Rehabilitation and Health Care Robotics 53.2 Physical Therapy and Training Robots 1227 compared to previous sessions.Later robots used ad- several demonstration systems were developed.In the vanced force-based control,which required significantly early 2000s,Corinna Latham of Anthrotronix,Inc.com- more computer power.The early 1990s saw the start mercialized small robot systems to enable children with of the MIT-MANUS Project with Neville Hogan and physical disabilities to play games with simple inter- Igo Krebs,followed a few years later by the Palo Alto faces.Later.small mobile robots were used in clinics VA mirror image movement enabler(MIME)project by Kerstin Dautenhahn's group [53.26]with children and its derivative,Driver's simulation environment for who have autism;since robots have such simple inter- arm therapy (SEAT),with Charles Burgar,Machiel Van faces,communication with them does not appear not der Loos,and Peter Lum,as well as the Rehabilita-be as challenging as with other humans.The early tion Institute of Chicago ARM project with Zev Rymer 2000s also saw the advent of pet robots,such as the and David Reinkensmeyer.Each had a different phi- Paro seal robot developed by Shibata et al.[53.27],as losophy on upper-extremity stroke therapy and each companions for both children and the elderly who are was able to demonstrate clinical effectiveness in a dif- confined to clinics and have limited real companion- ferent way.All three programs,now a decade later,ship. have made significant technical advances and are still The applications for robotics continue to increase active. in number as advances in materials.control software, Cognitive robotics had a start in the early 1980s to higher robustness and the diminishing size of sensors aid children with communication disorders and physi-and actuators allow designers to attempt new ways of cal disorders to achieve a measure of control of their using mechatronics technology to further the well-being physical space.Using mostly educational manipulators, of people with disabilities. 53.2 Physical Therapy and Training Robots 53.2.1 Grand Challenges and Roadblocks nize beginning in the late 1980s,neuro-rehabilitation is a logical target for automation because of its labor- The human neuromuscular system exhibits use-intensive,mechanical nature,and because the amount of dependent plasticity,which is to say that use alters the recovery is linked with the amount of repetition.Robots properties of neurons and muscles,including the pattern could deliver at least the repetitive parts of movement of their connectivity,and thus their function [53.28-30]. therapy at lower cost than human therapists,allowing The process of neuro-rehabilitation seeks to exploit this patients to receive more therapy. use-dependent plasticity in order to help people re- The grand challenge for automating movement ther- learn how to move following neuromuscular injuries or apy is determining how to optimize use-dependent diseases.Neuro-rehabilitation is typically provided by plasticity.That is,researchers in this field must de- skilled therapists,including physical,occupational and termine what the robot should do in cooperation with speech therapists.This process is time-consuming,in-the patient's own movement attempts in order to maxi- volving daily,intensive movement practice over many mally improve movement ability.Meeting this challenge weeks.It is also labor-intensive,requiring hands-on involves solving two key problems:determining appro- assistance from therapists.For some tasks,such as teach- priate movement tasks(what movements should patients ing a person with poor balance and weak legs to walk, practise and what feedback should they receive about this hands-on assistance requires that the therapist have their performance),and determining an appropriate pat- substantial strength and agility. tern of mechanical input to the patient during these Because neuro-rehabilitation is time-and labor-movement tasks(what forces should the robot apply to intensive,in recent years health care payers have put the patient's limb to provoke plasticity).The prescription limits on the amount of therapy that they will pay for,in of movement tasks and mechanical input fundamen- an effort to contain spiraling health care costs.Ironically,tally constrains the mechanical and control design of at the same time,there has been increasing scientific ev- the robotic therapy device. idence that more therapy can in some cases increase There are two main roadblocks to achieving the movement recovery via use-dependent plasticity.As grand challenge.The first is a scientific roadblock: robotics and rehabilitation researchers began to recog- neither the optimal movement tasks nor the optimalRehabilitation and Health Care