Novel methods for the restoration of upper limb and hand motor function

Novel methods for the

restoration of upper limb and

hand motor function

Scuola Superiore Sant’Anna, Pisa (I)

Swiss Federal Institute of Technology, Zurich (CH)

Silvestro Micera

Outline of the presentation

New approaches for neurorehabilitation

The “robotic gym”

New protocols

Synergies among different technological

solutions

Error-enhancing protocol for

neurorehabilitation

A wearable system for FES

Conclusions and future works

Outline of the presentation

New approaches for neurorehabilitation

The “robotic gym”

New protocols

Synergies among different technological

solutions

Error-enhancing protocol for

neurorehabilitation

A wearable system for FES

Conclusions and future works

Classification of machines for

neurorehabilitation

Exoskeleton-like machines:

Application to patients with severe disabilities (when single joint control is required,

absence/very few motor sinergies)

Class I

Mechanical/Hydraulic/Pneumatic actuation High power, very precise

Heavy, non-portable

Class II

Wearable, portable systems Low power, limited precision

Operational-type machines:

Application to patients with moderate disabilities (when the patients feature a sufficient

level of natural motor sinergies)

Class I

Low mechanical inertia/friction High back-driveability

Fine tuning of viscoelastic properties for force fields generation and measurement of the impedance of the human arm

Class II

Simple mechanical structure, no back-driveability Active compensation of inertia/friction

Exoskeleton-like machines

The machine is designed so that the

trajectories of its end-effector AND of

ALL its joints are equal to that of the

natural limb in the operational space

AND in the joint space

The contact between the patient

and the machine is only at the end

effector, through a purposive

mechanical interface (e.g. pedal or

handle)

The machine is designed so that

the trajectory of its end-effector is

equal to that of the natural effector

(hand/foot) in the operational space

The patient is expected to exploit

her/his own synergies at joint level

to follow a trajectory in the

operational space

The MIT-MANUS system (Inmotion Ltd.)

Operational Type Machines

Class I and II

Operational Machines

Among the operational machines, two different classes of

devices can be identified

Class I systems (Volpe et al., 1999) characterized by a low mechanical

inertia/friction, a high back-driveability, fine tuning of viscoelastic

properties for force fields generation and measurement of the impedance

of the human arm, and high cost

Class II (Reinkensmeyer et al., 2002) systems characterized by a simple

mechanical structure, no back-driveability, (in some cases) an active

compensation of inertia/friction and a low cost

Even if Class II operational machines present some limits, they

are very interesting because the low

-

cost and the simplicity of

functioning can make them more acceptable in clinical practice

and even for telerehabilitation

The potentials of these simple machines in terms of functional

The “robotic-gym” for neurorehabilitation

Class II robots for telerehabilitation At the

hospital

Clinical Validation of the MEMOS I

system

Clinical trials (2003 – present) at

Fondazione Maugeri, Veruno (Italy) –

Drs. Pisano and Colombo

P1(X1,Y1) P2(X2,Y2)

Starting position

Final position

Clinical validation

An example of trajectory for subject S1 before and

after the treatment

The activity carried out by the robot is underlined

PRE

Clinical validation

An example of tracking of the squared trajectory for one

An example of tracking of the squared trajectory for one

subject

Clinical assessment scales

The robot-assisted therapy was accepted and well tolerated by all the patients included in the study; no

Robot-derived assessment parameters

Example of the time course of the

motor recovery components assessed by the evaluation metric

One can note that the AMI increases

up to half-way through treatment when the patient is able to complete the

motor task

The mean speed VM is constantly

increasing, indicating continuous improvement of the patient's

performance throughout the treatment.

The mean distance (MD) and the

normalized path length (nPL) decrease, thus showing an

improvement in both accuracy and efficiency of the movement

The nPeaks show the continuous

improvement of movement smoothness

The nFCP shows an improvement of

force control, indicating a positive change in the movement dynamics

Tele-rehabilitation using the MEMOS

Modified from Carigan and Krebs, JRRD, 2006 Therapist at the hospital (checking the

safety of the experiments, the values of the assessment parameters, the need for a change of the protocols, etc.)

