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 |
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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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