mazharul islam (uiuc) · 2015. 10. 3. · 2015 fluid power innovation & research conference 3...

Mazharul Islam (UIUC)

Morgan Boes (UIUC)

Ziming Wang (UIUC)

Adviser

Prof Elizabeth T. Hsiao-Wecksler (UIUC)

University of Illinois at Urbana-Champaign (UIUC)

Motivation

Test Bed 6 Timeline

Progress on Pneumatic AFO

Technology opportunities

PPAFO Gen 3.0

Controls of PPAFO

Clinical Studies using PPAFO

Future directions

Overview

2015 Fluid Power Innovation & Research Conference2015 Fluid Power Innovation & Research Conference 2

2015 Fluid Power Innovation & Research Conference 3

Do energy-to-weight and power-to-weightadvantages of fluid power (FP) continue to hold for tiny, mobile FP systems (10-100 W)?

Drive development of enabling FP technologies

Create new portable, wearable, FP assist devices

TB6 Product Platform: Ankle-Foot Orthosis

Numerous pathologies / injuries create below the knee muscle

weakness and impair gait

Currently no portable powered ankle-foot orthosis available for treatment

Stroke (4.7M*)

Polio (1M*)

Multiple sclerosis (400K*)

Spinal cord injuries (200K*)

Cerebral palsy (100K*)

Trauma

* Number of people in US that would benefit from an active lower limb

orthosis [Dollar and Herr, IEEE Trans Robotics, 24(1): 144-158, 2008]

Major Questions Answered

• Controls• Actuation timing: Subject-specific tuning, misfire avoidance• Gait mode recognition: level ground, stairs, ramps• Autonomous, minimum sensors

• Efficiency improvements for increased runtime• Energy recycling

• Compact components• Actuation system (high torque : small size), Valves, Power supply

• Functional evaluation of externally applied torque• Gait assistance, Gait initiation for patient populations• Which populations would benefit?


ca 2007

ca 2008

PPAFO 1.0

ca 2010Passive

pneumatic

power- harvesting

AFO for motion

control

(Untethered)

Powered

pneumatic

AFO for both

motion

control and

assistance

(Tethered)

Portable Powered

pneumatic AFO for

both motion control

and assistance

(Untethered)

Multiple Design Versions

Started with motion control and progressed to powered actuation

PPAFO 2.0

ca 2012

Modular shell and

hardware design

Reduced-weight

Energy regeneration

Gait mode recognition

Prelim clinical testing

Off-the-shelf componentsPPAFO 3.0

ca 2015Compact increased

torque actuation

Improved controls


Microcontroller connection

Solenoid Valves

Dorsiflexion Regulator

Force Resistive SensorsPneumatic

ActuatorAngle Sensor Housing

CompressedCO2 Bottle

PressureRegulator


Thrust 2: Power HCCI Engine (2B.2)

Thrust 2: MEMS proportional valves (2F)

Stirling Thremocompressors(2B.4)

Using elastomericAccumulator to improve

efficiency (2C.2)

Silencer

Rebound spring

Engine piston

Compressed air output

Microcontroller connection

Solenoid Valves

Dorsiflexion Regulator

Force Resistive SensorsPneumatic

ActuatorAngle Sensor Housing

CompressedCO2 Bottle

PressureRegulator

Current System (Gen 2.0)

11 Nm peak @ 110 psigActuator: 0.5 kg, 64mm (OD) ×60mm(D)

New Linear Actuation System (Gen 3.0)

31 Nm peak at 110 psigActuation sys: 0.9 kg, 56(D)×83(W)×224(L)

Force Sensors

Position sensor

Linear Actuators

Compressed 𝑪𝑶𝟐Bottle

Fast-switchingSolenoidValves

Linear Actuator System

Sector gear train

Pressure sensors

IMU sensor

IMU sensor

Aaron Benjamin, Mechanical Engineering, Montana State University

• Same Range of Motion with Reduced volume (44% volume reduction)• Materials of Housing changed (28% weight reduction)

13Gen 3.1Gen 3.0

Gear Ratio 1:1

Gen 3.0

Gear Ratio 2:1

Gen 3.1

44 % volume

reduction

3D printed housing

Aluminum housing

28% weight

reduction


Fractional TimeEstimator (FT)

Modified Fractional Time Estimator (MFT)

Artificial Neural Network Estimator

(ANN)

Heel FSRANN

Toe FSR

Ankle angle

𝝀State

Input Hidden Layer Output Layer

Hee

l FS

RT

oe

FS

RA

nkl

e A

ng

le


Reference/True State Estimation

LeftGRF

RightGRF

𝜆 = argmin𝑘∈[0,99)

𝒚 𝑡 − 𝒚 𝑘 2

[1] Forner-Corderoa, et al., Journal of Biomechanics, 2006

• Motion capture data

• Force plates’ data

From ankle angle, left GRF, right GRF and their derivatives

• Nearest-Neighbor algorithm[1]

• Reference State

𝒚 found from weighted LSE method

Reference State

(Gold Standard)

FractionalTime (FT)

Modified Fractional Time (MFT)

Artificial Neural Network (ANN)

Performance of different state estimators compared to reference state

From Motion Capture System and force plates

compare

From PPAFO sensor values


* Significantly less than FT (p < 0.05)

0

8

16

24

Comfortable Speed

Err

or

( %

GC

)

FT MFT ANN

0

8

16

24

Variable Speed

Err

or

( %

GC

)

* *

FT MFT ANN

ERROR = mean( abs(TRUE STATE- ESTIMATED STATE) )


Applied at 51%

Too early

Too late

Applied at 40%

Applied at 50%

Applied at 55%

Applied at 60%

20 degree ROM

Plantarflexion

DorsiflexionNormative ankle angle

Method

Fourier coefficient from ankle angle by Discrete Fourier Transformation

(DFT)

Feature extraction &dimension reduction

by Principal Component Analysis (PCA)

Classification of by probability density

functions


Gait Initiation: Parkinson’s disease Mechanical cue to facilitate initial step postural response

Gait Assistance: Multiple Sclerosis

A set of well-coordinated movements, termed anticipatory postural

adjustments (APAs), are generated to take the first step.

APAs are diminished or absent for people with Parkinson’s disease that suffer

from freezing of gait.

Using the PPAFO, APAs can be directly modulated to facilitate the first step.

Shoes only (FES not used) PPAFO

2015 Fluid Power Innovation & Research Conference


• Testing and investigating higher torque

output actuator system with better weight

distribution at the ankle

• Implementing machine learning algorithms • State estimation

• Gait mode recognition

• Determining minimum necessary sensor suite

• Investigating optimal torque timing approach

• Clinical testing of device for gait assistance

and gait initiation• Stroke rehabilitation

mazharul islam (uiuc) · 2015. 10. 3. · 2015 fluid power innovation & research conference 3...

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