Context-Aware control of smart objects via human-machine communication

Md Muztoba, Eric Qin, Nicholas Tran, Umit Ogras

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Brain-machine interface (BMI) is a promising technology that can provide accessibility to sensors and actuators using limited physical interaction. This technology can benefit millions of people with physical disabilities, such as Amyotrophic Lateral Sclerosis (ALS) and limb problems. However, its practical application depends critically on the accuracy of interpreting the commands received through BMI. This paper presents two techniques that exploit contextual awareness to improve the accuracy of communication using BMIs. We first present a technique that reduces the false interpretation probability significantly by analyzing the current system state. Then, we quantify the benefits of automating actions with the help of previously learned patterns. Experimental evaluations using a commercial BMI headset and a virtual reality environment show 2.6× decrease in the completion time of a navigation task.

Original languageEnglish (US)
Title of host publicationIEEE Biomedical Circuits and Systems Conference: Engineering for Healthy Minds and Able Bodies, BioCAS 2015 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781479972333
StatePublished - Dec 4 2015
Event11th IEEE Biomedical Circuits and Systems Conference, BioCAS 2015 - Atlanta, United States
Duration: Oct 22 2015Oct 24 2015


Other11th IEEE Biomedical Circuits and Systems Conference, BioCAS 2015
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Biotechnology
  • Instrumentation
  • Biomedical Engineering
  • Electrical and Electronic Engineering


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