S.M.A.R.T Final Presentation 2016

SENIOR DESIGN 2015-2016

SMARTSPECIALIZED MOBILIZATION AND REHABILITATION

TEAM

Outline

• Background• Justification• Goals• Methods• Analysis• Force Sensor Sub-Team• Budget• Key Dates

1

Disability Prevalence

• 1500 children born per year with spina bifida [1]

• 1 in 323 children born with cerebral palsy [2]

• 16.5% of 7-11 year olds have sensory processing disorder [3]

[1] S. E. Parker et al., “Updated National Birth Prevalence estimates for selected birth defects in the United States, 2004-2006,”

[2] D. Christensen et al., “Prevalence of cerebral palsy, co-occurring autism spectrum disorders, and motor functioning - Autism and Developmental Disabilities Monitoring Network, USA, 2008,”

[3] A. Ben-Sasson et al., “Sensory Over-Responsivity in Elementary School: Prevalence and Social-Emotional Correlates,”

3

Physical Therapy

• The AmTryke is a hand and foot tricycle• Therapeutic effects on musculoskeletal control not quantified

• Methods to quantify musculoskeletal activity using:• Kinematic data• Kinetic data• Electromyographic data

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INSERT PICTURE OF AMTRYKE HERE

Overall Objective

•Quantify the therapeutic effects of AmTyke exercise:

•Perform initial study using kinematic data and gross motor function measure (GMFM)

•Improve kinematic measurement process•Develop force sensors for future use in kinetic data gathering

5

High Level Deliverables

Force Sensor Team• Two working handlebar force sensors• Documentation, SolidWorks model, and electrical

schematic of the sensor and circuit for future work

BME• Journal manuscript on the kinematic analysis of

AmTryke rehabilitation

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SMART Members

Data Analysis• Amerz Chek• Daniella Guerrero• Allen Hill (Lead)• Wei Shu

Modeling• Alana Alston• Immanuel Phiri• Alexia Thomas (Sub-

Lead)

Load Cell• Ellie Blow (Sub-

Lead)• Aaron Jones• Johannus Smith 7

Functional Diagram

Data Acquisition

Data Processing

Model Scaling

Kinematic Representation Data Analysis

Processing Team

Modeling Team

Load CellDesign

Test

Working Load Cell

Load Cell Team

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Methods: Experiment

• 6 subjects• Age: 2-7 years old• Disabilities: Cerebral palsy,

spina bifida, SPD, prenatal drug exposure

• Before and after motion capture

• 3-month interval

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Data Processing

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Data Analysis

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0 30 60 90 120 150 180 210 240 270 300 330 360

30

40

50

60

70

80

90

100

110

120

130Old Right Elbow Flexion (deg) vs. Handlebar Angle (deg)

Handlebar Angle (deg)

Rig

ht E

lbow

Fle

xion

(deg

)

Average Std = 15.9918

0 30 60 90 120 150 180 210 240 270 300 330 360

30

40

50

60

70

80

90

100

110

120

130New Right Elbow Flexion (deg) vs. Handlebar Angle (deg)

Handlebar Angle (deg)

Rig

ht E

lbow

Fle

xion

(deg

)

Average Std = 6.9423

0 30 60 90 120 150 180 210 240 270 300 330 360

-50

-40

-30

-20

-10

0

10

20

30

40

Handlebar Angle (deg)

Rig

ht E

lbow

Fle

xion

(deg

)

Right Elbow Flexion (deg) vs. Handlebar Angle (deg)

Old Average Std = 15.99175New Average Std = 6.94228

0 30 60 90 120 150 180 210 240 270 300 330 360-115

-110

-105

-100

-95

-90

-85

-80

-75Right Knee Angle (deg)

Handlebar Angle (deg)

Rig

ht K

nee

Ang

le (d

eg) (

deg)

LeftRightCcnorm = 0.974468

0 30 60 90 120 150 180 210 240 270 300 330 360-115

-110

-105

-100

-95

-90

-85

-80

-75Right Knee Angle (deg)

Handlebar Angle (deg)

Rig

ht K

nee

Ang

le (d

eg) (

deg)

LeftRightCcnorm = 0.973816

0 30 60 90 120 150 180 210 240 270 300 330 360-5

0

5

10Old Knee Angle vs. Handlebar Angle (deg)

Handlebar Angle (deg)

Kne

e A

ngle

(per

cent

)

Average Std = 2.1794

0 30 60 90 120 150 180 210 240 270 300 330 360-5

0

5

10New Knee Angle vs. Handlebar Angle (deg)

Handlebar Angle (deg)

Kne

e A

ngle

(per

cent

)

Average Std = 2.3439

Alana Alston, Immanuel Phiri, Alexia Thomas

Modeling

Process Improvement

Model simplification•Simple vs. Complex

Marker set reduction•What is vital?

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Experiment Setup

Initial Capture:•38 markers •Specifically modeled upper limb movement in a bicycling motion

Control Inverse Kinematics:•OpenSim was used to scale and run inverse kinematics •An average RMS error was calculated for the model as a whole from the individual markers

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Angle Comparisons

3 117 231 345 459 573 687 801 915 1029114312571371148515991713182719412055216922832397251126252739285329670

10

20

30

40

50

60

70

Shoulder Flexion

Complex Simple

Time (sec)

Ang

le (d

eg)

Angle comparisons

3 117 231 345 459 573 687 801 915 102911431257137114851599171318271941205521692283239725112625273928532967-40

-35

-30

-25

-20

-15

-10

-5

0

Shoulder Adduction

Complex Simple

Time (sec)

Ang

le (d

eg)

Angle comparisons

3 117 231 345 459 573 687 801 915 1029114312571371148515991713182719412055216922832397251126252739285329670

10

20

30

40

50

60

Shoulder Rotation

Complex Simple

Time (sec)

Ang

le (d

eg)

Marker Removal and IK Generation

Marker Removal:•Focused on scapula and lateral elbow markers in this order:•L&R Scap1, L&R TriSpn, L&R Shoulder, L&R ElbowMed

IK Data Generation:•New .mot files were generated each time a marker was removed along with new RMS values.

