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Why Healing Robots are the Way of the Future Liora Engel-Smith The Robots are Coming for Us and It’s a Good Thing

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Page 1: Basic Photography Project

Why Healing Robots are the Way of the Future

Liora Engel-Smith

The Robots are Coming for Us and It’s a Good Thing

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Contents

Introduction 3Chapter 1: Brief History of Rehabilitation 4Robotics 4Chapter 2: Dr. Michelle Johnson 6Chapter 3: Robots Galore 9References 15

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Introduction

If books and movies are symptomatic of social fears, Americans appear to be terrified

of robots. From the Matrix trilogy to the Terminator movies, popular culture reveals

our apprehension of all things technology. We are particularly fearful of robots, which

we expect to become smarter and stronger than us, and consequently annihilate or

subjugate humanity. Even noted physicist Stephen Hawking seems to have joined the

anti-robot camp: in an interview with the BBC Hawking warned that the invention of

robots who can think for themselves will be humanity’s undoing 1.

Reality, however, tells a different story. First, real robots are a far cry from the

menacing creatures of Hollywood’s blockbusters. Even the most sophisticated robot in

the world is still incapable of operating completely on its own. And robots most certainly

cannot yet think for themselves, except in the most benign of ways.

Second, robots can be a positive force in people’s lives, particularly when it comes to

people with disabilities. The possibilities are endless. Robots can be programed to fetch

items for patients who can’t walk; they can support a stroke survivor’s hands and arms

while they goes through rehabilitation exercises. They can even evaluate the patient’s

progress and provide precise feedback and verbal encouragements.

This book explores some of these positive developments in rehabilitation robotics,

particularly Dr. Michelle Johnson’s work in the field. Most importantly, this book is a

testament to the healing powers of robots, and the ways in which they can improve

disabled people’s health, well-being, and independence.

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Chapter 1: A Brief History of Rehabilitation RoboticsRehabilitation robotics became an official field in the 1970s, though related robotics

work began in the 1950s when factories began working on robotic arms for repetitive

tasks1. The first robotic arm to be used in a factory was the Unimate (1961) in a

General Motors factory in Ewing, New Jersey. The arm weighed 4,000 pounds and

was used to weld pieces of metal to form car exteriors. Though its operations were

fairly specific, the Unimate was the first truly programmable electronic arm and the

technology was later used in rehabilitative robots 2,3

While repetitive, predictable movements were useful in factory settings, they were less

useful in therapeutic settings. In 1959, scientists at the Case Institute in Cleveland

used Veterans Administration funding to design the first truly flexible robotic arm. The

arm, known as the Case Manipulator, was an exoskeleton strapped to the arm and

shoulder of a paralyzed person. An external power source and a computer rested on

the floor next to the wearer. The first prototype included a computer with a set of pre-

programed movements. Later, scientists replaced the pre-programed movements with

computer interpretations of electrical signals from the wearer’s upper back muscles.

The Case Manipulator enabled paralyzed people to grasp large objects. The design was

only functional in the laboratory; however, it led to rehabilitative applications involving

electrical muscle stimulation to restore function 3,4.

In 1962, UCLA used Veteran’s Administration funding to create another therapeutic

robot, Rancho Los Amigos. The original prototype was meant for people with limited

motor function but a relatively intact sense of touch. Rancho Los Amigos was an

exoskeletal device mounted on a wheelchair. The arm was originally controlled by a

complex system of tongue switches, but these proved too complicated for users. Thus,

researchers replaced them with a computer controlled by eye movements, which was

easier to use. However, the eye trackers of the time had limited precision, which meant

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the arm moved somewhat erratically. As a result, the project was never implemented

outside of the lab 3,5.

