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NCT Number: NCT07580612

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

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.

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Key information

Conditions

Parkinson Disease, Idiopathic Artifical Intelligence Basal Ganglia Diseases Brain Diseases Central Nervous System Diseases Freezing of Gait Movement Disorders Nervous System Diseases Neurodegenerative Diseases Parkinson Disease Parkinsonian Disorders Synucleinopathies Validation Wearable Sensors

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Department of Rehabilitation Sciences, Leuven, Belgium

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Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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)

Treatment and study plan

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).

    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.

Secondary outcomes

  1. 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)

    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.

  2. Number of FOG episodes

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

    Same as primary outcome, but now for the absolute number of FOG episodes.

  3. 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)

    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.

  4. 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)

    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.

  5. 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 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.

  6. 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)

    The absolute sum of false detections made by the algorithm.

  7. 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)

    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).

  8. Performance of automated FOG detection during free-living mobility

    Time frame: 1 week of free-living mobility with IMU

    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.

Other outcomes

  1. AID-FOG version 2.0 (percentage 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 data obtained in the study will be used to train the AID-FOG algorithm v1.0. This trained AID-FOG algorithm v2.0 will be evaluated using the same listed outcome measures, using a leave-one-subject-out approach.

    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 v2.0 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.

  2. AID-FOG version 2.0 (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)

    The data obtained in the study will be used to train the AID-FOG algorithm v1.0. This trained AID-FOG algorithm v2.0 will be evaluated using the same listed outcome measures, using a leave-one-subject-out approach. Same as percentage TF, but now for the F1-score (rather than percent TF). F1 scores range between 0 and 1, the higher the score the better.

  3. AID-FOG version 2.0 (Number of FOG episodes)

    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 data obtained in the study will be used to train the AID-FOG algorithm v1.0. This trained AID-FOG algorithm v2.0 will be evaluated using the same listed outcome measures, using a leave-one-subject-out approach. Same as percentage TF, but now for the absolute number of FOG episodes.

  4. AID-FOG version 2.0 (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)

    The data obtained in the study will be used to train the AID-FOG algorithm v1.0. This trained AID-FOG algorithm v2.0 will be evaluated using the same listed outcome measures, using a leave-one-subject-out approach. The performance of the AID-FOG algorithm v2.0 to discriminate 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.

  5. AID-FOG version 2.0 (OFF/ON medication)

    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 data obtained in the study will be used to train the AID-FOG algorithm v1.0. This trained AID-FOG algorithm v2.0 will be evaluated using the same listed outcome measures, using a leave-one-subject-out approach. Comparing performance of AID-FOG v2.0 to detect freezing in OFF and ON medication states. The FOG outcomes as obtained by the human expert and the offline AID-FOG algorithm v2.0 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.

  6. AID-FOG version 2.0 (consistency of detection during two free-living assessments)

    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 data obtained in the study will be used to train the AID-FOG algorithm v1.0. This trained AID-FOG algorithm v2.0 will be evaluated using the same listed outcome measures, using a leave-one-subject-out approach. Consistency of FOG detection with AID-FOG v2.0 compared between two free-living assessments. The agreement in FOG detection (%TF, F1-score) between the AID-FOG algorithm v2.0 and the gold-standard human annotations will be compared between the two free-living test days.

  7. AID-FOG version 2.0 (number of false positives)

    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 data obtained in the study will be used to train the AID-FOG algorithm v1.0. This trained AID-FOG algorithm v2.0 will be evaluated using the same listed outcome measures, using a leave-one-subject-out approach. The number of false detections (AID-FOG v2.0) of FOG episodes during free-living

  8. AID-FOG version 2.0 (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)

    The data obtained in the study will be used to train the AID-FOG algorithm v1.0. This trained AID-FOG algorithm v2.0 will be evaluated using the same listed outcome measures, using a leave-one-subject-out approach. Comparing AID-FOG v2.0 with subjective FOG. The FOG outcomes obtained with AID-FOG v2.0 will be correlated to the total score of the New Freezing of Gait Questionnaire (NFOGQ) and Patient Reported Outcomes of FOG (PRO).

  9. AID-FOG online (percentage 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 AID-FOG algorithm will be modified for real-time FOG detection. Performance of this AID-FOG online algorithm will be compared with AID-FOG offline versions, using the same listed outcomes. 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 online 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.

  10. AID-FOG online (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)

    The AID-FOG algorithm will be modified for real-time FOG detection. Performance of this AID-FOG online algorithm will be compared with AID-FOG offline versions, using the same listed outcomes. Same as percentage TF, but now for the F1-score (rather than percent TF). F1 scores range between 0 and 1, the higher the score the better.

  11. AID-FOG online (number of FOG episodes)

    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 AID-FOG algorithm will be modified for real-time FOG detection. Performance of this AID-FOG online algorithm will be compared with AID-FOG offline versions, using the same listed outcomes. Same as percentage TF, but now for the absolute number of FOG episodes.

  12. AID-FOG online (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)

    The AID-FOG algorithm will be modified for real-time FOG detection. Performance of this AID-FOG online algorithm will be compared with AID-FOG offline versions, using the same listed outcomes. The performance of the AID-FOG online algorithm to discriminate between the FOG manifestations.

  13. AID-FOG online (OFF/ON medication)

    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 AID-FOG algorithm will be modified for real-time FOG detection. Performance of this AID-FOG online algorithm will be compared with AID-FOG offline versions, using the same listed outcomes. The FOG outcomes as obtained by the human expert and the offline AID-FOG online algorithm 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.

  14. AID-FOG online (agreement of the detection between two free-living test days)

    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 AID-FOG algorithm will be modified for real-time FOG detection. Performance of this AID-FOG online algorithm will be compared with AID-FOG offline versions, using the same listed outcomes. The agreement in FOG detection (%TF, F1-score) between the AID-FOG online algorithm and the gold-standard human annotations will be compared between the two free-living test days.

  15. AID-FOG online (number false positives)

    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 AID-FOG algorithm will be modified for real-time FOG detection. Performance of this AID-FOG online algorithm will be compared with AID-FOG offline versions, using the same listed outcomes. The absolute sum of false detections made by the online algorithm.

  16. AID-FOG online (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)

    The AID-FOG algorithm will be modified for real-time FOG detection. Performance of this AID-FOG online algorithm will be compared with AID-FOG offline versions, using the same listed outcomes. Comparing AID-FOG online with subjective FOG. The FOG outcomes obtained with AID-FOG online will be correlated to the total score of the New Freezing of Gait Questionnaire (NFOGQ) and Patient Reported Outcomes of FOG (PRO).

Sponsors and collaborators

Lead sponsor

KU Leuven

Other

Collaborators

  • Medical School Hamburg
  • Michael J. Fox Foundation for Parkinson's Research
  • Tel Aviv Medical Center

Registry information

Official study title

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

Acronym: AID-FOG

Important dates

Study start
2025
Primary completion
2027
Study completion
2027
First posted
May 12, 2026
Registry last updated
May 12, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

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This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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