinson's disease who self-report to experience Freezing of Gait daily.

Non-freezers

Patients with Parkinson's disease who do not experience Freezing of Gait.

Healthy controls

Healthy older adults

Eligibility Criteria

Age18 Years+
Sexall
Healthy VolunteersYes
Age GroupsAdult (18-64), Older Adult (65+)
Sampling MethodNon-Probability Sample
Study Population

People with PD and healthy age-matched controls will be recruited from three primary sites, namely KU Leuven, Hamburg Medical Center, and Tel-Aviv Sourasky Medical Center.

You may qualify if:

  • 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.

You may not qualify if:

  • 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)

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (3)

Department of Rehabilitation Sciences

Leuven, 3001, Belgium

RECRUITING

Sports Science and Neurorehabilitation

Hamburg, 20457, Germany

NOT YET RECRUITING

Center for the study of movement, cognition and mobility

Tel Aviv, 64, Israel

NOT YET RECRUITING

Related Publications (2)

  • Yang PK, Filtjens B, Ginis P, Goris M, Nieuwboer A, Gilat M, Slaets P, Vanrumste B. Freezing of gait assessment with inertial measurement units and deep learning: effect of tasks, medication states, and stops. J Neuroeng Rehabil. 2024 Feb 13;21(1):24. doi: 10.1186/s12984-024-01320-1.

    PMID: 38350964BACKGROUND

  • Yang PK, Filtjens B, Ginis P, Goris M, Nieuwboer A, Gilat M, Slaets P, Vanrumste B. Automatic Detection and Assessment of Freezing of Gait Manifestations. IEEE Trans Neural Syst Rehabil Eng. 2024;32:2699-2708. doi: 10.1109/TNSRE.2024.3431208. Epub 2024 Jul 31.

    PMID: 39028610BACKGROUND

Related Links

MeSH Terms

Conditions

Parkinson Disease

Condition Hierarchy (Ancestors)

Parkinsonian DisordersBasal Ganglia DiseasesBrain DiseasesCentral Nervous System DiseasesNervous System DiseasesMovement DisordersSynucleinopathiesNeurodegenerative Diseases

Study Design

Study Type
observational
Observational Model
COHORT
Time Perspective
PROSPECTIVE
Sponsor Type
OTHER
Responsible Party
PRINCIPAL INVESTIGATOR
PI Title
Prof

Study Record Dates

First Submitted

April 2, 2026

First Posted

May 12, 2026

Study Start

September 22, 2025

Primary Completion (Estimated)

June 1, 2027

Study Completion (Estimated)

June 1, 2027

Last Updated

May 12, 2026

Record last verified: 2026-05

Locations

IL(1)BE(1)DE(1)