L24 Rehabilitation Robotics Flashcards

1
Q

Rehabilitation Robotics

A
  • Intelligent Mobility Aides
  • Robot Manipulation Aides
  • Therapeutic Robots: Upper and Lower limb
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2
Q

Intelligent Mobility Aids

A
  • Smart power wheelchairs
  • Smart power assist module (SPAM)
  • Smart wheeled walkers: Guido
  • Sensors: sonar, infrared range finder, laser rangefinder
  • Control software
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3
Q

Hands-Free Wheelchair Prototype I

A
  • School of Theater and Dance, CoTA
  • Designed for a mixed ability dance group
  • Device provides a freedom of movement for a variety of choreographic elements
  • First prototype: converted the chair into a large joystick, where leaning to any direction created a motion towards that direction
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4
Q

Hands-Free Wheelchair Prototype II

A

Second prototype:
* used on almost any commercial powered wheelchair
* Provides wireless control of the powered wheelchair using Bluetooth
* Custom control software for Android based phones
* Accelerometer in phone determine tilt
* Converted to speed power WC controller

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5
Q

Hands-Free Wheelchair - Future

A
  • ultrasonic sensors mounted around the wheelchair
  • ultrasonic sensors: conical sensors, each sensor giving general positional data
  • an input data source for feedback
  • detect obstacles to be avoided
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6
Q

Robotic Manipulation Aids

A
  • Task specific devices: powered feeders, page turners
  • Workstation-based manipulation aides: robotics manipulator built into a workstation
  • Wheelchair mounted manipulation aides: at USF WMRA (Wheelchair Mounted Robotic Arm)
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7
Q

Workstations and Assistive Feeders

A

DeVAR Workstation: Stanford
* Excel at one task or task set
* Confines user independence to one
location

MySpoon Feeder: Secom
* Must be installed or setup by caregiver
* User cannot relocate the device

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8
Q

Rail-Mounted Robotic Arms

A
  • Large hardware installation
  • Confines user to one location
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9
Q

Mobile Robots and Manipulators

A
  • Interaction at a distance
  • Free to move about the environment
  • Safe manipulators
  • Control and automation in development
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10
Q

Wheelchair-Mounted Robotic Arms

A

WMRA-II: Univ. of S. Florida
* Reference frame is natural
* Mounting location convenient for
personal tasks

Weston: Bath Institute
* Applicable for general ADLs
* Safe manipulators
* Social impact is critical

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11
Q

WMRA Designs

A

Two prototype WMRAs have been developed:
* Both use custom 7-DoF robotic arm and 2-DoF power wheelchair for
combined 9-DoF system
* Outperform WMRAs using commercially-available arms [2]
* 7-DoF arm provides capability to overcome singularities, joint limits, and
workspace limitations while performing ADL tasks

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12
Q

iARM by Exact Dynamics

A
  • Product of Exact Dynamics of the Netherlands
  • 6-degree of freedom WMRA with 2-finger end effector
  • Weight: 9 kg
  • Input devices range from 16-button keypads of varying dimension, to 2D joystick, to single-button control.
  • Features highly customizable control scheme in which the end user may store up to 12 arbitrary end effector positions and return to these positions with push-button control
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13
Q

JACO by Kinova

A

*Product of Kinova of Canada
*6-degree of freedom WMRA with 3-finger end effector
*Weight: 5 kg
*Features a weatherproof design and claims to consume less
energy than a standard light bulb
*Standard controller is a 3-axis, or 3D joystick
*Features a high level of singularity and collision avoidance

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14
Q

WMRA Gripper Design

A
  • 1 DOF Parallel motion mechanism.
  • Variable 0 to 10 lb force.
  • Can handle spherical and lever type doorknobs.
  • Can handle switches and push buttons.
  • Can handle round and square objects.
  • Can handle thin and tiny objects.
  • Compatible with the WMRA control system
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14
Q

Vision-Based System for Selecting Objects Using a Brain-Computer Interface (BCI)

A
  • A Brain Computer Interface (BCI) for communicating the needs of a person with severe disabilities (e.g. ALS) to a computer using the brain’s electrical activity
  • Using the oddball paradigm with P300 Event Related Potentials (ERPs)
  • Flashing objects stimulate
    the user’s EEG signal.
  • User sees likely stimuli
    (i.e. the object the user
    wants) as well as unlikely
    stimuli (i.e. irrelevant
    objects or blank space)
  • A custom BCI System
  • Perfroms semi-autonomously activities of daily living (ADLs)
  • Minimum required interactions with the user
  • Maximize accuracy by custom tailoring the view of the environment shown
  • User selects a desired object and task robot performs the task autonomously
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14
Q

Vision-Based System for Selecting Objects Using a Brain-Computer
Interface (BCI) - Modes

A

Move Mode:
* Select the general area of the object of interest.
* The robotic platform will try to navigate there automatically

Selection Mode:
* User selects the cell whose blue dot
lies on the object

  • Using SIFT (Scale Invariant Feature Transform), the system will attempt to
    identify the object of interest and display appropriate task choices
  • For instance, a light switch may generate options to turn the light on or off.
15
Q

BaxBot

A
  • Baxter robot mounted on PowerBot mobile platform.
  • Navigation through unstructured environment and mapping/localization.
  • Interactive demos include: follow me; play connect-four; facial recognition; facial expression; get information off the internet; Assist in activities of daily living
16
Q

BaxBot Teleoperation with Haptic Devices

A

User Interface
* Three cameras are on BaxBot’s head and both end-effectors, and view angles of the cameras are controllable by the user
* Two Phantom Omni devices provide haptics experience
* IR-sensor graph provides the distance information between objects and end- effectors.
* Velocity control based on two modes:
- Base frame control
- End-effector frame control

17
Q

Autonomous Pick and Play

A
  • Use 3D computer vision to recognize
    objects based on their geometries and estimate their 6D poses
  • Use a database of object 3D models and default grasp options
  • Tablet touch screen user interface
18
Q

Human-Robot iPhone Interface

A
  • Regular joysticks cannot provide 6 DoF control.
  • User friendly interface is designed using the iPhone’s accelerometer, gyroscope and touch screen.
19
Q

Lower limb rehabilitation

A
  • Hocoma Lokomat
  • Robotic gait orthosis
  • Used to treat individuals with neurological diseases or injuries such as stroke, SCI, brain injury, MS and cerebral palsy
20
Q

Upper Limb Rehabilitation

A

Forced use
* Effectively retrains highly functioning people with stroke

Robotic Rehabilitation
* Many have been developed - a couple examples are: MIT-MANUS, T-WREX

Two general methods of robot-rehabilitation:
* Assistive - guide the user: trains coordination
* Resistive - push against force: trains strength

21
Q

Bimanual Upper Limb Rehabilitation

A
  • Tight neural coupling allows the same
    motor commands to be duplicated on the opposite side of the body.
  • Physical coupling provides same motions and feedback for both limbs.
22
Q
A