Upload
vernon-hopkins
View
224
Download
0
Tags:
Embed Size (px)
Citation preview
Applications of Robotics and Computer Games in Pediatric Rehabilitation to Improve Motor Function: Byte Me!
Paolo Bonato, PhDDonna L. Nimec, MD, MSMichelle Coldwell, OTR/L
Objectives
1. Review examples of currently available technology to improve motor function in pediatrics.
2. Review results of technology for robot-assisted gait training.
3. Show how interactive gaming can improve functional results.
4. Describe characteristics of robotics that can improve function more effectively.
5. Discuss how the motor learning literature can guide us to set up better interventions.
6. Review the characteristics of effective interactive gaming systems for rehabilitation.
Speakers
Donna Nimec, MDDirector, Pediatric Physical Medicine & Rehabilitation Spaulding Rehabilitation Hospital
Paolo Bonato, PhDDirector, Motion Analysis Lab Spaulding Rehabilitation Hospital
Michelle Coldwell, OTPediatric Occupational TherapistSpaulding Rehabilitation Hospital
Disclosures
The speakers have no financial disclosures related to the content of the material presented in this course.
Robot-Assisted Rehabilitation
Galloway and AgrawalUniversity of Delaware
Robotics
Meyer-HeimUniversityof Zurich
Children’s Healthcare of Atlanta
Hogan and Krebs, MITFasoli, MGH IHP
Wilmington Robotic Exoskeleton
Robotics for Gait Rehabilitation
End-effector vs. Exoskeleton Systems
GEORehaTechnology
LokomatHocoma
Robotics for Gait Rehabilitation
Wyss InstituteHarvard University
Aretech ZeroG
Robotics for Gait Rehabilitation/“Facilitation”
B-Temia
Alter-GBionic Leg
ReWalk
Ekso Bionics
Robotics for Upper-Limb Rehabilitation
End-effector vs. Exoskeleton Systems
MIT Manus Armeo Power
Robotics for Upper-Limb Rehabilitation
Pablo by TyroMotion
Armeo SpingHocoma
Non-Actuated andTracking Systems
YouGrabber by YouRehab
Gait Training in Children with Cerebral Palsy
Gait Training in Children with Cerebral Palsy
• There has been an increased interest in gait training in children with cerebral palsy
• Treadmill training with bodyweight support:– Schindl et al. (2000)– Cherng et al. (2007)– Dodd and Foley (2007)
• Treadmill training with bodyweight support and a driven gait orthosis:– Meyer-Heim et al. (2007)
Driven Gait Orthosis
Pediatric Lokomat© Hocoma AG, Switzerland
Subjects
• Boys and girls• ages 5 – 14 years• diagnosis of spastic diplegia due to cerebral palsy• ambulatory but have difficulty walking• ability to walk 50’ (with an assistive device)• medically stable
n =14 n = 4 n = 2
Protocol
No targeted gait training or anti-spastic medication
1 week
PRE-EVAL
1 week6 weeks
LOKOMAT TRAINING POST-EVAL FOLLOW-UP
3 months
Gait analysis
PT evaluationGMFM D & E
10m walk6min walk
3 x 1 hr sessions/week 30 min walking/session
3 x 10 min
18 sessions
Gait analysis
PT evaluationGMFM D & E
10m walk6min walk
Gait analysis
PT evaluationGMFM D & E
10m walk6min walk
Pediatric Lokomat Training
A child during a gait training session with the Pediatric Lokomat. Photo used with permission.
A
B
C
D
• (A) Bodyweight support
• (B) Guidance force
• (C) Treadmill speed
• (D) Distance walked
A
B
D
C
40
45
50
55
60
65
70
75
80
85
90
0 5 10 15 20 25 30
0
10
20
30
40
50
60
70
0 5 10 15 20 25 30
session 1
session 6
session 12
session 18
1.4
1.5
1.6
1.7
1.8
1.9
0 5 10 15 20 25 30
650
675
700
725
750
775
800
0 2 4 6 8 10 12 14 16 18
Gia
nce
fo
rce
(%)
Bo
dy
wei
gh
t su
pp
ort
(%
)
A B
C D
Wal
kin
g s
pee
d (
km/h
r)
Training sessionTraining minutes
Training minutes Training minutesProgression of gait training parameters for a child using the Pediatric Lokomat.