Patient at home

Internet

Delay

Delay

Similarly to therapist hand-over-hand assistance during conventional therapy

Highly task oriented practice environments

Different biofeedback1

Active resistive exercises2

Virtual reality3

•Customized therapy depending on patient injury level

•The robot assisted motion when patient could not complete the task

Different training approaches

For highly functional patients, because of the ”ceiling effect” in the learning process, no improvements could be possible

1_{DiPietro et. al. 2005,} 2_{Krebs et. al. 2003,} 3_{Merians et al 2002,} 4_{Patton et al 2005} Standard robotic aid

therapy

New protocols?

EMG-based

control

of pointing

movements

Recording of

EEG signals

Synergies among different technologies

Robotics and FES are

two complementary

rehabilitation

technologies which can

be used together

To restore different motor

functions (e.g., hip

-

k

nee

using robotics, ankle using

FES)

To restore the same motor

functions in a customized

way for the different

Outline of the presentation

New approaches for neurorehabilitation

The “robotic gym”

New protocols

Synergies among different technological

solutions

Error-enhancing protocol for

neurorehabilitation

A wearable system for FES

Conclusions and future works

Exploiting the potentials of motor

learning in neurorehabilitation

There is a general consent on the theory stating that,

when human subjects are asked to move in new

dynamic environments, an Internal Model of the

external world is generated and/or updated by the

CNS to achieve the desired trajectory of the arm

Motor leaning is fundamental in neurological

rehabilitation

Recent studies (Patton et al., 2006) involved the use

of adaptive training techniques with hemiparetic stroke

patients, and concluded that an “amplification

approach” provides a new pathway for augmenting

motor learning in individuals with brain injuries

Exploiting the potentials of motor

learning in neurorehabilitation

This kind of approach may induce the CNS to

attempt a new motor strategy

The change in reflex tone leads to a better

movement, so that if a spastic muscle pulls the limb

to the side and the robot pushes the arm to

increase the error, the spastic muscle would be

shortened

An adaptive training could lead the CNS to promote

learning by making errors

Augmenting errors may be also correlated with

motivation and attention, and it can increase the

signal to noise ratio for sensory feedback and self

evaluation

Divergent force fields in able-bodied

subjects

Burdet et al., Nature, 2001

Divergent force field

Divergent force field

Humans can learn to make accurate

Humans can learn to make accurate

movements by controlling magnitude,

movements by controlling magnitude,

shape, and orientation of the endpoint

shape, and orientation of the endpoint

impedance

A NOVEL ERROR ENHANCING ROBOTIC-AID THERAPY A NOVEL ERROR ENHANCING ROBOTIC-AID THERAPY

ERROR ENHANCING THERAPIES ERROR ENHANCING THERAPIES

Burdet et al, 2001

Comparison between the outcomes of the classic active assistive robotic therapy and a new ”error enhancing therapy”

We are investigating whether: 1) hemiparetic subjects are able

to adapt to unstable dynamics 2) the use of this new protocol

could enhance motor recovery

3) it provides a better outcome when compared with the “assisted-as-needed”

A NOVEL ERROR ENHANCING ROBOTIC-AID THERAPY A NOVEL ERROR ENHANCING ROBOTIC-AID THERAPY

TRAINING PROTOCOL TRAINING PROTOCOL

• 6 weeks of therapy:

2 weeks (10 days, 1 hour session each) - first therapy cycle 2 weeks – break

2 weeks (10 days, 1 hour session each) – second therapy cycle

ACTIVE ASSISTIVE / DIVERGENT FIELD (GROUP 2)

DIVERGENT FIELD/ ACTIVE ASSISTIVE (GROUP 1)

9 turns of the game, being trained with active assistive or DF field, with 1 turn in Null field (NF) conditions

Three different magnitudes of the divergent field (high, medium, and low)

During each day of DF therapy, the hand was deviated initially using a low intensity field, then a high one and finally a middle one.

