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Fatemeh Noushin Golabchi
Researcher at Spaulding Rehabilitation Hospital
Publications - 7
Citations - 208
Fatemeh Noushin Golabchi is an academic researcher from Spaulding Rehabilitation Hospital. The author has contributed to research in topics: Dyskinesia & Deep learning. The author has an hindex of 5, co-authored 7 publications receiving 135 citations. Previous affiliations of Fatemeh Noushin Golabchi include University of Massachusetts Amherst.
Papers
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Proceedings ArticleDOI
Recent machine learning advancements in sensor-based mobility analysis: Deep learning for Parkinson's disease assessment
Bjoern M. Eskofier,Sunghoon Ivan Lee,Jean-Francois Daneault,Fatemeh Noushin Golabchi,Gabriela Ferreira-Carvalho,Gloria Vergara-Diaz,Stefano Sapienza,Gianluca Costante,Jochen Klucken,Thomas Kautz,Paolo Bonato +10 more
TL;DR: This paper compared standard machine learning pipelines with deep learning based on convolutional neural networks and showed that deep learning outperformed other state-of-the-art machine learning algorithms in terms of classification rate.
Posted ContentDOI
Crowdsourcing digital health measures to predict Parkinson’s disease severity: the Parkinson’s Disease Digital Biomarker DREAM Challenge
Solveig K. Sieberts,Jennifer Schaff,Marlena Duda,Bálint Ármin Pataki,Ming Sun,Phil Snyder,Jean-Francois Daneault,Jean-Francois Daneault,Federico Parisi,Federico Parisi,Gianluca Costante,Gianluca Costante,Udi Rubin,Peter Banda,Yooree Chae,Elias Chaibub Neto,Ray Dorsey,Zafer Aydin,Aipeng Chen,Laura L. Elo,Carlos Espino,Enrico Glaab,Ethan Goan,Fatemeh Noushin Golabchi,Yasin Gormez,Maria K. Jaakkola,Maria K. Jaakkola,Jitendra Jonnagaddala,Riku Klén,Dongmei Li,Christian McDaniel,Dimitri Perrin,Nastaran Mohammadian Rad,Nastaran Mohammadian Rad,Nastaran Mohammadian Rad,Erin Rainaldi,Stefano Sapienza,Patrick Schwab,Nikolai Shokhirev,Mikko S. Venäläinen,Gloria Vergara-Diaz,Yuqian Zhang,Yuanjia Wang,Yuanfang Guan,Daniela Brunner,Paolo Bonato,Paolo Bonato,Lara M. Mangravite,Larsson Omberg +48 more
TL;DR: The use of crowdsourcing to specifically evaluate and benchmark features derived from accelerometer and gyroscope data in two different datasets to predict the presence of Parkinson’s Disease (PD) and severity of three PD symptoms: tremor, dyskinesia and bradyKinesia is described.
Proceedings ArticleDOI
Estimating bradykinesia in Parkinson's disease with a minimum number of wearable sensors
Jean-Francois Daneault,Sunghoon Ivan Lee,Fatemeh Noushin Golabchi,Shyamal Patel,Ludy C. Shih,Sabrina Paganoni,Paolo Bonato +6 more
TL;DR: It is demonstrated that the use of multiple sensors on a single limb does not significantly improve the estimation of clinical scores related to bradykinesia, and a minimum of one wearable sensor per limb is required.
Journal ArticleDOI
Crowdsourcing digital health measures to predict Parkinson’s disease severity: the Parkinson’s Disease Digital Biomarker DREAM Challenge
Solveig K. Sieberts,Jennifer Schaff,Marlena Duda,Bálint Ármin Pataki,Ming Sun,Phil Snyder,Jean-Francois Daneault,Jean-Francois Daneault,Federico Parisi,Federico Parisi,Gianluca Costante,Gianluca Costante,Udi Rubin,Peter Banda,Yooree Chae,Elias Chaibub Neto,E. Ray Dorsey,Zafer Aydin,Aipeng Chen,Laura L. Elo,Carlos Espino,Enrico Glaab,Ethan Goan,Fatemeh Noushin Golabchi,Yasin Gormez,Maria K. Jaakkola,Maria K. Jaakkola,Jitendra Jonnagaddala,Riku Klén,Dongmei Li,Christian McDaniel,Dimitri Perrin,Thanneer M. Perumal,Nastaran Mohammadian Rad,Nastaran Mohammadian Rad,Nastaran Mohammadian Rad,Erin Rainaldi,Stefano Sapienza,Patrick Schwab,Nikolai Shokhirev,Mikko S. Venäläinen,Gloria Vergara-Diaz,Yuqian Zhang,Yuanjia Wang,Yuanfang Guan,Daniela Brunner,Paolo Bonato,Paolo Bonato,Lara M. Mangravite,Larsson Omberg +49 more
TL;DR: In this article, the authors describe the use of crowdsourcing to specifically evaluate and benchmark features derived from accelerometer and gyroscope data in two different datasets to predict the presence of Parkinson's disease and severity of three PD symptoms: tremor, dyskinesia, and bradykineia.
Proceedings ArticleDOI
A novel method for assessing the severity of levodopa-induced dyskinesia using wearable sensors
Sunghoon Ivan Lee,Jean-Francois Daneault,Fatemeh Noushin Golabchi,Shyamal Patel,Sabrina Paganoni,Ludy C. Shih,Paolo Bonato +6 more
TL;DR: A novel method suitable to automatically select data segments from the training dataset that are marked by dyskinetic movements is proposed, which aggregates results derived from the testing dataset using a machine learning algorithm to estimate the severity of dyskinesia from wearable sensor data.