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S.M.A.R.T Final Presentation 2016
- 1. SENIOR DESIGN 2015-2016 SMART SPECIALIZED MOBILIZATION AND REHABILITATION TEAM
- 2. Outline • Background • Justification • Goals • Methods • Analysis • Force Sensor Sub-Team • Budget • Key Dates 1
- 3. 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
- 4. 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 4 INSERT PICTURE OF AMTRYKE HERE
- 5. 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
- 6. 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 6
- 7. 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
- 8. Functional Diagram Data Acquisition Data Processing Model Scaling Kinematic Representation Data Analysis Processing Team Modeling Team Load Cell Design Test Working Load Cell Load Cell Team 8
- 9. 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 9
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- 14. Data Analysis 14 0 30 60 90 120 150 180 210 240 270 300 330 360 30 40 50 60 70 80 90 100 110 120 130 Old Right Elbow Flexion (deg) vs. Handlebar Angle (deg) Handlebar Angle (deg) RightElbowFlexion(deg) Average Std = 15.9918 0 30 60 90 120 150 180 210 240 270 300 330 360 30 40 50 60 70 80 90 100 110 120 130 New Right Elbow Flexion (deg) vs. Handlebar Angle (deg) Handlebar Angle (deg) RightElbowFlexion(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) RightElbowFlexion(deg) Right Elbow Flexion (deg) vs. Handlebar Angle (deg) Old Average Std = 15.99175 New 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 -75 Right Knee Angle (deg) Handlebar Angle (deg) RightKneeAngle(deg)(deg) Left Right Ccnorm = 0.974468 0 30 60 90 120 150 180 210 240 270 300 330 360 -115 -110 -105 -100 -95 -90 -85 -80 -75 Right Knee Angle (deg) Handlebar Angle (deg) RightKneeAngle(deg)(deg) Left Right Ccnorm = 0.973816 0 30 60 90 120 150 180 210 240 270 300 330 360 -5 0 5 10 Old Knee Angle vs. Handlebar Angle (deg) Handlebar Angle (deg) KneeAngle(percent) Average Std = 2.1794 0 30 60 90 120 150 180 210 240 270 300 330 360 -5 0 5 10 New Knee Angle vs. Handlebar Angle (deg) Handlebar Angle (deg) KneeAngle(percent) Average Std = 2.3439
- 15. Alana Alston, Immanuel Phiri, Alexia Thomas Modeling
- 16. Process Improvement Model simplification •Simple vs. Complex Marker set reduction •What is vital? 16
- 17. 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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- 22. 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
- 23. 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
- 24. Final Marker set
- 25. Final Marker set
- 26. Force Sensor Ellie Blow, Johannus Smith, Aaron Jones
- 27. 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
- 28. 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
- 29. 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
- 30. 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
- 31. Sensor Circuit: Instrumentation Amp •Gain of 1 •1 ohm resistors are placeholders •Boosts the signal to mitigate noise
- 32. Sensor Circuit: Notch Filter •4 pull notch filter •Focused at 60 HZ to reduce room noise •Leads into final gain phase (R9 resistor)
- 33. 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
- 34. 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
- 35. Sensor Calibration w/ Circuit •Comparable results with similar linearity •Proves the response of the circuit tracks with the force applied
- 36. Project Budget Items Expected Actual Raw Materials $75 $122 Strain Gages $900 $285 Manufacturing $400 $781 Electronics $125 $100 Lab and Testing Supplies $250 $250 Total $1750 $1538 36
- 37. Key Dates 10/20 12/20 01/21 03/0709/03 04/22 Force Sensor BME Planning Literature compilation Research Phase 1 Design Pilot Data Phase 2 Basic Prototyping/Testing Initial Data Phase 3 Rescaling/Manufacturing Final Data Finalization Final Modifications Documentation Force Sensor BME 37
- 38. 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. 38
- 39. 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. 39
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- 42. Housing 42
- 45. Future Work • Four (4) force sensors for kinetic analysis • Develop interactive app for AmTryke users 45
- 46. 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. 46
- 47. Symmetry Analysis 𝐶𝑐 𝑛𝑜𝑟𝑚 = max(𝐶𝑐) 𝐴𝑐 𝑅0 ∙ 𝐴𝑐 𝐿0 𝑁𝑆𝐼 = 𝜃 𝑛𝑅 − 𝜃 𝑛𝐿 𝜃 𝑛𝑅 + 𝜃 𝑛𝐿 2 ∙ 100% 47
- 48. Gap Filling •Interpolation algorithms: •Spline Fill •Pattern Fill •Rigid Body Fill •Kinematic Fill 48