Throughout the decade, robotic arms continued to be heavy and cumbersome; therefore,

they were either stationary or attached to wheelchairs. In 1978, scientists at Stanford

University used Veteran Administration funds to create the first independently moving

rehabilitative robot, known as MoVAR (Motor Vocational Assistive Robot). MoVAR

consisted of a modified industrial robotic arm and a camera on motorized wheels, and was

designed to assist disabled users in the workplace. Users controlled the robot through a

complicated computer with three screens. MoVAR’s movements had to be programmed in

line by line. MoVAR could fetch printer paper and even grasp small objects such as throat

lozenges. The robot had limited use because it was too complicated to control; however, it

was successfully employed by a disabled computer programmer 6. These design flaws led

the scientists to create DeVAR (Desktop Vocational Assistive Robot). DeVAR was easier

to use, owing to its voice-activated controls. It was evaluated as a rehabilitative tool in a VA

hospital; however, the robot was still too expensive and was discontinued 3,6,7.

Multifunctional robots remained difficult to use, so in the 1980s, many projects focused

on single-use robots. One such prototype was a feeding robot designed in Keele University

by a graduate student named Mike Topping, known as Handy 1 robot. The first prototype

consisted of a stationary arm with a spoon attached and a computer. In this setup, an able-

bodied caretaker had to load a program to the computer with each meal. Later, Topping

patented his robot and created the company Cyber Robotics. He continued to improve

Handy 1 and it remains one of the least expensive, most widely used assistive robot in the

industry, serving 200 people worldwide 6,8,9.

The 1990s and early 2000s brought about an explosion of assistive robots such as

autonomous motorized wheelchairs that can evade obstacles, easy to use wheelchairs with

attached arms, and rehabilitative robots for a hospital environment. However, these robots

are still out of reach for most disabled people and in most settings. Usability and cost

continue to challenge scientists in the field 6.

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Chapter 2: Dr. Michelle Johnson

Dr. Michelle Johnson is a compact woman with an easy smile and a booming laugh.

Her passion for rehabilitation robotics is evident in the way she talks about them, and

her ideas are grounded in applications and the real world: she is constantly thinking

about that world beyond the lab. Would therapists be able to use her robots? Could they

help people in developing countries? Her ideas stand in contrast to the unaffordable,

complicated robots of the past. She truly wants to help.

“At some point in my life, I thought I’d be a lawyer,” said Johnson. But in high school, she

said, she decided that engineering was a better fit for her - she was good at math and a

guidance counselor suggested she study engineering. Johnson went to the University of

Dr. Michelle Johnson in her office at University of Pennsylvania

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Pennsylvania. She began her studies in biomedical engineering, but she later switched to

mechanical engineering. “I wanted to learn how to design things,” she said.

“When I graduated, I still didn’t know what it was like to be an engineer,” she said. So after

her graduation in 1990, she interned with the US Army Corp of Engineers and later with

the Westinghouse Electric Corporation 10. In 1992, she decided to further her studies and

got accepted to the mechanical engineering program at University of California-Irvine.

There, she designed a control system that would allow robots to exert appropriate force

for different tasks. This problem may sound esoteric, but it is an important one in the field

of robotics, particularly when robots interact with fragile people. When we grasp an object,

our brains and hands automatically adapt the amount of pressure we put on that object.

We wouldn’t grasp an apple with the same force as we would a fragile glass vase. Robots,

however, do not yet have a brain, and they need to be told exactly how much force to exert.

The decisive moment in her career came a few months later, when her grandmother,

Flo, had a massive stroke. In a matter of days, her grandmother, a once-vibrant person,

became disabled. “That actually had an impression on me and while I was doing [my]

master’s program, a colleague was working on this robot for a wheel chair and that was

the first time I put robots and people with disabilities together in the same sentence,” she

said.

The colleague introduced her to the field of rehabilitation robotics and suggested she go to

a conference about it. At the conference, she met Dr. Larry Leifer and Dr. Machiel Van der

Loots, who pioneered several robotics projects in the 1980s and 1990s, and who would

later become her advisers. She pitched her ideas about stroke rehabilitation and robotics

and they suggested she apply to Stanford University.

Her grandmother died in 1995, a year after Johnson began working on her stroke-

rehabilitation project. “The irony is, she inspired the beginning and I dedicated my

dissertation to her. In many ways, her situation motivated my decision,” she said.

