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Open AccessArticle

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

1
Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv-Yafo 6492416, Israel
2
Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
3
Harvard Medical School, Boston, MA 02115, USA
4
Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Roslindale, MA 02131, USA
5
Division of Gerontology, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA
6
Department of Neurology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
7
Department of Physical Therapy, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
8
Rush Alzheimer’s Disease Center and Department of Orthopedic Surgery, Rush University Medical Center, Chicago, IL 60612, USA
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(16), 4474; https://doi.org/10.3390/s20164474
Received: 26 July 2020 / Revised: 6 August 2020 / Accepted: 8 August 2020 / Published: 10 August 2020
(This article belongs to the Special Issue Wearable Sensors for Movement Analysis)
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 “ground-truth” 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. View Full-Text
Keywords: Parkinson’s disease; wearables; machine learning; freezing of gait; accelerometer; gyroscope Parkinson’s disease; wearables; machine learning; freezing of gait; accelerometer; gyroscope
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MDPI and ACS Style

Reches, T.; Dagan, M.; Herman, T.; Gazit, E.; Gouskova, N.A.; Giladi, N.; Manor, B.; Hausdorff, J.M. Using Wearable Sensors and Machine Learning to Automatically Detect Freezing of Gait during a FOG-Provoking Test. Sensors 2020, 20, 4474. https://doi.org/10.3390/s20164474

AMA Style

Reches T, Dagan M, Herman T, Gazit E, Gouskova NA, Giladi N, Manor B, Hausdorff JM. Using Wearable Sensors and Machine Learning to Automatically Detect Freezing of Gait during a FOG-Provoking Test. Sensors. 2020; 20(16):4474. https://doi.org/10.3390/s20164474

Chicago/Turabian Style

Reches, Tal; Dagan, Moria; Herman, Talia; Gazit, Eran; Gouskova, Natalia A.; Giladi, Nir; Manor, Brad; Hausdorff, Jeffrey M. 2020. "Using Wearable Sensors and Machine Learning to Automatically Detect Freezing of Gait during a FOG-Provoking Test" Sensors 20, no. 16: 4474. https://doi.org/10.3390/s20164474

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