CHS: Medium: Collaborative Research: Electromyography (EMG)-Based Assistive Human-Machine Interface Design: Cognitive Workload and Motor Skill Learning Assessment

Posted March 9, 2020 in Humans and Technology

Principal Investigator: David Kaber

Co-PI: Jaime Ruiz

Sponsor: NSF

Start Date: October 1, 2020

End Date: September 30, 2022

Amount: $480,000

Abstract

This project seeks to develop models of powered-prosthetic user cognitive workload under different modes of EMG-based control. The models are to be used for predicting workload associated with use of EMG-based control interfaces in other applications, including virtual reality-based training of motor skills and object handling as well as complex motor tasks, such as driving and vehicle control. Broader impacts: The work is expected to result in a set design guidelines for EMG-based control interface design for various applications.

More Information: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1900044&HistoricalAwards=false