Changing Training Parameters
Results
• 20 subjects have been enrolled• 18 subjects have completed the training
• 16 subjects (12 boys)• Mean age 9.3 years (range 7-13 years)
• Group mean data for pre, post and follow-up evaluations
Clinical Measures
Standing Function GMFM Item D
Walking Function GMFM Item E
* *
* = p<0.05 after ANOVA (repeated measures) followed by Tukey SHD test
pre x post: 32% increase
pre x follow-up: 37% increase
pre x post: 24% increase
pre x follow-up: 33% increase
*** *
Walking Speed 10m Walk Test
Walking Endurance 6 min Walk Test
pre x post: 12% increase
pre x follow-up: 16% increase
pre x post: 16% increase
pre x follow-up: 17% increase
Clinical Measures
* **
Spatiotemporal Gait Parameters
Walking speed Stride length Double support time
Mean (+SD) pooled data for 16 subjects
pre x post: 25% decrease
pre x follow-up: 17.5% decrease
* = p<0.05 after ANOVA (repeated measures) followed by Tukey SHD test
0,4
0,6
0,8
PRE POST FOLLOW-UP0,5
0,6
0,7
0,8
0,9
1,0
PRE POST FOLLOW-UP
* *
0,2
0,4
0,6
0,8
PRE POST FOLLOW-UP
pre x post: 18% increase
pre x follow-up: 14% increase
pre x post: 12% increase
pre x follow-up: 14% increase
Hip Kinematics
Hip flexion in swing
Pre Post
CPL007
PrePost
Pre Post
Hip extension in stance
No significant change
* = p<0.05 after ANOVA (repeated measures) followed by Tukey SHD test
pre x post: 9% increase
Knee Kinematics
Knee extension in stancepre-post: 13% increase
Knee flexion in swing
Pre Post Pre Post
pre-post: 6% increase
Ankle Kinematics
Ankle dorsiflexion in stancepre-post: 15% decrease
Ankle plantarflexion in stance
Pre Post Pre Post
*
* = p<0.05 after ANOVA (repeated measures) followed by Tukey SHD test
Pre-training Post-training
Left
Right
Pre- vs. Post-Training
Summary DGO Study
Results show:
• Increases in clinical scores pre- to post-training which are retained at 3 month follow-up
• Improvements in spatiotemporal gait parameters • Positive changes in gait mechanics
Robotic gait training can lead to improved locomotor function in children with CP.
Similar Results from Other Groups
Meyer-Heim A, Borggraefe I, Ammann-Reiffer C, Berweck S, Sennhauser FH, Colombo G, Knecht B, Heinen F, “Feasibility of robotic-assisted locomotor training in children with central gait impairment”, Dev Med Child Neurol. 2007 Dec;49(12):900-6 Borggraefe I, Meyer-Heim A, Kumar A, Schaefer JS, Berweck S, Heinen F, “Improved gait parameters after robotic-assisted locomotor treadmill therapy in a 6-year-old child with cerebral palsy”, Mov Disord. 2008 Jan 30;23(2):280-3 Meyer-Heim A, Ammann-Reiffer C, Schmartz A, Schäfer J, Sennhauser FH, Heinen F, Knecht B, Dabrowski E, Borggraefe I, “Improvement of walking abilities after robotic-assisted locomotion training in children with cerebral palsy”, Arch Dis Child. 2009 Aug;94(8):615-20 Borggraefe I, Kiwull L, Schaefer JS, Koerte I, Blaschek A, Meyer-Heim A, Heinen F, “Sustainability of motor performance after robotic-assisted treadmill therapy in children: an open, non-randomized baseline-treatment study”, Eur J Phys Rehabil Med. 2010 Jun;46(2):125-31 Borggraefe I, Schaefer JS, Klaiber M, Dabrowski E, Ammann-Reiffer C, Knecht B, Berweck S, Heinen F, Meyer-Heim A, “Robotic-assisted treadmill therapy improves walking and standing performance in children and adolescents with cerebral palsy”, Eur J Paediatr Neurol. 2010 Nov;14(6):496-502 van Hedel HJ, Meyer-Heim A, Rüsch-Bohtz C, “Robot-assisted gait training might be beneficial for more severely affected children with cerebral palsy: Brief report”, Dev Neurorehabil. 2015 Aug 19:1-6
Motor Gains vs. Baseline Values
R2 = 0.234
-15
-10
-5
0
5
10
15
20
0 3 6 9 12 15 18 21 24 27 30 33 36 39
GMFM D baseline
GM
FM
D p
re-p
os
t
R2 = 0.151
-15
-10
-5
0
5
10
15
20
0 3 6 9 12 15 18 21 24 27 30 33 36 39
GMFM D baseline
GM
FM
D p
re-f
ollo
w-u
p
R2 = 0.002
-10
-5
0
5
10
15
20
25
0 6 12 18 24 30 36 42 48 54 60 66 72
GMFM E baseline
GM
FM
E p
re-p
os
t
R2 = 0.010
-10
-5
0
5
10
15
20
25
0 6 12 18 24 30 36 42 48 54 60 66 72
GMFM E baseline
GM
FM
E p
re-f
ollo
w-u
p
Walking Function GMFM Item E
Standing Function GMFM Item D
How To Improve Outcomes in “Non-Respondents”
Several factors have the potential to account for differences observed across subjects in response to gait training.
