Applications of Robotics and Computer Games in Pediatric Rehabilitation to Improve Motor Function:...

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


10m walk6min walk

Gait analysis


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



*** *

Walking Speed 10m Walk Test

Walking Endurance 6 min Walk Test





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


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





Hip Kinematics

Hip flexion in swing

Pre Post

CPL007

PrePost

Pre Post

Hip extension in stance

No significant change



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

*


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)


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)


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

… 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

… 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 …

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

[email protected]

Applications of Robotics and Computer Games in Pediatric Rehabilitation to Improve Motor Function:...

Documents

Transcript of Applications of Robotics and Computer Games in Pediatric Rehabilitation to Improve Motor Function:...