Parkinson Disease, IdiopathicAID-FOGNCT07580612
AID-FOG: Artificial Intelligence-Driven Freezing of Gait Detection in the Home
An observational study of Parkinson Disease, Idiopathic and Freezing of Gait, run by KU Leuven at 3 sites in 3 countries.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Parkinson Disease, Idiopathic, Freezing of Gait, Validation
- How many people
- 126 participants
- Where
- 3 sitesin 3 countries
- Study dates
- 22 Sep 2025to Jun 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
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.
Who may be able to join
- 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.
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- 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.
Who cannot join
- 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)
Trial locations and contacts
3 sites in 3 countries. Contact the study team or a site near you to ask about taking part.
3 sites in 3 countries
Belgium1 site, 1 recruiting
- Recruiting
Department of Rehabilitation Sciences
Leuven, 3001
Moran Gilat, +3216 32 94 27, moran.gilat@kuleuven.be
Margot Genbrugge, +3216 19 44 94, margot.genbrugge@kuleuven.be
Germany1 site, 1 recruiting
- Not yet recruiting
Sports Science and Neurorehabilitation
Hamburg, 20457
Christian Schlenstedt, +4940.361 226 43206, christian.schlenstedt@medicalschool-hamburg.de
Israel1 site, 1 recruiting
- Not yet recruiting
Center for the study of movement, cognition and mobility
Tel Aviv, 64
Jeffrey M Hausdorff, +972-3-6973081, jhausdor@tlvmx.gov.il
About this study
Official title: Artificial Intelligence-Driven Freezing Of Gait Detection in the Home: Investigating How Free-living Activities Affect the Algorithm
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
- Parkinson Disease, Idiopathic, Freezing of Gait, Validation, Wearable Sensors, Artifical Intelligence
- Medical subject headings
- Parkinson Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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 |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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
Same as primary outcome, but now for the F1-score (rather than percent TF). F1 scores range between 0 and 1, the higher the score the better.
Measured: 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
Same as primary outcome, but now for the absolute number of FOG episodes.
Measured: 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.
Freezing of gait (FOG) manifests in multiple forms, including akinetic and kinetic subtypes, which may be associated with trembling or occur without it. This study investigates the performance of AID-FOG in discriminating between these manifestations, using expert annotations as the reference standard.
Measured: 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.
The FOG outcomes as obtained by the human expert and the offline AID-FOG algorithm v1.0 will be calculated for both the OFF and ON medication states. These scores will be compared to evaluate the change in algorithm performance depending on medication status. The FOG outcomes will be the percentage TF which ranges between 0-100 percent. The higher the percentage the more freezing the patient has. But also the F1-score which ranges between 0-1. The higher the score the more overlap there is between the expert and the algorithm.
Measured: 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
The agreement in FOG detection (%TF, F1-score) between the AID-FOG algorithm and the gold-standard human annotations will be compared between the two free-living test days.
Measured: T0= test day 1: free-living gait (5 hours) and T1= test day 2: free-living gait (5 hours)
The number of false detections of FOG episodes during free-living
The absolute sum of false detections made by the algorithm.
Measured: 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 AID-FOG with subjective FOG
The FOG outcomes obtained with AID-FOG will be correlated to the total score of the New Freezing of Gait Questionnaire (NFOGQ) and Patient Reported Outcomes of FOG (PRO).
Measured: 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)
Performance of automated FOG detection during free-living mobility
The FOG outcomes obtained with AID-FOG offline will be calculated from multiple days of free-living mobility IMU data. These outcomes will be correlated to FOG severity as determined during the observed walking tasks of the project and self-reported FOG severity.
Measured: 1 week of free-living mobility with IMU
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 May 2026
- Study start22 Sep 2025
- Primary completionJun 2027 (estimated)
- Study completionJun 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (2)Papers the sponsor has linked to this study.
- Other trials for Parkinson Disease, IdiopathicStudies of the same condition, open and under way.
- More trials run by KU LeuvenThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 126 participants (estimated)
Sponsor and investigators
- Lead sponsor
- KU Leuven
- Sponsor type
- University, hospital or other
- Collaborators
- Michael J. Fox Foundation for Parkinson's Research, Tel Aviv Medical Center, Medical School Hamburg
- Responsible party
- Principal investigator, Moran Gilat, KU Leuven
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07580612
- Sponsor's protocol ID
- S70220
- Other IDs
- MJFF-024628
- Acronym
- AID-FOG
- Status last verified
- May 2026
- First submitted
- 2 Apr 2026
- Last updated
- 12 May 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided: The datasets could be made available under restricted access after publication of the results, following ethical approval and a data-transfer agreement. GDPR and privacy regulations will be adhered to.
Publications
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.
PubMed 38350964Yang 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.
PubMed 39028610
Study at a glance
From the registry record, last updated by the sponsor on 12 May 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence-Driven Freezing Of Gait Detection in the Home: Investigating How Free-living Activities Affect the Algorithm
- ClinicalTrials.gov ID
- NCT07580612
- Acronym / sponsor ID
- AID-FOG / S70220
- Conditions
- Parkinson Disease, Idiopathic, Freezing of Gait, Validation, Wearable Sensors, Artifical Intelligence
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 126 participants (estimated)
- Lead sponsor
- KU LeuvenUniversity, hospital or other
- Collaborators
- Michael J. Fox Foundation for Parkinson's Research, Tel Aviv Medical Center, Medical School Hamburg
- Study start
- 22 Sep 2025 (actual)
- Primary completion
- Jun 2027 (estimated)
- Study completion
- Jun 2027 (estimated)
- First posted
- 12 May 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| 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 |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (10 listed)
- 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;
Key exclusion criteria (3 listed)
- 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)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
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).
Time frame: 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)
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.
8 secondary outcomes
F1-score
Time frame: 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
Time frame: 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.
Time frame: 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.
Time frame: 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
Time frame: T0= test day 1: free-living gait (5 hours) and T1= test day 2: free-living gait (5 hours)
The number of false detections of FOG episodes during free-living
Time frame: 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 AID-FOG with subjective FOG
Time frame: 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)
Performance of automated FOG detection during free-living mobility
Time frame: 1 week of free-living mobility with IMU
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 12 May 2026.
- Background: PubMed 38350964Yang 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.
- Background: PubMed 39028610Yang 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.
- The website of our research group.Link provided by the sponsor.
- Individual participant dataSharing planned: Undecided. The datasets could be made available under restricted access after publication of the results, following ethical approval and a data-transfer agreement. GDPR and privacy regulations will be adhered to.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Sports Science and Neurorehabilitation
Hamburg, Germany
Center for the study of movement, cognition and mobility
Tel Aviv, Israel
3 sites in 3 countries: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Parkinson Disease, Idiopathic and Freezing of Gait, run by KU Leuven at 3 sites in 3 countries.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 10 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 12 May 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 3 sites in Belgium, Germany, Israel. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Each site lists its own contact: choose the nearest one under Trial locations. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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). (time frame: 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)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07580612, which also lists 2 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 12 May 2026.