R. Riener, H. Vallery, A. Duschau-WickeETH Zurich, Balgrist University Hospital, Hocoma

Z. Rymer & Y. DhaherRehabilitation Institute of Chicago

MARS-RERC Advisory Board Meeting18/19 August 2009

D1: Cooperative Control for Robot-Aided Gait TherapyD1: Cooperative Control for Robot-Aided Gait Therapy

Zurich NetworkZurich Network

R. Riener & V. Dietz & G. Colombo

Robotic Treadmill Training: LokomatRobotic Treadmill Training: Lokomat

Conventional Version

• Position control: fixed trajectory, no interactivity

• Fixed speed

• Limitted pelvis movement

G. Colombo, V. Dietz

Cited Limitations

• Altered EMG patterns [Hidler & Wall 2005]

• Abnormal force patterns[Neckel et al. 2007]

• Irregular accelerations and decelerations [Regnaux et al. 2008]

• Better treatment outcome of manual therapy compared to Lokomat

[Hornby et al. 2008; Hidler et al. 2009]

Limitations of Robotic Gait TrainingLimitations of Robotic Gait Training

Parameters

VirtualAssistent

Position

Force

Patient-Cooperative ControlPatient-Cooperative Control

Design and evaluate cooperative control strategies that provide more freedom and participation by the patients, while still guaranteeing functional gait training

Assess the effects of cooperative control strategies on stroke patients using quantitative and qualitative measures of gait performance

D1 ObjectivesD1 Objectives

Patient-Cooperative ControlPatient-Cooperative Control

Goal: Active patient participation

Prerequisites-Transparency: “Hide” the robot

when not needed

-Constraints: Keep patient

within safe domain

Goal: Active patient participation

Prerequisites-Transparency: “Hide” the robot

when not needed

-Constraints: Keep patient

within safe domain

Patient-Cooperative ControlPatient-Cooperative Control

Transparency: Task FormulationTransparency: Task Formulation

Minimize Interaction Forces/Torques!

Transparency: Task FormulationTransparency: Task Formulation

Interactiontorques Inertia

Gravity, Coriolis,centrifugal,damping

Actuatortorques

(robot)

Example: Mass with 1 DOFExample: Mass with 1 DOF

1. Given Mass (Robot) Connected to Operator (Human)

2. Given Movement of the Operator (Human)

3. Calculate Forces to Let Mass (Robot) Follow

Example: Mass with 1 DOFExample: Mass with 1 DOF

4. Find Optimal Conservative (Elastic) Force Field as Function of Position

5. Apply Force Field by Actuators (Robot)

Example: Mass with 1 DOFExample: Mass with 1 DOF

Multi-Joint Robot: Optimal Force FieldMulti-Joint Robot: Optimal Force Field

Generalized Elasticities: ResultsGeneralized Elasticities: Results

Mean RMS Interaction Torques at the Joints

Generalized Elasticities: ResultsGeneralized Elasticities: Results

Impact on Gait Parameters

Generalized Elasticities: ResultsGeneralized Elasticities: Results

Gravity cancellation

Generalized elasticities

Goal: Active patient participation

Prerequisites:-Transparency: “Hide” the robot

when not needed

-Constraints: Keep patient

within safe domain

Patient-Cooperative ControlPatient-Cooperative Control

Challenge

•Support, but do not restrict patient

Path Control

•Path: virtual tunnel

•Robot applies assistive and corrective torques

Path ControlPath Control

Path ControlPath Control

1 (hip angle)

2 (

knee a

ng

le)

φrefφact

F

allowed region

reference pathIdeaCombine free timing with spatial guidance

Generalized Elastic Path ControlGeneralized Elastic Path Control

Solution

Re-formulate path control as a potential field with• constraining forces outside the tunnel and• generalized elasticities inside the tunnel.

1 (hip angle)

2 (

knee a

ng

le)

Constraining forces

Transparency-enhancing forces to hide robot

Path Control: Healthy SubjectPath Control: Healthy Subject

Tested on 12 Healthy Subjects

Path Control: iSCI SubjectPath Control: iSCI Subject

Pos Path_a Path_b Imp

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

Hf rz

*

Muscle Activity Heart Rate

Pos.contr.Path contr.

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

0.22

0.24

Init. loading

Mid stance

Term. stance

Pre swing

Init. swing

Mid Swing

Term. swing

Nor

mal

ized

mus

cle

activ

ity (

BF

)

Position controlPath control

Rela

tive incr

ease

of

heart

rate

14 incomplete SCI subjects

Path Control Increases ParticipationPath Control Increases Participation

Path Control Increases VariabilityPath Control Increases Variability

Position Control

-20 -10 0 10 20 30 400

10

20

30

40

50

60

70

80

Hip angle [°]

Kne

e an

gle

[°]

Path Control

-20 -10 0 10 20 30 400

10

20

30

40

50

60

70

80

Hip angle [°]

Kne

e an

gle

[°]

Treadmill Speed AdaptationTreadmill Speed Adaptation

Basic Principle

F = m a = m v.

Admittance Controller

Treadmill

F

a

a

mF

Processing

F

m

=> v = ∫ F/m dt + v0

Is that enough?Is that enough?

Video courtesy of Klinik Valens

Lokomat Extension from 4 to 7 DoFLokomat Extension from 4 to 7 DoF

4 active DoF

1 1

11

7 active DoF

1 1

2 21

Components of 7DoF ControlComponents of 7DoF Control

5 DoF Generalized Elastic Path Control:Hip and knee flexion+ pelvis translation

6 DoF collision avoidance:Hip and knee flexion+ hip abduction

2 x 1 DoF abduction limitation:Abduction only

Evaluation on Stroke Patients at the RIC Evaluation on Stroke Patients at the RIC

Shift to Clinical EvaluationShift to Clinical Evaluation

2009 2010

Balgrist, 4 DoFHealthy; SCI pilot

RIC, 4 DoFStroke pilot

Balgrist, 7 DoFTests on healthies

RIC, 7 DoF Stroke pilot

RIC,Stroke study

today

Balgrist, 7 DoF Multicenter RCT with iSCI?