Using Wearable Sensors and Machine Learning to Automatically Detect Freezing of Gait during a FOG-Provoking Test

Autor: Nir Giladi, Natalia Gouskova, Jeffrey M. Hausdorff, Eran Gazit, Talia Herman, Brad Manor, Moria Dagan, Tal Reches
Jazyk: angličtina
Rok vydání: 2020
Předmět:
medicine.medical_specialty
gyroscope
genetic structures
Computer science
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Wearable computer
Accelerometer
lcsh:Chemical technology
01 natural sciences
Biochemistry
Article
Analytical Chemistry
freezing of gait
Wearable Electronic Devices
03 medical and health sciences
0302 clinical medicine
Physical medicine and rehabilitation
Gait (human)
medicine
Humans
lcsh:TP1-1185
Electrical and Electronic Engineering
Instrumentation
Gait Disorders
Neurologic
Wearable technology
Aged
ComputingMethodologies_COMPUTERGRAPHICS
business.industry
010401 analytical chemistry
Parkinson Disease
Gait
Atomic and Molecular Physics
and Optics
0104 chemical sciences
Test (assessment)
accelerometer
medicine.anatomical_structure
wearables
machine learning
Parkinson’s disease
Ankle
Gait Analysis
business
030217 neurology & neurosurgery
Zdroj: Sensors, Vol 20, Iss 4474, p 4474 (2020)
Sensors
Volume 20
Issue 16
Sensors (Basel, Switzerland)
ISSN: 1424-8220
Popis: Freezing of gait (FOG) is a debilitating motor phenomenon that is common among individuals with advanced Parkinson's disease. Objective and sensitive measures are needed to better quantify FOG. The present work addresses this need by leveraging wearable devices and machine-learning methods to develop and evaluate automated detection of FOG and quantification of its severity. Seventy-one subjects with FOG completed a FOG-provoking test while wearing three wearable sensors (lower back and each ankle). Subjects were videotaped before (OFF state) and after (ON state) they took their antiparkinsonian medications. Annotations of the videos provided the &ldquo
ground-truth&rdquo
for FOG detection. A leave-one-patient-out validation process with a training set of 57 subjects resulted in 84.1% sensitivity, 83.4% specificity, and 85.0% accuracy for FOG detection. Similar results were seen in an independent test set (data from 14 other subjects). Two derived outcomes, percent time frozen and number of FOG episodes, were associated with self-report of FOG. Bother derived-metrics were higher in the OFF state than in the ON state and in the most challenging level of the FOG-provoking test, compared to the least challenging level. These results suggest that this automated machine-learning approach can objectively assess FOG and that its outcomes are responsive to therapeutic interventions.
Databáze: OpenAIRE
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