Robotics 53.2 Physical Therapy and Training Robots 1227 compared to previous sessions. Later robots used ad￾vanced force-based control, which required significantly more computer power. The early 1990s saw the start of the MIT-MANUS Project with Neville Hogan and Igo Krebs, followed a few years later by the Palo Alto VA mirror image movement enabler (MIME) project and its derivative, Driver’s simulation environment for arm therapy (SEAT), with Charles Burgar, Machiel Van der Loos, and Peter Lum, as well as the Rehabilita￾tion Institute of Chicago ARM project with Zev Rymer and David Reinkensmeyer. Each had a different phi￾losophy on upper-extremity stroke therapy and each was able to demonstrate clinical effectiveness in a dif￾ferent way. All three programs, now a decade later, have made significant technical advances and are still active. Cognitive robotics had a start in the early 1980s to aid children with communication disorders and physi￾cal disorders to achieve a measure of control of their physical space. Using mostly educational manipulators, several demonstration systems were developed. In the early 2000s, Corinna Latham of Anthrotronix, Inc. com￾mercialized small robot systems to enable children with physical disabilities to play games with simple inter￾faces. Later, small mobile robots were used in clinics by Kerstin Dautenhahn’s group [53.26] with children who have autism; since robots have such simple inter￾faces, communication with them does not appear not be as challenging as with other humans. The early 2000s also saw the advent of pet robots, such as the Paro seal robot developed by Shibata et al. [53.27], as companions for both children and the elderly who are confined to clinics and have limited real companion￾ship. The applications for robotics continue to increase in number as advances in materials, control software, higher robustness and the diminishing size of sensors and actuators allow designers to attempt new ways of using mechatronics technology to further the well-being of people with disabilities. 53.2 Physical Therapy and Training Robots 53.2.1 Grand Challenges and Roadblocks The human neuromuscular system exhibits use￾dependent plasticity, which is to say that use alters the properties of neurons and muscles, including the pattern of their connectivity, and thus their function [53.28–30]. The process of neuro-rehabilitation seeks to exploit this use-dependent plasticity in order to help people re￾learn how to move following neuromuscular injuries or diseases. Neuro-rehabilitation is typically provided by skilled therapists, including physical, occupational and speech therapists. This process is time-consuming, in￾volving daily, intensive movement practice over many weeks. It is also labor-intensive, requiring hands-on assistance from therapists. For some tasks, such as teach￾ing a person with poor balance and weak legs to walk, this hands-on assistance requires that the therapist have substantial strength and agility. Because neuro-rehabilitation is time- and labor￾intensive, in recent years health care payers have put limits on the amount of therapy that they will pay for, in an effort to contain spiraling health care costs. Ironically, at the same time, there has been increasing scientific ev￾idence that more therapy can in some cases increase movement recovery via use-dependent plasticity. As robotics and rehabilitation researchers began to recog￾nize beginning in the late 1980s, neuro-rehabilitation is a logical target for automation because of its labor￾intensive, mechanical nature, and because the amount of recovery is linked with the amount of repetition. Robots could deliver at least the repetitive parts of movement therapy at lower cost than human therapists, allowing patients to receive more therapy. The grand challenge for automating movement ther￾apy is determining how to optimize use-dependent plasticity. That is, researchers in this field must de￾termine what the robot should do in cooperation with the patient’s own movement attempts in order to maxi￾mally improve movement ability. Meeting this challenge involves solving two key problems: determining appro￾priate movement tasks (what movements should patients practise and what feedback should they receive about their performance), and determining an appropriate pat￾tern of mechanical input to the patient during these movement tasks (what forces should the robot apply to the patient’s limb to provoke plasticity). The prescription of movement tasks and mechanical input fundamen￾tally constrains the mechanical and control design of the robotic therapy device. There are two main roadblocks to achieving the grand challenge. The first is a scientific roadblock: neither the optimal movement tasks nor the optimal Part F 53.2