Designing Human-Machine Interfaces Using Principles of Stochastic Resonance

Abstract

An experimental study is described which involves two types of haptic sensory information and its impact on the ability of the human operator to perform a target tracking task. A principal termed, "stochastic resonance was applied to design the haptic interface to enhance the operator's sense of presence. In essence, the way that stochastic resonance works, is to provide a sufficiently small amount of noise into a nonlinear system in such a way that the overall system is stimulated, but not degraded in performance. Too little noise or too much noise input is not beneficial to the operator. A small amount of noise, however, presented in judicious manner can be shown to stimulate the process but not to degrade its efficacy. In this study, haptic (force reflecting joystick) inputs were provided to the operator through his hand as well as haptic (motion chair) inputs as derived by a motion chair in the Human Sensory Feedback Laboratory. The results (across seven subjects) clearly demonstrated that an optimum amount of noise from the haptic joystick and motion chair combination could improve the ability of the operator to track signals and provide higher signal to noise ratios in his response.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 2001
Accession Number
ADA412330

Entities

People

  • A. Neidhard
  • C. A. Phillips
  • Daniel W. Repperger
  • James E. Berlin
  • Michael W. Haas

Organizations

  • Wright State University

Tags

Communities of Interest

  • Autonomy
  • Biomedical
  • Energy and Power Technologies
  • Human Systems

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Aircrafts
  • Data Analysis
  • Experimental Design
  • Haptics
  • Human Factors Engineering
  • Human-Machine Interfaces
  • Information Processing
  • Linear Systems
  • Motor Skills
  • Nonlinear Systems
  • Physics
  • Random Variables
  • Resonance
  • Stochastic Processes
  • Unmanned Aerial Vehicles

Readers

  • Control Systems Engineering.
  • Radar Systems Engineering.
  • Robotics and Automation.