New Grant: NSF/FDA SIR: Objective Assessment of Recovery during Post Stroke NeuroRehabilitation Therapy using Brain-Muscle Connectivity Network

NSF/FDA SIR: 

We are honored to announce that our active research in development on neurophysiological measures of stroke recovery at MERIIT lab is now supported by a National Science Foundation (NSF) to expand our collaboration with US Food and Durg Adminstration (FDA). 

Award: Link

Initial Amendment Date: December 10, 2020

 

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

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

 

Special Issue: Robotics, Autonomous Systems and AI for Nonurgent/Nonemergent Healthcare Delivery During and After the COVID-19 Pandemic

As part of our active response to the COVID-19 pandemic, we are pleased to announce our “Frontiers on Robotics & AI” Special Issue on “Robotics, Autonomous Systems and AI for Nonurgent/Nonemergent Healthcare Delivery During and After the COVID-19 Pandemic”. Papers can be in the format of Original Research, Mini-review, Perspective, Opinion piece, Case Report. Please see CfP for more details. This is a joint effort between editors from New York University, University of Alberta, University of Western Ontario, and Intuitive Surgical.

Call For Paper

 

Parkinson’s disease affects the fabrics of time perception

NYU NewsNature Scientific Reports

Abstract

Non-motor symptoms in Parkinson’s Disease (PD) predate motor symptoms and substantially decrease quality of life; however, detection, monitoring, and treatments are unavailable for many of these symptoms. Temporal perception abnormalities in PD are generally attributed to altered Basal Ganglia (BG) function. Present studies are confounded by motor control facilitating movements that are integrated into protocols assessing temporal perception. There is uncertainty regarding the BG’s influence on timing processes of different time scales and how PD therapies affect this perception. In this study, PD patients using Levodopa (n = 25), Deep Brain Stimulation (DBS; n = 6), de novo patients (n = 6), and healthy controls (n = 17) completed a visual temporal perception task in seconds and sub-section timing scales using a computer-generated graphical tool. For all patient groups, there were no impairments seen at the smaller tested magnitudes (using sub-second timing). However, all PD groups displayed significant impairments at the larger tested magnitudes (using interval timing). Neither Levodopa nor DBS therapy led to significant improvements in timing abilities. Levodopa resulted in a strong trend towards impairing timing processes and caused a deterioration in perceptual coherency according to Weber’s Law. It is shown that timing abnormalities in PD occur in the seconds range but do not extend to the sub-second range. Furthermore, observed timing deficits were shown to not be solely caused by motor deficiency. This provides evidence to support internal clock models involving the BG (among other neural regions) in interval timing, and cerebellar control of sub-second timing. This study also revealed significant temporal perception deficits in recently diagnosed PD patients; thus, temporal perception abnormalities might act as an early disease marker, with the graphical tool showing potential for disease monitoring.

 

 

 

Fighting hand tremors: First comes AI, then robots

NYU News, Nature Scientific Reports

Abstract

The global aging phenomenon has increased the number of individuals with age-related neurological movement disorders including Parkinson’s Disease (PD) and Essential Tremor (ET). Pathological Hand Tremor (PHT), which is considered among the most common motor symptoms of such disorders, can severely affect patients’ independence and quality of life. To develop advanced rehabilitation and assistive technologies, accurate estimation/prediction of nonstationary PHT is critical, however, the required level of accuracy 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 paper addresses this unmet need through establishing a deep recurrent 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 developed over a hand motion dataset of 81 ET and PD patients collected systematically in a movement disorders clinic over 3 years. 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.