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

Официальное название

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

Обзор

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.

Первичные конечные точки

• 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). [Срок оценки: 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)]

Вторичные конечные точки (8)

• F1-score [Срок оценки: 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 [Срок оценки: 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. [Срок оценки: 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. [Срок оценки: 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 [Срок оценки: 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 [Срок оценки: 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 [Срок оценки: 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 [Срок оценки: 1 week of free-living mobility with IMU]

Критерии участия

Критерии включения

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.

Критерии исключения

• 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)

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Да

Дизайн исследования

Модель наблюдения

Когортное

Центры проведения

Бельгия · 1 центр

• Department of Rehabilitation Sciences — Leuven

Германия · 1 центр

• Sports Science and Neurorehabilitation — Hamburg

Израиль · 1 центр

• Center for the study of movement, cognition and mobility — Tel Aviv

Публикации

• 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

Идентификаторы

NCT: NCT07580612 · S70220 · MJFF-024628

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

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