•Ex. 1.mot: Scap1•Ex. 2.mot: TriSpn•Ex. 3.mot: Scap1 + TriSpn

RMS Values

RMS Values•A MATLAB program was used to calculated the RMS errors of the comparison between the base model and new generated models

Aimed for the following values:•0 < RMS < 5 degrees – for markers on segments with rotations•0 < RMS < 1 cm - for makers on thorax and Pelvis regions •Markers were picked based on their RMS value

Final Marker set

Final Marker set

Force SensorEllie Blow, Johannus Smith, Aaron Jones

Background Research

•Previous Multiaxial Force Sensors•Liu’s Model

• Side-centered Support Beams• 7 cm X 7 cm• 20 strain gages

•Kim’s Model• Corner Support Beams and Central Support Beam• 9 cm X 9 cm• 24 strain gages

Selection

•Liu’s Model, and shrink it down to a 4 cm by 4 cm piece

•Reasons•Easier geometry to produce•Fewer strain gages to measure the same forces and moments

•Closer to the desired size

Prototype•Made of 7075-T6 Aluminum•Hand-milled at the MTDL (18 hours)•Fitted with the Fz Bridge•5 cm X 5 cm•Used to develop a testing procedure for use with the final sensor•Used to validate linearity of responses

Attachment to the AmTryke•Cut off the original handlebar to retain the threading that connects to the bicycle•Slide the baseplate over the cut handlebar and use a nut so that it freely rotates.•Use four 4-40 screws to attach the baseplate to the sensor housing•Attach the handlebar to the housing using a 7/16-20 bolt

Sensor Circuit: Instrumentation Amp

•Gain of 1 •1 ohm resistors are placeholders•Boosts the signal to mitigate noise

Sensor Circuit: Notch Filter

•4 pull notch filter •Focused at 60 HZ to reduce room noise•Leads into final gain phase (R9 resistor)

Sensor Circuit: Final Amp

•Gain of 5•Outputs to the Vicon system•Used to amplify the filtered signal to a level that the Vicon system can work with

Sensor Calibration Testing•Isolate each force and moment by applying along/around the respective axis•Create a input(lbf or lbf*in) vs output(mV) plot to find the voltage increase per lbf or lbf*in for each bridge for each force•This results in a 6X6 matrix

Sensor Calibration w/ Circuit•Comparable results with similar linearity•Proves the response of the circuit tracks with the force applied

Project Budget

Items Expected ActualRaw Materials $75 $122

Strain Gages $900 $285

Manufacturing $400 $781

Electronics $125 $100

Lab and Testing Supplies $250 $250

Total $1750 $1538

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Key Dates

10/20

12/20

01/21

03/07

09/03

04/22Force Sensor BME

Planning Literature compilation ResearchPhase 1 Design Pilot DataPhase 2 Basic Prototyping/Testing Initial DataPhase 3 Rescaling/Manufacturing Final DataFinalization Final Modifications Documentation

Force Sensor

BME

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References

[1] S. E. Parker, C. T. Mai, M. A. Canfield, R. Rickard, Y. Wang, R. E. Meyer, P. Anderson, C. A. Mason, J. S. Collins, R. S. Kirby, A. Correa, and National Birth Defects Prevention Network, “Updated National Birth Prevalence estimates for selected birth defects in the United States, 2004-2006,” Birth Defects Res. Part A Clin. Mol. Teratol., vol. 88, no. 12, pp. 1008–1016, Dec. 2010.

[2] D. Christensen, K. Van Naarden Braun, N. S. Doernberg, M. J. Maenner, C. L. Arneson, M. S. Durkin, R. E. Benedict, R. S. Kirby, M. S. Wingate, R. Fitzgerald, and M. Yeargin-Allsopp, “Prevalence of cerebral palsy, co-occurring autism spectrum disorders, and motor functioning - Autism and Developmental Disabilities Monitoring Network, USA, 2008,” Dev Med Child Neurol, vol. 56, no. 1, pp. 59–65, Jan. 2014.

[3] A. Ben-Sasson, A. S. Carter, and M. J. Briggs-Gowan, “Sensory Over-Responsivity in Elementary School: Prevalence and Social-Emotional Correlates,” J Abnorm Child Psychol, vol. 37, no. 5, pp. 705–716, Jan. 2009.

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References

[4] S. A. Liu and H. L. Tzo, “A Novel Six-Component Force Sensor of Good Measurement Isotropy and Sensitivities,” Sensors and Actuators A: Physical, vol. 100, no. 2–3, pp. 223–230, Sep. 2002.

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Strain Gage Placement

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Housing

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Dimensions: Top View

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Dimensions: Side View

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Future Work

• Four (4) force sensors for kinetic analysis

• Develop interactive app for AmTryke users

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Handlebar Angle Calculations

• PCA on marker data to extract axis of rotation

• LMS for outlier detection

• Inverse tangent to calculate angle

Y.-S. Liu and K. Ramani, “Robust principal axes

determination for point-based shapes using least median

of squares,” Computer-Aided Design, vol. 41, no. 4, pp.

293–305, Apr. 2009.

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Symmetry Analysis

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Gap Filling

•Interpolation algorithms:•Spline Fill•Pattern Fill•Rigid Body Fill•Kinematic Fill

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