1) More stringent inclusion/exclusion criteria could lead to more consistent results across subjects.
2) Tools could be developed to maximize (while monitoring) the level of “engagement” of the child during the gait training session.
3) Gait deviations that are currently not addressed by commercially-available systems could be targeted by new robotic devices.
4) Adaptive strategies could be developed if we gained a better understanding of the interactions between the child and the robotic system.
The analysis of the subjects’ baseline characteristics do not appear to correlate with the outcomes of the robotic-assisted gait training intervention.
More Stringent Inclusion/Exclusion Criteria?
Engaging Children During Gait Training?
We are experimenting with the Augmented Feedback module of the Lokomat system to assess its potential benefits.
The user can walk down a street, change direction, reach for objects etc
The system directs the patient’s attention to the effects of his/her movement rather than to the movement itself.
Case Series: Subjects’ Characteristics
Subject #
Subject Code
Gender Age (years)
GMFCS Level
Walking History Gait Type Intervention
#1 II-LMAF Male 5.5 II walking at 2 years
walking independently at 4 years
Crouch Robotic gait training +
Augmented feedback
#2 II-LM Male 8 II walking at 2 years
walking independently at 4 years
Crouch Robotic gait training
#3 III-LMAF Male 6.5 III walking at 5.5 years
unable to walk independently
Toe walking Robotic gait training +
Augmented feedback
#4 III-LM Female 5.5 III walking at 4 years
unable to walk independently
Toe walking Robotic gait training
Subject Code Time (min) Speed (m/s) Distance (m) Unloading (%BW) Guidance (%)
II-LMAF 26 - 30 1.5 - 1.7 581 - 911 47 - 16 100 - 40
II-LM 30 - 32 1.5 - 2.0 742 - 950 75 - 0 100 - 30
III-LMAF 26 - 30 1.5 - 1.7 443 - 871 71 - 30 100 - 40
III-LM 20 - 31 1.5 - 1.8 499 - 980 50 - 11 100 - 65
Case Series: Training Parameters
(Km/h)
Case Series: Clinical Outcomes
GMFM D GMFM E
Subject Code Pre Post Follow up Pre Post Follow up
II-LMAF 30 33 33 33 56 58
II-LM 30 34 34 40 52 50
III-LMAF 3 13 13 6 9 10
III-LM 3 9 11 13 12 11
10 m walk (m/s) 6 min walk (m)
Subject Code Pre Post Follow up Pre Post Follow up
II-LMAF 0.83 1.02 1.12 176 286 340
II-LM 0.96 1.11 1.03 298 295 298
III-LMAF 0.56 0.98 1.28 264 308 326
III-LM 0.36 0.65 0.87 109 121 128
Case Series: Spatio-Temporal Parameters
Cadence (steps/min) Walking Speed (m/s)
Subject Code Pre Post Follow up Pre Post Follow up
II-LMAF 147 130 112 0.88 0.92 0.76
II-LM 120 117 120 0.75 0.80 0.89
III-LMAF 112 80 90 0.54 0.28 0.40
III-LM 103 104 89 0.35 0.38 0.33
Stance (% stride) Step Length (m)
Pre Post Follow up Pre Post Follow up
Subject Code L R L R L R L R L R L R
II-LMAF 65 62 64 64 65 61 0.37 0.36 0.43 0.42 0.43 0.40
II-LM 67 62 68 60 63 62 0.36 0.36 0.38 0.42 0.44 0.46
III-LMAF 49 64 63 50 62 66 0.19 0.41 0.10 0.35 0.23 0.30
III-LM 62 70 64 70 73 71 0.28 0.17 0.23 0.19 0.26 0.18
Case Series: Gait Biomechanics
-20
0
20
40
-20
0
20
40
Pe
lvic
Tilt
(d
eg
)H
ip F
lex
/Ex
t (d
eg
)K
ne
e F
lex
/Ex
t (d
eg
)A
nk
le F
lex
/Ex
t (d
eg
)
0 20 40 60 80 100
%Gait Cycle
0 20 40 60 80 100
%Gait Cycle
60
40
20
0
-20
0
20
PrePostFollow up
0
2
4
-20 20 40 60 80 100
%Gait Cycle
0 20 40 60 80 100
%Gait Cycle
An
kle
Mo
me
nt
(Nm
/kg
)A
nk
le P
ow
er
(W/k
g)
0
1
2
PrePostFollow up
Patient II LMAF showed improvements in gait kinematics, but no major changes gait kinetics.
