ICVR2013 Workshop: Designing an effective rehabilitation simulation

Sergi Bermúdez i BadiaInv. Assist. Prof., Universidade da Madeira

Marie Curie Research Fellow, Madeira – ITI

[email protected]

University of PittsburghDepartment of Occupational Therapy

NeuroRehabLabhttp://neurorehabilitation.m-iti.orgUniversidade da Madeira / Madeira – ITI

Sergi Bermúdez i BadiaMónica S. Camerião

Athanasios VourvopoulosAna Lúcia dos Santos Faria

Quality of Life Technologies Center http://www.cmu.edu/qoltCarnegie Mellon University

Daniel P. Siewiorek

Scott Bleakley

ICVR 2013: Designing an effective rehabilitation simulation ICVR 2013: Designing an effective rehabilitation simulation -- 26/08/2013 26/08/2013 -- PhiladelphiaPhiladelphia

Myomo Inc http://www.myomo.com

Steve KellyEla Lewis

SPECS - http://specs.upf.eduUniversitat Pompeu Fabra

Paul F.M.J. Verschure

PERCRO - http://www.percro.orgScuola Superiore Sant'Anna

Antonio Frisoli

Hospital del MarMedicina Física i Rehabilitació

Esther Duarte Oller

• Our approach (not necessarily the best one)• The process for developing, designing and testing an

effective rehabilitation simulation– Only Stroke– Particular case of upper limb rehabilitation– Not going to explain or justify the use of VR


– Not going to explain or justify the use of VR

• Metrics for simulated movement performance and monitoring

• Personalization using the metrics• Lessons learned from different studies• Open source / game engines for rehabilitation

activities

1. The process for designing, developing and testing an effective rehabilitation simulation

hypothesis


where

- Designing defining

- Developing formulating and implementing

- Testing clinical validation

Optimal rehabilitation guidelines


VR and Serious Games HCI

Optimal rehabilitation guidelines

Neuroscience


VR and Serious Games HCI

1. Treatment frequency and intensity correlate with recovery (Kwakkel et al., 2004; Sonoda, Saitoh, Nagai, Kawakita, & Kanada, 2004).

deployment

2. Movement practice and repetition play a fundamental role in


2. Movement practice and repetition play a fundamental role in recovery (Karni et al., 1995).

simulated task / game mechanics

3. Specificity of rehabilitation training with respect to the deficits and required functional outcomes has an impact on recovery (Krakauer 2006).

Interface technology & personalization

- The brain has the capability to reorganize itself in such a way that alternate brain areas

take over other functions. The best way to

stimulate this reorganization is still under discussion, and several approaches have been proposed.


proposed.

- Stroke rehabilitation should focus on

maximizing cortical reorganization.Dobkin, Nat ClinPractNeurol 2008

- VR can provide:

- Fully controlled environments

- Minimally supervised intensive training

- Task-specific movement reiteration

- Individualized training


- Feedback for reward and motivation

- Mirror Neuron System: neurons that areactive both, during the execution of goal-oriented movements and during theobservation of the same action performedby others.1-3

- There is strong evidence of the existence


Rizzolatti & Fabbri-Destro, Exp Brain Res 2010

- There is strong evidence of the existenceof a Mirror Neuron System (MNS) in thehuman brain.3-5

1di Pellegrino et al., Exp Brain Res 1992; 2Gallese et al., Brain 1996; 3Rizzolatti & Fabbri-Destro, Exp Brain Res 2010;4Iacoboni et al., Science 1999; 5Buccino et al., Eur J Neurosci 2001

- VR can provide:

- Fully controlled environments

- Minimally supervised intensive training

- Task-specific movement reiteration

- Individualized training


- Feedback for reward and motivation

1Gazzola et al., Neuroimage 2007; 2Maeda et al., J Neurophysiol 2000

The Rehabilitation Gaming System: Spheroids

- Moreover:

- MN can be activated through the

observation of artificial agents.1

- Evidence that a first-personperspective is the optimal frame of reference during action observation.

