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Recruiting NCT07580612

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

— Observational Parkinson Disease, Idiopathic Freezing of Gait Validation Wearable Sensors

For patients and families

In plain language

An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.

What is being studied
This is an observational study: the protocol does not assign a study treatment.
Who it may be relevant to
Registry conditions: Parkinson Disease, Idiopathic, Freezing of Gait, Validation, Wearable Sensors. Basic parameters: from 18 years · All.
What needs checking
Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
Where it takes place
Belgium, Germany, Israel
Next step
Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →

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Official title

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

Overview

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.

Primary outcome measures

  • 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)]
Secondary outcome measures (8)
  • 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]

Eligibility criteria

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)

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: Yes

Study design

Observational model
Cohort

Study locations

Belgium · 1 center
  • Department of Rehabilitation Sciences — Leuven
Germany · 1 center
  • Sports Science and Neurorehabilitation — Hamburg
Israel · 1 center
  • Center for the study of movement, cognition and mobility — Tel Aviv

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. PMID 38350964
  • 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 39028610

Identifiers

NCT: NCT07580612 · S70220 · MJFF-024628

Primary sources (government registries)

View this study on ClinicalTrials.gov ↗