Case Series: Gait Biomechanics
-20
0
20
40
-20
0
20
40
0 20 40 60 80 100
%Gait Cycle
Pe
lvic
Tilt
(d
eg
)H
ip F
lex
/Ex
t (d
eg
)K
ne
e F
lex
/Ex
t (d
eg
)A
nk
le F
lex
/Ex
t (d
eg
)
0 20 40 60 80 100
%Gait Cycle
60
40
20
0
-20
0
20
PrePostFollow up
0 20 40 60 80 100
%Gait Cycle
0 20 40 60 80 100
%Gait Cycle
0
2
4
0
1
2
-2
An
kle
Mo
me
nt
(Nm
/kg
)A
nk
le P
ow
er
(W/k
g)
PrePostFollow up
Patient II LM showed smaller improvements in gait kinematics, but showed improvements in ankle kinetics.
Case Series: Summary Results
GMFCS level II children showed small improvements in GMFM section D (standing) score, but large changes in GMFM section E (walking) score. GMFCS level III children showed the opposite response to the intervention.
GMFM section E score and walking speed changes were larger in children undertaking training with the augmented feedback. Only the subjects who received training with augmented feedback showed a large improvement in endurance.
Gait analysis revealed a general improvement in gait symmetry.
Biomechanical changes were observed in GMFCS level II children. No consistent changes were observed in the GMFCS level III children.
Similar Results from Other Groups
Koenig A, Wellner M, Köneke S, Meyer-Heim A, Lünenburger L, Riener R, “Virtual gait training for children with cerebral palsy using the Lokomat gait orthosis”, Stud Health Technol Inform. 2008;132:204-9 Brütsch K, Schuler T, Koenig A, Zimmerli L, -Koeneke SM, Lünenburger L, Riener R, Jäncke L, Meyer-Heim A, “Influence of virtual reality soccer game on walking performance in robotic assisted gait training for children”,J Neuroeng Rehabil. 2010 Apr 22;7:15 Schuler T, Brütsch K, Müller R, van Hedel HJ, Meyer-Heim A, “Virtual realities as motivational tools for robotic assisted gait training in children: A surface electromyography study”, NeuroRehabilitation. 2011;28(4):401-11 Brütsch K, Koenig A, Zimmerli L, Mérillat-Koeneke S, Riener R, Jäncke L, van Hedel HJ, Meyer-Heim A, “Virtual reality for enhancement of robot-assisted gait training in children with central gait disorders”, J Rehabil Med. 2011 May;43(6):493-9 Labruyère R, Gerber CN, Birrer-Brütsch K, Meyer-Heim A, van Hedel HJ, “Requirements for and impact of a serious game for neuro-pediatric robot-assisted gait training”,Res Dev Disabil. 2013 Nov;34(11):3906-15 Meyer-Heim A, van Hedel HJ, “Robot-assisted and computer-enhanced therapies for children with cerebral palsy: current state and clinical implementation”, Semin Pediatr Neurol. 2013 Jun;20(2):139-45
How To Improve Outcomes in “Non-Respondents”
Several factors have the potential to account for differences observed across subjects in response to gait training.
1) More stringent inclusion/exclusion criteria could lead to more consistent results across subjects.
2) Tools could be developed to maximize (while monitoring) the level of “engagement” of the child during the gait training session.
3) Gait deviations that are currently not addressed by commercially-available systems could be targeted by new robotic devices.
4) Adaptive strategies could be developed if we gained a better understanding of the interactions between the child and the robotic system.
