Jeffrey Hausdorff to Machine Learning
This is a "connection" page, showing publications Jeffrey Hausdorff has written about Machine Learning.
Connection Strength
1.815
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Using Wearable Sensors and Machine Learning to Automatically Detect Freezing of Gait during a FOG-Provoking Test. Sensors (Basel). 2020 Aug 10; 20(16).
Score: 0.726
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A novel performance scoring quantification framework for stress test set-ups. PLoS One. 2023; 18(4):e0284083.
Score: 0.219
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Gait Detection from a Wrist-Worn Sensor Using Machine Learning Methods: A Daily Living Study in Older Adults and People with Parkinson's Disease. Sensors (Basel). 2022 Sep 19; 22(18).
Score: 0.210
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Detecting Sensitive Mobility Features for Parkinson's Disease Stages Via Machine Learning. Mov Disord. 2021 09; 36(9):2144-2155.
Score: 0.191
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Model-based and Model-free Machine Learning Techniques for Diagnostic Prediction and Classification of Clinical Outcomes in Parkinson's Disease. Sci Rep. 2018 05 08; 8(1):7129.
Score: 0.155
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Objective characterization of daily living transitions in patients with Parkinson's disease using a single body-fixed sensor. J Neurol. 2016 Aug; 263(8):1544-51.
Score: 0.135
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Can a Body-Fixed Sensor Reduce Heisenberg's Uncertainty When It Comes to the Evaluation of Mobility? Effects of Aging and Fall Risk on Transitions in Daily Living. J Gerontol A Biol Sci Med Sci. 2016 11; 71(11):1459-1465.
Score: 0.126
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Relation of gait measures with mild unilateral knee pain during walking using machine learning. Sci Rep. 2022 12 23; 12(1):22200.
Score: 0.053