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Her work in Stanford focused on robotics rehabilitation for stroke survivors. She and

her colleagues built Driver SEAT (Driver’s Simulation Environment for Arm Therapy).

This device was designed to help stroke survivors relearn how to drive. The device has

a wheel and a computer screen that shows a road. Stroke survivors use the wheel to

drive; the wheel not only measures the amount of force they’re using, but also resists

their movements so they can exercise their arms. Johnson tested the device on 8 stroke

survivors and found that their motor functions improved as a result of using Driver SEAT 11-13. Johnson got her doctorate in 2002.

She did her post-doctoral research at Marquette University and later directed the

Robotics Research and Design Laboratory at the Clement Zablocki VA. There she

designed ADLER (the Activities of Daily Living Exercise Robot), a stationary arm connected

to a computer that allows stroke survivors to practice tasks such as eating and grasping

with the assistance of the robot 14,15.

In 2013, she joined the University of Pennsylvania, where she heads her own laboratory.

Her research focuses on affordable rehabilitation robots to assist stroke survivors in

clinical and home settings 16,17.

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Chapter 3: Robots Galore

Baxter’s original hands (seen here) are too limited and they will be replaced with more-flexible hands.

Johnson’s laboratory is a small room at the department of physical medicine and

Rehabilitation in University of Pennsylvania. Though it looks like an average laboratory,

Johnson’s projects are at the cutting edge of rehabilitation robotics.

Baxter Baxter is a manufacturing robot made by Rethink Robotics, but Dr. Johnson and

her team reprogramed it and replaced its hands to make it more appropriate for

therapeutic use 21,22. Baxter will help stroke survivors relearn to use their hands by

demonstrating the correct movements and helping them lift their arms and hands.

In addition, it will track their movements and provide feedback on their accuracy and

progress. Johnson’s team will “teach” Baxter the necessary movements by analyzing

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Baxter is a modified industrial robot. He can “learn” movements from 3D videos and will instruct and assist stroke survivors with their physical therapy exercises.

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three-dimensional videos of physical therapists in action. Baxter’s program then

translates the video analysis into movement 22.

TheraDriveTheraDrive is a second iteration of Johnson’s Driver SEAT simulator. It has a crank arm

rather than a steering wheel to allow stroke survivors to work on a greater range of

movement during therapy. The crank wheel can be set to resist movement more or less

depending on the user’s level of disability. Stroke survivors can play different video games

to keep them engaged in the therapy. Last year, Johnson and her team tested the device

on 8 subjects; all of the patients preferred this new system to Driver SEAT. In addition,

they all showed improved motor abilities 18.

Because of her interest in global health, Dr. Johnson deliberately designed the TheraDrive

to be cost-effective. Her laboratory has partnered with a Mexican group to use the

TheraDrive there. She is also in the process of testing the TheraDrive’s feasibility in

Botswana 19.

TheraDrive’s crank-arm replaced the previous model’s steering wheel

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FloFlo (named after Johnson’s grandmother) is a lower-cost therapy robot intended for home

use or in a remote clinic with few specialists. The robot is created from two existing robots

designed by others. VGo is a motorized screen and camera already used for telemedicine

applications in some hospitals. NAO is a commercial robot previously used in therapy for

children with autism. The VGo robot will enable doctors and physical therapists to connect

with stroke survivors remotely and check their progress. The NAO component can teach

patients movements, track their progress, and provide encouragement 20.

Flo can remotely connect clinicians and patients and assist with exercises

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BiASBiAS (Bilateral Assessment System) is an evaluative tool to assess coordination between

a stroke survivor’s two arms. The machine consists of two gloves attached to movement

sensors. BiAS has been tested in two separate studies. The first study involved showing

that BiAs could evaluate impairment accurately. The second study involved robot-assisted

therapy. BiAs was used before and after therapy to assess patient progress 23.

One of the BiAS gloves attached to the movement sensors

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BiAS has been tested in two separate studies and was found accurate and useful.

