Simplifying Wheelchair Mounted Robotic Arm Control with a Visual Interface

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Simplifying Wheelchair Mounted Robotic Arm
Control with a Visual Interface

• Katherine M. Tsuiand Holly A. Yanco
• University of Massachusetts, Lowell Computer
  Science Department Robotics Laboratory

http//www.cs.uml.edu/robots
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Collaborators

• University of Central Florida Aman Behal
• Crotched Mountain Rehabilitation Center David
  Kontak
• Exact Dynamics GertWilem Romer
• NSF IIS-0534364

http//www.cs.uml.edu/robots
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Research Question

• What is the most effective user interface to
  manipulate a robot arm?
• Our target audience is power wheelchair users,
  specifically
• Physically disabled, cognitively aware people.
• Cognitively impaired people who do not have fine
  motor control.

http//www.cs.uml.edu/robots
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Hardware

• Manus ARM by Exact Dynamics
• 6 DoF
• Joint encoders, slip couplings
• Cameras
• Manual and computer control modes
• Both are capable of individual joint movement and
  Cartesian movement of the wrist.

http//www.cs.uml.edu/robots
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Interface Design

• Interface is compatible with single switch
  scanning.
• Left
• Original image is quartered.
• Quadrant containing the desired object is
  selected.
• Middle
• Selection is repeated a second time.
• Right
• Desired object is in 1/16th close-up view.

http//www.cs.uml.edu/robots
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User Testing Hypotheses

• H1 Users will prefer a visual interface to a
  menu based system.
• H2 With greater levels of autonomy, less user
  input is necessary for control.
• H3 It should be faster to move to the target in
  computer control than in manual control.

http//www.cs.uml.edu/robots
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User Testing Experiment

• Participants
• 12 able-bodied participants (10 male, 2 female)
• Age 18, 52
• 67 technologically capable
• Computer usage per week (including job related)
• 67 20 hours 25 10 to 20 hours 8 3 to 10
  hours
• 1/3 had prior robot experience
• 1 industry 2 university course 1 toy robots

http//www.cs.uml.edu/robots
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User Testing Experiment Methodology

• Two tested conditions manual and computer
  control.
• Input device was single switch for both controls.
• Each user performed 6 runs (3 manual, 3
  computer).
• Start control was randomized and alternated.
• 6 targets were randomly chosen.

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User TestingExperiment Methodology

• Neither fine control nor depth existed in
  implementation of computer control during user
  testing.
• In manual control, users were instructed to move
  the opened gripper sufficiently close to the
  target.

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User TestingExperiment Methodology

• Manual control procedure, using single switch and
  single switch menu
• Unfold ARM.
• Using Cartesian movement, maneuver opened gripper
  sufficiently close to target.

http//www.cs.uml.edu/robots
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User TestingExperiment Methodology

• Computer control procedure
• Turn on ARM.
• Select image using single switch.
• Select major quadrant using single switch.
• Select minor quadrant using single switch.
• Color calibrate using single switch.

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User Testing Results

• H1 Users will prefer a visual interface to a
  menu based system.
• 83 stated preference for manual control in exit
  interviews.
• Likert scale rate of manual and computer control
  (1 to 5) showed no significant difference in user
  experience preference.
• H1 was not proven.
• Why? Color calibration

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User Testing Results

• H2 With greater levels of autonomy, less user
  input is necessary for control.
• In manual control, counted the number of clicks
  executed by users during runs, divide by run
  time. This yields average clicks per second.
• In computer control, the number of clicks is
  fixed.
• H2 was confirmed.

http//www.cs.uml.edu/robots
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User Testing Results

• H3 It should be faster to move to the target in
  computer control than in manual control.
• Distance to time ratio moving distance X takes Y
  time.
• Under computer control, ARM moved farther in less
  time.
• H3 was confirmed.

http//www.cs.uml.edu/robots
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Current/Future Work

• Identify specific volunteers
• User interface
• User testing
• H1
• Baseline evaluation
• Initial testing at Crotched Mountain
• Integration with power wheelchair
• Depth extraction
• Object occlusion

http//www.cs.uml.edu/robots