A NOVEL ERROR ENHANCING ROBOTIC-AID THERAPY

ASSESSMENT OF RECOVERY COMPONENTS

CLINICAL SCALES

Motor Status Score (MSS)

Modified Ashworth Scale (MAS) Range of Motion (ROM)

Chedoke Master Stroke Assessment (CM) Evaluated before and

after each therapy cycle

REACHING INDEXES

Used in NF conditions

NPeaks - Number of peaks in the speed profile Smoothness – The Teulings parameter

5 2 2 * duration S J dt length   =     ∫

nPL - Path Length Parameter

MVD - Movement direction variability

ABSOLUTE HAND PATH ERROR (AHE) Used in DF conditions

A NOVEL ERROR ENHANCING ROBOTIC-AID THERAPY

RESULTS

GROUP 1 gradually become proficient at

producing straighter trajectories: they learned how to contrast the field

GROUP 2 presented a more discontinuous trend: lower decay rate and a not significant correlation coefficient of the regression

A NOVEL ERROR ENHANCING ROBOTIC-AID THERAPY

RESULTS

Significant reduction in impairment of the hemiparetic limbs, as shown by the evolution of the MSS and MAS throughout the therapy

0 10 20 30 40 50 60 70 80 I II I II I II I II I II I II 6 4 3 6 4 3

Chedoke stages Chedoke stages

CF DF

Active assistive DF

A NOVEL ERROR ENHANCING ROBOTIC-AID THERAPY

RESULTS

Variation of the number of peaks depending on both the therapy and the patient severity level

The application of DF seems to be more effective in patient with lower upper limb impairment score

They benefit more if trained in the active assistive than in DF

A NOVEL ERROR ENHANCING ROBOTIC-AID THERAPY

DISCUSSIONS

They could reach the target end perform the exercise but depending on the level of impairment the ability in contrast the perturbation changed

DF seems to have no negative effects

Application of the DF at first could determine a stabilization of the posture

Does fatigue affect the outcome of the results? A 3 month follow up will point better the differences between the two approaches

Divergent/active assistive seems to be better to active assistive/Divergent therapy

Different effects have been observed between different injury level patients and therapies

Robotic-aid therapy led to significant improvements in both cases

Post stroke patients were able to contrast the perturbation

DF/active assistive more effective on mild moderate patients; active assistive/DF more on severe patients

Customization of therapy

For severe pathological subjects interacting with unstable dynamics and motor variability can led to undesired outcomes

Outline of the presentation

New approaches for neurorehabilitation

The “robotic gym”

New protocols

Synergies among different technological

solutions

Error-enhancing protocol for

neurorehabilitation

A wearable system for FES

Conclusions and future works

Main Goal

“

“

To develop a Neuroprosthesis (NP) garment

To develop a Neuroprosthesis (NP) garment

based on novel textile electrode technology to

based on novel textile electrode technology to

restore hand function

restore hand function

“

“

Deliverables

NP garment with integrated [multi-channel] electrode pads, sensing and stimulator Methods for garment integrated sensing & NP control

Sensing: EMG, Flexion etc

Processing: Muscle activity & fatigue, User interaction, control cmds Advanced control algorithms

Targeted Population

Stroke: Largest group, >10,000 / year (USA)

Requirements: Functionality

Grasp functions to

assist

Activities of Daily Living (ADL)

Cylinder (volar) grasp [Light 2002; Sollerman 1995]

Lateral (key) grasp

Opposition (pulp pinch) grasp Stabilise wrist extension (>20±3°)

Easy to (re) configure grasp function for different patient /

user

Electrode re-configuration during early treatment Multiple electrodes /

channels

Adjustable control of timing for grasp function. Adaptable

configurations

Should easily integrate with patient intention

Easily select / start / stop different grasps Goal orientated MMI

Enable

home-based

patient treatment programs

Provide muscle strengthening protocols Muscle strength

training

Introduction to TES

Natural muscle movement

Action potentials (AP’s) from motor cortex AP’s propagate along spinal cord

Produce contraction of muscle fibres

Transcutaneous (surface) Electrical Stimulation (TES)

Activates motor-neurons with electrical pulses Delivered between pairs of electrodes

Introduction to TES

Self Adhesive Transcutaneous Electrodes

Flexible conductive material

Stainless steel mesh, carbonized rubber

Skin interface of conductive hydrogel

Gelatinous adhesive electrolyte

<1mm

Anode (+) Cathode (-)

Sensory receptor activation…

Discomfort during TES

Multiple muscle activation… Lack of selectivity

Accurate cathode

Objectives

Overview

Can we achieve selective finger activation?

Can embroidered electrodes be used for TES?