… are we targeting the right aspects of gait control?
Addressing All Gait Deviations?
Controlling Pelvis Motion During Gait Training
Force feedback:Load cell between pelvic brace and actuator shaft
Force generation and position feedback:Linear servo-tube actuators
Linear guides for movement in non-actuated DOFs
Controlling Pelvis Motion During Gait Training
2 actuated DOFVertical positionObliquity
4 “allowed” DOFPelvic rotationPelvic tiltSide to side translationForward – back
translationTwo linear actuators
Positioned verticallyApply forces to pelvic
area via orthopedic brace
How To Improve Outcomes in “Non-Respondents”
Several factors have the potential to account for differences observed across subjects in response to gait training.
1) More stringent inclusion/exclusion criteria could lead to more consistent results across subjects.
2) Tools could be developed to maximize (while monitoring) the level of “engagement” of the child during the gait training session.
3) Gait deviations that are currently not addressed by commercially-available systems could be targeted by new robotic devices.
4) Adaptive strategies could be developed if we gained a better understanding of the interactions between the child and the robotic system.
… can we infer the potential for motor learning from the interaction between the child and the robotic system?
Developing Adaptive Training Strategies?
Force Field Adaptation Paradigm
Baseline.
Force Field (Adaptation Phase).
Return to Baseline (Retention Assessment).
Borrowing from Upper Extremity Motor Learning …
Force Field Adaptation Paradigm
Baseline.
Force Field (Adaptation Phase).
Return to Baseline (Retention Assessment).
Borrowing from Upper Extremity Motor Learning …
Force Field Adaptation Paradigm
Baseline.
Force Field (Adaptation Phase).
Return to Baseline (Retention Assessment).
Borrowing from Upper Extremity Motor Learning …
Force Field Adaptation Paradigm
Baseline.
Force Field (Adaptation Phase).
Return to Baseline (Retention Assessment).
Borrowing from Upper Extremity Motor Learning …
Force Field Adaptation Paradigm
Baseline.
Force Field (Adaptation Phase).
Return to Baseline (Retention Assessment).
Borrowing from Upper Extremity Motor Learning …
Force Field Adaptation Paradigm
Baseline.
Force Field (Adaptation Phase).
Return to Baseline (Retention Assessment).
Borrowing from Upper Extremity Motor Learning …
Force Field Adaptation Paradigm
Baseline.
Force Field (Adaptation Phase).
Return to Baseline (Retention Assessment).
Borrowing from Upper Extremity Motor Learning …
Lower-Limb Motor Adaptations
The upper-limb force field adaptation paradigm can be extended to the lower limbs by considering the trajectory of movement associated with gait in the knee vs. hip joint coordinate space and by introducing a perturbation orthogonal to the direction of movement in this space.
Lower-Limb Motor Adaptations
Motor Adaptations vs. Clinical Outcomes
Surprisingly, we know very little about the relationship between the ability of patients to generate motor adaptation strategies and the clinical outcomes of rehabilitation interventions.
It is conceivable that assessing the ability of individuals to generate motor adaptation strategies could lead to targeted interventions thus resulting in better clinical outcomes.
Discussion Points …
While the need for motor rehabilitation is obvious, the way to go about it is not. A simple “exercise prescription” is clearly not sufficient to achieve satisfactory motor gains.
Intensity and specificity of training appear to be key in motor training. Interactive games are not “good enough” per se.
Robotic-assisted motor training is promising, but existing systems need to be improved and the interaction between patient and robot better understood.
Since not everybody responds to error feedback, the achievement of motor gains requires ways to elicit motor adaptations.
Interactive-Gaming in Rehabilitation
Virtual Reality and Interactive Gaming
Ottawa Hospital
Music Glove
Microsoft Kinect
Virtual Reality and Interactive Gaming
Off-the-shelf vs. dedicated systems
DevMotion
Hospital Nacional de Parapléjicos de Toledo
Nintento Wii
Microsoft Kinect
Conclusions
1. Robotics provides the means to deliver high-intensity, task-specific training.
2. However, interventions have to be individualized by taking into account each subject’s motor abilities.
3. Interactive-gaming based interventions allow one to motivate patients, but also challenge them during the intervention.
4. The design of interactive-gaming based interventions has to be carried out based on specific clinical objectives.
http://srh-mal.net/
Donna Nimec, MDDept. of PM&R
Harvard Medical SchoolSpaulding Rehabilitation Hospital