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

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4474 ◽  
Author(s):  
Tal Reches ◽  
Moria Dagan ◽  
Talia Herman ◽  
Eran Gazit ◽  
Natalia A. Gouskova ◽  
...  
Keyword(s):  
Machine Learning ◽  
Wearable Sensors ◽  
Freezing Of Gait ◽  
Ground Truth ◽  
Self Report ◽  
Therapeutic Interventions ◽  
Percent Time ◽  
Machine Learning Approach ◽  
Automated Machine Learning ◽  
Fog Detection

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.

Accelerating Surgical Site Infection Abstraction With a Semi-automated Machine-learning Approach

2020 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Steven J. Skube ◽  
Zhen Hu ◽  
Gyorgy J. Simon ◽  
Elizabeth C. Wick ◽  
Elliot G. Arsoniadis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Surgical Site Infection ◽  
Learning Approach ◽  
Site Infection ◽  
Machine Learning Approach ◽  
Automated Machine Learning

scholarly journals A machine learning approach for gait speed estimation using skin-mounted wearable sensors: From healthy controls to individuals with multiple sclerosis

PLoS ONE ◽  
2017 ◽  
Vol 12 (6) ◽  
pp. e0178366 ◽  
Author(s):  
Ryan S. McGinnis ◽  
Nikhil Mahadevan ◽  
Yaejin Moon ◽  
Kirsten Seagers ◽  
Nirav Sheth ◽  
...  
Keyword(s):  
Machine Learning ◽  
Multiple Sclerosis ◽  
Gait Speed ◽  
Wearable Sensors ◽  
Learning Approach ◽  
Speed Estimation ◽  
Healthy Controls ◽  
Machine Learning Approach

scholarly journals Automatic subtyping of individuals with Primary Progressive Aphasia

2020 ◽  
Author(s):  
Charalambos Themistocleous ◽  
Bronte Ficek ◽  
Kimberly Webster ◽  
Dirk-Bart den Ouden ◽  
Argye E. Hillis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Classification Accuracy ◽  
Primary Progressive Aphasia ◽  
Support Vector ◽  
Progressive Aphasia ◽  
Primary Progressive ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Automated Machine Learning

AbstractBackgroundThe classification of patients with Primary Progressive Aphasia (PPA) into variants is time-consuming, costly, and requires combined expertise by clinical neurologists, neuropsychologists, speech pathologists, and radiologists.ObjectiveThe aim of the present study is to determine whether acoustic and linguistic variables provide accurate classification of PPA patients into one of three variants: nonfluent PPA, semantic PPA, and logopenic PPA.MethodsIn this paper, we present a machine learning model based on Deep Neural Networks (DNN) for the subtyping of patients with PPA into three main variants, using combined acoustic and linguistic information elicited automatically via acoustic and linguistic analysis. The performance of the DNN was compared to the classification accuracy of Random Forests, Support Vector Machines, and Decision Trees, as well as expert clinicians’ classifications.ResultsThe DNN model outperformed the other machine learning models with 80% classification accuracy, providing reliable subtyping of patients with PPA into variants and it even outperformed auditory classification of patients into variants by clinicians.ConclusionsWe show that the combined speech and language markers from connected speech productions provide information about symptoms and variant subtyping in PPA. The end-to-end automated machine learning approach we present can enable clinicians and researchers to provide an easy, quick and inexpensive classification of patients with PPA.

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