Project Abstract

We exist in a world with advanced AI, yet there is a lack of AI for assisting disabled populations who cannot achieve basic manipulability functions. This project will be to develop trustworthy Machine Learning models to to address existing problems of human-machine interfaces.

Project Description & Overview

This research project will try to answer whether: 1) AI models can be trained faster to exclude the need for high-performance computers; and 2) if calibration can be minimized for new users so that neuro-robots are easy to use and ubiquitous in less-developed regions.

For this, we will develop a new biosignal processing pipeline using artificial intelligence, specifically a shallow-hybrid neural network, which includes an engine for modeling long term and short term dynamical dependencies in the signal space. The model will be tested in comparison with exiting state-of-the-art algorithms that we have developed in the last years. For this, data will be used from large datasets available to us. There is a potential possibility of involvement in data collection, depending on the academic and research background.

Students are more than welcome to contact f.atashzar@nyu.edu with questions. See one of our recent efforts with applications in neurorobotics for more context.

Datasets 

We will use available large data sets on high-density electromyography and will try to predict the intended gestures. The data set includes high volume of signals collected from the upper limb of ~50 human subjects.

Competencies

Academic/research background in machine learning and/or signal/data processing is encouraged.

Learning Outcomes & Deliverables

Signal Processing, Deep Learning, Human-Machine Interface

Students

Jeff Guo, Yusen Li, Soobin Lim, Baohan Liu