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

KU Leuven126 enrolled

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.

Study Type

OBSERVATIONAL

Enrollment

126

Conditions

Parkinson Disease, Idiopathic Freezing of Gait Validation Wearable Sensors Artifical Intelligence

Eligibility

Sex: ALLMin age: 18 YearsHealthy volunteers:

Medical Language ↔ Plain English

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)

Outcomes

Primary Outcomes

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.