Artifical Intelligence, Freezing of Gait, Parkinson Disease, Idiopathic, Validation, Wearable Sensors
Conditions
Brief summary
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.
Interventions
None listed
Sponsors
KU Leuven
Michael J. Fox Foundation for Parkinson's Research
Tel Aviv Medical Center
Medical School Hamburg
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
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)
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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). | 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
| Measure | Time frame | Description |
|---|---|---|
| F1-score | 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. |
| Number of FOG episodes | 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. |
| The performance of the AID-FOG algorithm to differentiate 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) | 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. |
| Comparing performance of AID-FOG to detect freezing in OFF and ON medication states. | 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. |
| Consistency of FOG detection with AID-FOG compared between two free-living assessments | 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. |
| The number of false detections 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) | The absolute sum of false detections made by the algorithm. |
| Comparing AID-FOG with subjective FOG | 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). |
| Performance of automated FOG detection during free-living mobility | 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. |
Countries
Belgium, Germany, Israel
Outcome results
None listed