Selective muscle activation using TES?

Effect of cathode and anode positions?

Embroidered

Measuring Finger Selectivity

finger forces AND wrist torques

Developed Grasp Force and Wrist Torque Assessment System

5 load cells to record isometric finger

forces (A)

6-dof load cell to record isometric wrist

torques (F)

3D measurement system for anatomical

landmarks (C)

Integrated with Virtual Electrode Environment

Includes 64 element multiplexer for arrays Embedded PC running xPC real-time OS Data logging & array control

Selective Activation using TES?

Can middle & ring finger be selectively activated?

Small probe (Ø=3mm), 11×11 grid, 5mm spacing

What is influence of pulse width (PW) on selectivity?

200µs

500µs

Higher PW increases coupling, reduces comfort

Use shorter pulses

Selective activation is possible

Effect of

cathode

_position

Place arrays above extrinsic flexors and extensors 30 elements (~12×12mm), hydrogel interface

Dynamically switch cathode across array surface;

Anode elements remain as far away as possible

Coupled middle and ring finger extension Coupled wrist extension

• Selective middle and ring finger flexion • Coupled wrist flexion

Extrinsic flexor maps Extrinsic extensor maps

Co-activate extensors to compensate for wrist torques

Selective finger flexion

Functional grasp Selective finger activation

Overview

Selective finger flexion requires co-activation of extensors

Can embroidered electrodes be used for TES?

Use shorter pulse widths ~200µs

Anode can be placed

arbitrarily

Embroidered

Embroidered Textile Electrodes

Conductive yarns

Embroidered electrodes

KTI “Smart Electrodes” 7735.1 DCS-LS &

9005.1 PFLS-LS

Embroidered arrays & cables

Prototype NP Design

Optimised electrode positions for Cylindrical, Lateral, Opposition grasps Size, shape, orientation of activation regions adapted using hydrogel pads Embroidered, machine washable garment with integrated electrodes +

cables

Clinical testing starts soon

Extensors EMG Ref Anode Flexors Thumb adductors Index Thumb flexor

Main Contributions: Results

Enables selective activation

Reduces resolution

Dynamic anode placement

Simplifies array design; Continuous hydrogel layer

Complex multiplexer

Embroidered Electrodes

Suitable for use in arrays; enables integrated wiring;

requires use of hydrogel

Multiple Electrode Arrays

Selective finger activation Co-activate flexors & extensors

Electrode Comfort

Comfort related to contact area, not resistivity

Improve Selectivity Simplify Application Improve Comfort Integrate Cabling

Clinical trials

The wearable devices will go into clinical

trials in the next months:

Balgrist Hospital and ZAR, Zurich

University of Southampton

REL, University of Toronto

Particular attention will be devoted to the

combination between robotics and FES for

upper and lower limb function restoration

Outline of the presentation

New approaches for neurorehabilitation

The “robotic gym”

New protocols

Synergies among different technological

solutions

Error-enhancing protocol for

neurorehabilitation

A wearable system for FES

Conclusions and future works

Conclusions

Error-enhancing protocols could be used

with interesting results (especially in

people with mild impairments)

More extensive clinical trials are necessary

to confirm these results

It would be important to define

“customized” strategies to provide

error-enhancing and “assisted-as-needed” trials

Conclusions

Individual limitations of the robotic and FES

therapies can be eliminated by combining the

two modalities

Immediate advantages include promotion of

normal muscle activation, the possibility for

practice of normal patterns earlier during

rehabilitation, reduced requirements on

physical therapist support, and ankle/hand

activation

Wearable systems could address some of the

The “robotic-gym” for neurorehabilitation

Class II robots for telerehabilitation At the

hospital

Micera et al., 2008

MEMOS II will be tested

in a network

of clinical

(Sensory-) Motor impairment Brain injury / Neurological impairment Recover of brain functions (through motor learning and plasticity) Limb motor rehabilitation Neuro-rehabilitation

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Off-line brain imaging (assessment)

Recovery of

motor function

(motor outcome)

Off-line brain imaging (assessment)

On-line brain imaging (assessment)

Epidural neuroproshesis for locomotion

Project

coordinated by

Dr. Courtine

(UZH)

Thanks for the attention!!

Thanks for the attention!!

Email: [email protected]