This document summarizes research on robotics and rehabilitation devices for locomotor training. It discusses how body-weight supported treadmill training and robotic gait trainers have expanded access to gait practice but that studies found they were not more effective than other intensive therapies. Researchers are working to improve devices by making training more challenging, engaging the brain more, and requiring more patient effort to maximize the benefits of motor learning and recovery. The future of rehabilitation devices may involve adaptations that dynamically respond to individual patients' capabilities.

2. ROBOTICS AND
REHABILITATION
Diane Damiano, PhD PT
Na8onal Ins8tutes of Health

3. Rehab Devices for Locomotor Training
§ Harnessed treadmills or robo8c gait trainers
common in rehabilita8on centers
§ Goal is to provide step training and prac8ce
through specific sensory inputs by therapist or
robot; i.e. hip extension, plantarflexion
§ Expanded who could prac8ce gait and how much

4. • First report on 10 children with CP; trained 3 mos.
with 0-40% BW; with significant gains
• Training in CP prior to this was mainly NDT; no
strengthening or motor learning approaches, so
treatment made significant gains in GMFM
Body-Weight Supported Treadmill Training
(Schindl et al., 2000)

5. As Amount of Evidence Increased
• RCTs and systema8c reviews indicated these
approaches were effec8ve when compared
to NO TREATMENT, but not be superior to
equally intense alterna-ves
• Why were these not more effec-ve and how
can we improve on these?

6. Should Body Weight-Supported Treadmill Training &
Robo8c-Assis8ve Steppers for Locomotor Training
Trot Back to the Star8ng Gait?
§ LEAPS: Locomotor Experience Applied Post-Stroke ($21M)
§ 400+ RCT comparing gait outcomes at 1 yeay for BWSTT
2 & 6 mos. post stroke to home exercise
§ NO DIFFERENCE!
. Dobkin & Duncan, Neurorehab Neuro Repair, 2012

7. So we are we now?

8. W.Z Rymer, 2010
“The problem is not with the robots
(devices), its with us!”

9. Why are results not beeer?
• Devices too helpful? Assistance reduces
pa8ent effort
• Trunk supported too much
• Support reduces loading (strength)
• Rhythm too regular (constant speed)
• Availability & feasibility issues: center
based, labor & cost-intensive; limits
length and dura8on of training

10. Child with CP on Robo8c Gait Device

11. Neuroscience Principles for MOTOR TRAINING
• Training op8mally should include:
• Self-ini8ated movement & physical effort
• Underlying loose but variable rhythm (CPG)
• Sufficient prac8ce and intensity
• Mental engagement (cogni8ve effort)
• Involve error recogni8on and correc8on
• Be meaningful & mo8va8ng to pa8ent

12. Other explana8ons for why results not beeer?
(Rymer, 2010)
§ Missed “therapeu8c window”
§ Training not intense or long enough?
§ Need “combina8on” therapies to unleash
benefits? (e.g. using brain s8mula8on
during training)

13. What is the “therapeu8c window” in
cerebral palsy?
• GOOD NEWS: Brain & muscles remain plas8c
throughout life; more poten8al when younger
• NOT SO GOOD NEWS: May lose opportuni8es by
not intervening during development of cor8cospinal
tract (window narrows quickly in 1st year of life for
brain RECOVERY)

14. Neurobiology of CIMT
• KITTEN Unilateral CP model (Mar8n & Friel)
• 3 levels of plas8city: SC interneurons have earliest
cri8cal period (3-6 mos); M1 map, CST to SC.

15. “Dose” of therapy
• Intensity key factor in treatment effec8veness,
both intensity of pa8ent effort and 8me in prac8ce
• 60+ hours effec8ve in upper limb training
• Most lower limb training programs use much
lower doses and it may not be sufficient

16. Future: Smarter Scien8sts
§ Need stronger theore8cal background for rehab devices
based on motor learning principles and how these may
(not) differ in brain injuries
§ Must con8nue to inves8gate mechanisms underlying
gait disorders to design novel treatments and devices

17. Future: Smarter Clinicians
• Must be more cri8cal consumers since they are the
bridge from science to family
• Help decide when to use a device and which is
best (watch out for bogus devices)
• match device and pa8ent capabili8es
• Recognize that change comes from pa8ent
effort, not the device!

18. Future: Smarter Devices
• Ones that can op8mally challenge & progress users,
e.g. computerized and VR-based training devices
• Devices that respond to users & adapt accordingly:
• Mobility devices that sense movement
“inten8on” or ease requirements

19. Example of making training more “dynamic”
§ Instead of constant speed, we program random speed
changes or ask pa8ent to voluntarily change speed.
§ Visual target to Increase engagement & brain ac8vity
§ Special harness provides safety but liele trunk support or
control; increases loading and effort.

20. Example 2 : “Smart” harness

21. Gracias / Thank you