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

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.

Study Overview

• Status: Recruiting
• Conditions: Freezing of Gait; Parkinson Disease, Idiopathic; Validation; Artifical Intelligence; Wearable Sensors

• Study Type: Observational
• Enrollment (Estimated): 126

Contacts and Locations

Study Locations

• Belgium
  • Leuven, Belgium, 3001
    • Recruiting
    • Department of Rehabilitation Sciences
    • Contact: Moran Gilat; Phone Number: +3216 32 94 27; Email: moran.gilat@kuleuven.be
    • Contact: Margot Genbrugge; Phone Number: +3216 19 44 94; Email: margot.genbrugge@kuleuven.be
    • Principal Investigator: Moran Gilat
• Germany
  • Hamburg, Germany, 20457
    • Not yet recruiting
    • Sports Science and Neurorehabilitation
    • Contact: Christian Schlenstedt; Phone Number: +4940.361 226 43206; Email: christian.schlenstedt@medicalschool-hamburg.de
• Israel
  • Tel Aviv, Israel, 64
    • Not yet recruiting
    • Center for the study of movement, cognition and mobility
    • Contact: Jeffrey M Hausdorff; Phone Number: +972-3-6973081; Email: jhausdor@tlvmx.gov.il

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Non-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.

Description

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)

Study Plan

How is the study designed?

Number of groups / cohorts

3

Cohorts and Interventions

Group / Cohort
Healthy controls; Healthy older adults
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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
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.	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.	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.	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.	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.	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.	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).	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.	1 week of free-living mobility with IMU

Other Outcome Measures

Outcome Measure	Measure Description	Time Frame
AID-FOG version 2.0 (percentage TF)	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.	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)	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.	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)	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.	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 (FOG manifestations)	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.	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 (OFF/ON medication)	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.	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 (consistency of detection during two free-living assessments)	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.	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 false positives)	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	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 (subjective FOG)	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).	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 online (percentage TF)	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.	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 online (F1-score)	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.	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 online (number of FOG episodes)	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.	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 online (FOG manifestations)	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.	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 online (OFF/ON medication)	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.	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 online (agreement of the detection between two free-living test days)	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.	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 online (number false positives)	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.	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 online (subjective FOG)	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).	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)

Collaborators and Investigators

• Sponsor: KU Leuven
• Collaborators: Michael J. Fox Foundation for Parkinson's Research; Tel Aviv Medical Center; Medical School Hamburg

Publications and helpful links

General 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.
• 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.

Helpful Links

• The website of our research group.

Study record dates

Study Major Dates

• Study Start (Actual): September 22, 2025
• Primary Completion (Estimated): June 1, 2027
• Study Completion (Estimated): June 1, 2027

Study Registration Dates

• First Submitted: April 2, 2026
• First Submitted That Met QC Criteria: May 5, 2026
• First Posted (Actual): May 12, 2026

Study Record Updates

• Last Update Posted (Actual): May 12, 2026
• Last Update Submitted That Met QC Criteria: May 5, 2026
• Last Verified: May 1, 2026

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Synucleinopathies; Brain Diseases; Central Nervous System Diseases; Nervous System Diseases; Neurodegenerative Diseases; Movement Disorders; Parkinsonian Disorders; Basal Ganglia Diseases; Parkinson Disease
• Other Study ID Numbers: S70220; MJFF-024628 (Other Grant/Funding Number: Micheal J. Fox)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED
• IPD Plan Description: 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.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No