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References1. Stephan Hawking Warns of the Dangers of Artifical Intellegence. 2014. (Accessed at http://www.ign.com/articles/2014/12/03/stephen-hawking-warns-of-the-dangers-of-artificial-intelligence.)2. Unimate. 2012. (Accessed at http://www.robothalloffame.org/inductees/03inductees/unimate.html.)3. Van der Loos HFM, Reinkensmeyer DJ. Rehabilitation and Health Care Robotics. In: Siciliano B, Khatib O, eds. Springer Handbook of Robotics. Stanford, CA Columbia University; 2008.4. How and when did the rehabilitation engineering center program come into being? . 2002. (Accessed at http://www.rehab.research.va.gov/jour/02/39/6/sup/reswick.html.)5. Rahman T, Sample W, inventors; Orthosis device. United States. 2004.6. Hillman M. Rehabilitation robotics from past to present - a historical prespective. In: The 8th International Conference on Rehabilitation Robotics; 2003; Daejon, South Korea 2003. p. 1-4.7. Lessons Learned in the Application of Robotics Technology to the Field of Rehabilitation. 1995. (Accessed at http://web.stanford.edu/group/rrd/People/vdl/publications/IEEE95/IEEE.TRE.tech.html.)8. Topping M. An overview of Handy 1, a rehabilitation robot for the severly disabled. Artifical Life Robotics 2000;4:188-92.9. Topping M, Smith J. Handy 1: A Robotic System to Assist the Severely Disabled TechKnowLogia 2002.10. Design and Industry Experiance n.d. (Accessed at http://web.stanford.edu/group/rrd/People/johnson/New/design.html.)11. Embedded Corrective Force Cueing: A force-feedback control design to optimize the motivating potential of robot-assisted therapy devices to increase bilateral functioning in hemiplegic stroke patients. n.d. (Accessed at http://web.stanford.edu/group/rrd/People/johnson/abstract.html.)12. Driver’s SEAT, A Simulated Environment for Arm Therapy. 1999. (Accessed at http://web.stanford.edu/group/rrd/Projects/2kprojects/stroke17.html.)13. Johnson MJ, Loots HFMVd, Burger CG, Leifer LJ. Driver’s SEAT: Simulation Environment for Arm Therapy. In: CORR ’99: International Conference on Rehabilitation Robotics. Stanford, CA; 1999.14. GRASP Special Seminar - Michelle Johnosn, Marquette University, “Insights Into Motor and Brain Changes After Robot-Assisted NeuroRehbilitation”. 2012. (Accessed at http://www.grasp.upenn.edu/seminars/Michelle_Johnson.)

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15. MCW/Marquette Medical Alumni Association: Collaboration is King. 2010. (Accessed at http://www.mcw.edu/AlumniNews/2010Stories/Grantushersneweraofcollaboration.htm.)16. Michelle Johnson. 2013. (Accessed at https://vivo.upenn.edu/vivo/display/pi79132543.)17. Hi-Tech Approach to Rehab. 2014. (Accessed at http://news.pennmedicine.org/inside/2014/09/head-hi-tech-approach-to-rehab-photo-a-team-of-researchers-led-by-michelle-j-johnson-phd-director-of-penns-rehabi.html.)18. Theriualt A, Naguka M, Johnson MJ. Therapeutic Potential of Haptic Theradrive: An Affordable Robot/Computer System for Motivating Stroke Rehabilitation. transhepatic Journal 2014;7:161-74.19. The Rehabilitation Robotics Lab at Penn: Global Health. n.d. (Accessed at http://pennrehabrobotics.org/global-health/.)20. Wilk R, Johnson MJ. Usability Feedback of Patients and Therapists on a Conceptual Mobile Service Robot for Inpatient and Home-based Stroke Rehabilitation. In: EEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics Sao Paulo, Brazil; 2014.21. Baxter with Intera 3. n.d. (Accessed at http://www.rethinkrobotics.com/baxter/.)22. Mobile Therapy Assistants. n.d. (Accessed at http://pennrehabrobotics.org/mobile-service-robot/.)23. Johnson MJ. Bilateral assessment of functional tasks for robot-assisted therapy applications. Medical and Biological Engineering and Computing 2011;49:1157-71.