2

BRAIN

- Generation of Sensory Motor Rhythms- Use of remaining Cortico-Spinal Tracts - Close the sense-act loop

- Activation of Mirror Neuron System- Optimal Rehabilitation Guidelines

- Feedback & motivation

VR

Trade-off:


Trade-off:

In general, the more specific an exercise is…the more complex the interface is…and the more difficult deployment is …

Active movement against gravity/friction

Body movement Finger flexion Tangible interaction

BRAIN




VR


Natural User Interfaces (NUI)



BRAIN

No movement against gravity/friction




VR



Adapted / Assistive Interfaces

Passive assistance Active assistance

* mpower 1000, myomo inc.* Armeo, Hocoma



BRAIN

No movement against gravity/friction




VR

Interaction based on:



Adapted / Assistive Interfaces

Passive assistance Active assistance

* mpower 1000, myomo inc.* Armeo, Hocoma

No active movement

Brain Computer Interfaces (BCI)

Mental Imagery / Neurofeedback approaches

* international 10/20 system

* Armeo, Hocoma

Interaction based on:

- Movement (body kinematics):- Range of Movement (ROM)- Movement speed- Movement latency- Movement smoothness- Muscle synergies- Movement Coordination- EMG

- Autonomic Nervous System (EDA, respiration, HR, etc…)- Brain activity (EEG, NIR, fMRI, etc…)

- Limiting perceived factors in elderly population:

- Perceived barriers due to physical limitations1,2,3

- Lack of knowledge4,5

Study:


1Opalinski, J Technology in Human Services 2001; 2Peacock et al., Eu J of Ageing 2007; 3Ng, Educational Gerontology2008; 4Carpenter et al., Computers in Human Behavior2007; 5Saunders, Educational Gerontology 2004. Rubio et al., Presence 2012

- 8 participants with upper limb deficits ( 4 male + 4 female, M= 44.8 yo, not all strokes)- 3 out of 8 subjects reported previous computer experience.- Mouse + keyboard vs.Key-glove comparison.

- Limiting perceived factors in elderly population:

- Perceived barriers due to physical limitations1,2,3

- Lack of knowledge4,5

Study:

- 8 participants with upper limb deficits

t-test, p = .02


- 8 participants with upper limb deficits ( 4 male + 4 female, M= 44.8 yo, not all strokes)- 3 out of 8 subjects reported previous computer experience.- Mouse + keyboard vs.Key-glove comparison.

Rubio et al., Presence 2012

1Opalinski, J Technology in Human Services 2001; 2Peacock et al., Eu J of Ageing 2007; 3Ng, Educational Gerontology2008; 4Carpenter et al., Computers in Human Behavior2007; 5Saunders, Educational Gerontology 2004.

Originally developed by psychologists Robert M. Yerkes and John Dillingham Dodson in 1908


Yerkes & Dodson , 1908


Cameirão et al., J Neuroeng Rehabil 2010

Hitting Catching Grasping

Automated procedure for difficulty adjustment:

• Task individualization

• Optimized level of performance

• Adapts to individual arms



• Adapts to individual arms

- VR and real counterparts:

- Are they equivalent?

- Comparison with control group

- The calibration task is used


- The calibration task is used to measure arm kinematics and to set the baselineparameters of the training for every session.

- Range of Movement (ROM), movement latency, movement speed.

- Extracted from RGS calibration

- Groups showed a dissimilar pattern of


*p<.05, Mann-Whitney Test

dissimilar pattern of improvement in the speed of the paretic arm over time (ANOVA, p<.05).

Cameirão et al., Rest NeurNeursc, 2011.



12 stroke patients and 10 controls were exposed to random combinations of the 4 game parameters

4-factor ANOVA to disclose main and interaction effects



4-factor ANOVA to disclose main and interaction effects

Multiple regression to estimate constants (m0 ... m9)

- Not all VR parameters may be relevant

- Parameters interact in a non-trivial an non-linear manner.



linear manner.



- 9 stroke patients and 10 controls

- The model captures the behavior of the individual arms by different game parameters, and to adapt


parameters, and to adapt the difficulty level accordingly.


Performance

*p<.05, T-Test

Training paradigm:- Goal oriented and repetitive actions- Bimanual training (non-paretic arm support)- Parameterized (flying speed, turning speed, acceptance radius,


turning speed, acceptance radius, distance between objects)

Motivation:- Embedded in a game- Extensive visual and sound feedback- Automatic computation of training parameters (Avoid failure and frustration)Bermúdez i Badia, Stroke Research & Treat, 2012

A first study with 10 healthy participants

has shown that the NTT captures precise

quantitative kinematic information during a NTT training session, including:

ROM


• Range of Movement (ROM)

• Movement smoothness• Arm coordination• Arm contribution to task

Bermúdez i Badia, Stroke Research & Treat, 2012

s t a d s*t s*a s*d t*a t*d a*d s2 t2 a2 d2

Movement

Kinematic measure = c0 + c1*speed + c2*turning + c3*acceptance + c4*distance+ c5*speed*turning + c6*speed*acceptance + c7*speed*distance+ c8*turning*acceptance + c9*turning*distance+ c10*distance*acceptance + c11*speed

