NSF- RAPID: SCH: Smart Wearable IOT COVID19 BioTracker Necklace: Remote Assessment and Monitoring of Symptoms for Early Diagnosis, Continual Monitoring, and Prediction of Health Anomalies – Medical Robotics and Interactive Intelligent Technologies (MERIIT@NYU) Laboratory

NSF-RAPID: COVID RESEARCH

We are honored to announce that our active COVID19 research at MERIIT lab is now supported by a National Science Foundation (NSF) Rapid research grant. The project focuses on “Smart Wearable IoT COVID19 BioTracker Necklace: Remote Assessment, Early Diagnosis, Continual Monitoring, and Prediction of Health Anomalies”.

Award: Link

Initial Amendment Date:

June 10, 2020

Author: Farokh Atashzar Atashzar

Bio: Atashzar is an Assistant Professor of Electrical and Computer Engineering, as well as the Mechanical and Aerospace Engineering at New York University (NYU). He received his Ph.D. in 2016 and has recently joined NYU. His research interests include areas of applied computer science, robotics, neuro-rehabilitation robots, neuroscience, smart prostheses, and human-robot augmentation. His work has been featured in about 30 journal papers, 30 peer-reviewed conference papers, and 2 book chapters. Postdoctoral Journey: Prior to joining NYU, Atashzar was a senior postdoctoral scientist in the Department of Bioengineering, Imperial College, University of London, UK, sponsored by Natural Sciences and Engineering Research Council (NSERC) of Canada. From February 2017 to August 2018, he served as a postdoctoral research associate at Canadian Surgical Technologies and Advanced Robotics (CSTAR) center. Awards: His many awards include the highly-competitive Ontario Graduate Scholarship (OGS) in 2013 and an NSERC Post-Doctoral Fellowship (PDF) in 2018. He was ranked among the top 5 applicants in Canada for the 2018 NSERC PDF competition in the Electrical and Computer Engineering sector. View all posts by Farokh Atashzar Atashzar

Highlight: Fighting hand tremors: First comes AI, then robots

Link:

To develop advanced rehabilitation and assistive robotic technologies for patients wit pathological hand tremor (PHT), accurate estimation and prediction of nonstationary PHT is critical. However, the required level of accuracy and latency has not yet been achieved. The lack of sizable datasets and generalizable modeling techniques that can fully represent the spectrotemporal characteristics of PHT have been a critical bottleneck in attaining this goal. This work addresses this unmet need through establishing a deep recurrent neural network model to predict and eliminate the PHT component of hand motion. More specifically, we propose a machine learning-based, assumption-free, and real-time PHT elimination framework, the PHTNet, by incorporating deep bidirectional recurrent neural networks. The PHTNet is the first intelligent systems model developed on this scale for PHT elimination that maximizes the resolution of estimation and allows for prediction of future and upcoming sub-movements.

NSF-RAPID: COVID RESEARCH

Author: Farokh Atashzar Atashzar

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