2 + c12*turning2 + c13*acceptance

2

+ c14*distance2


Movement SmoothnessRange of Motion

Arm DisplacementArm coordination

- Not all parameters contribute to all movement kinematic measures

- We have a quantitative way of adapting parameters depending on a higher leveldesired kinematic training

RGS Control

IOT


- N = 16 acute stroke patients

- 3 weekly 20 min sessions during 12 weeks

NSG



- RGS group recovers faster than Controls, but not at follow-up Accelerates recovery

- RGS ≥ extended OT

- Reduces therapy costs


N = 44 chronic stroke patients

5 weekly 30 min sessions during 4 weeks

1. NUI interface

2. Haptic interface (GRAB, PERCRO):


Cameirão et al., Stroke, 2012.

2. Haptic interface (GRAB, PERCRO):

- Force-feedback

- Increase in the ecological validity of the task

3. Exoskeleton (ARMEO, Hocoma):

- Support against gravity

- Control of trunk movements

- All groups improvedsignificantly

- The haptics group retained during a longer period of time

include if possible

- The exoskeleton group


*p<.05, **p<.01, Wilcoxon Test

Within-groupBetween-group

- The exoskeleton group showed a “poorer” improvement at proximal movements

not all assistance is always helpful

Functional vs. correct movement

Cameirão et al., Stroke, 2012.

- 9 naïve healthy subjects (26.4±4.2 years)- 3 experimental conditions

Combined motor execution and motor imagery in VR seems to be more effective at:


effective at:

- Driving cortical sensorimotor areas1, - Increasing attention and alertness to

task2,- Engaging additional related networks

(cross-modal sensory processing3 or short term memory).

1 Solodkin et al., 2004; 2 Egner et al., 2004; 3 Kanayama et al., 2007

Bermúdez et al., IEEE Trans Neur Sys Reh Eng, 2013.

1 Solodkin et al., 2004; 2 Egner et al., 2004; 3 Kanayama et al., 2007

-18 right-handed healthy subjects (24±3 years)- Active and imagination conditions using RGS

Imagery of target catching was related to activation of frontal, parietal,


to activation of frontal, parietal, temporal, cingulate and cerebellar regions, consistent with:

- object processing,- motor intention,- attention,- mirror mechanisms.

Prochnow et al., Eu Journal of Neuroscience, 2013.

- Virtual Environments are simulations that engage the senses of users through real-time 3D graphics, audio and interaction to create an experience of presence within an artificial world.

- Game Engines bring together large-scale software architectures and programming paradigms to design and implement rendering, collision, physics and animation processes.


processes.

Cost Free

Platforms Windows


Platforms Windows

Community Large & commercial

Programming Development Tools

Learning Curve Fast

CustomizationSource code

available

Torque was used to implement the RGS

http://www.rgs-project.eu

http://www.rgs-project.eu/

Cost Free


Platforms Win, Mac, Linux

Community Developers

Programming Python

Learning Curve Slower


available

Panda3D was used to implement the NTT

http://neurorehabilitation.m-iti.org/NTT

http://neurorehabilitation.m-iti.org/NTT

Cost ~ 1500$ / developer

Platforms All


CommunityVery large & commercial

ProgrammingIntegrated

Development Environment

Learning Curve Very Fast

CustomizationUsing external

libraries

Unity is used in the RehabNet project

http://neurorehabilitation.m-iti.org/rehabnet

http://neurorehabilitation.m-iti.org/rehabnet

Cost Free ~ 1500$ / developer Free

Platforms Windows All Win, Mac, Linux


Community Large & commercialVery large & commercial

Developers

Programming Development ToolsIntegrated

Development Environment

Python

Learning Curve Fast Very Fast Slower


availableUsing external

librariesSource code

available

• A close dialog between health practicioners – neuroscientists –technologists is necessary

• Our systems are not the end product, are the hypotheses

• Hypotheses need validation impact assessment

• We believe that positive effects of gaming in rehabilitation training do not emerge out of superficial “gamification” of therapies.


emerge out of superficial “gamification” of therapies.

• Game / training mechanics need to include:

• Neuroscientific principles of recovery

• Difficulty vs skill needs to be quantified and well understood

• Game parameters need to be automatically personalized

• Stress levels need to be controlled to ensure maximal performance and consequent maximal learning

For more information contact:

[email protected]@uma.pt

or visit

http://neurorehabilitation.m-iti.org

Date post:	25-Jun-2015
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