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Design of a Terrain Detection System for Foot Drop

Design of a Terrain Detection System for Foot Drop. Christopher R. Sullivan Mechanical Engineering October 25 th , 2012. Project Goal. Create an Ankle mounted system for identifying specific ground conditions Today’s talk Background Literature review Pendulum Analysis of Gait

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Design of a Terrain Detection System for Foot Drop

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  1. Design of a Terrain Detection System for Foot Drop Christopher R. Sullivan Mechanical Engineering October 25th, 2012

  2. Project Goal • Create an Ankle mounted system for identifying specific ground conditions • Today’s talk • Background • Literature review • Pendulum Analysis of Gait • Experimental Methods • Terrain Characterization • Pattern Recognition • Conclusions • Future Work Image Source: http://transit-safety.volpe.dot.gov/publications/safety/pedestrian/html/images/dot-tsc-umta-84-36_p0009a.gif

  3. Background • What is Foot drop? • It is a symptom • Inability/ weakness of the ankle Image Source: http://sports.jrank.org/article_images/sports.jrank.org/dorsiflexion.1.jpg

  4. Gait Cycle and Foot Drop Stance Swing Swing Initial Double-Limb Support Terminal Swing Second Double-Limb Support Mid-Swing Single-Limb Stance Initial Swing Periods Terminal Swing Initial Swing Mid-Swing Periods Foot Strike Opposite Toe-Off Opposite Foot Strike Tibia Vertical Foot Clearance Foot Strike Toe-Off Toe-Off % of Cycle Foot Crash Mid Trip Fallen 62% 0% 100% % of Cycle 62% 100%

  5. Ankle Foot Orthotic • Replace lost ankle functionality • Correct brace for the correct problem • Wide variety Dynamic Walk Jointed Brace Solid Brace Image Source: http://www.mobilelimbandbrace.com/images/Articulating_AFO_Overlap.gif http://www.spsco.com/assets/images/dynamic-walk-single-side-2_large.jpg http://proactiveasia.com/web_image/orthotics/Trulife%20semi-solid-afo_web.gif

  6. Stakeholder Interviews • Interviewed Clients, Clinicians,and ProsthetistOrthotist • Clientele Question • Do you have any specific complaints about your AFO? • Do you have any specific compliments about your AFO? • How many AFOs have you had? • How long have any of your AFOs lasted? • What kind of hinges have your past or present AFOs had? • If you could remove material from your AFO, where would you remove it from? Image Source: http://www.humanresourcesdegree.net/images/stories/School%20Logos%20-%20Masters/NazarethCollege.jpg http://www.workforcediversitynetwork.com/images/logos/RGHS_stacked_150.jpg http://www.rochesterorthopedic.com/

  7. Major Interview Takeaways • Foot drop has many other compound symptoms • Pros • Allow clients to walk • Cons • weight/bulk of the AFO • Instability on ramps and stairs • AFO user’s needs can differ widely

  8. Device Basics • Functionality • Provide appropriate support for the foot at the appropriate time • Stairs • Ramp • Level surface • Provide accurate assessment of ground conditions, before the foot hits the ground. • Splitting the project • Brace • Control System

  9. Literature Review • Commercially available AFOs • Experimental AFOs • Human Gait Analysis • Terrain Detection

  10. Commercially Available AFOs • Hard Plastic Brace • No Joint • Jointed • Tamarack • “E-Stim” • Bio-ness • WalkAide Image Source: http://www.orthomedics.us/SiteImages/Bioness%20orange.gif http://www.orthomedics.us/SiteImages/WalkaideCuffGray.gif

  11. Experimental AFOs • I-AFO • Air Muscle AFO • Pneumatic Power Harvesting AFO

  12. Human Gait Analysis • 2-D Modeling of the ankle foot system • Limitations • Equipment • Coordinate Systems • Accelerometers & Gyroscopes Image Source: http://physio.otago.ac.nz/images/clinics/gait2.jpg

  13. Terrain Detection • Robotics • Lasers • GPS • Object Avoidance Image Source: http://www.pbs.org/wgbh/nova/darpa/images/cars-03-stanley-image3-l.jpg

  14. Gaps in Literature • Lack of design possess • Adaptive AFOs

  15. θ3 m2 m3 θ2 θ4 m4 m1 y m5 θ5 x θ1 Pendulum Analysis of Gait • Motivation • Assumptions • Methods • LaGrange's Method

  16. LaGrange's Method LaGrangian operator LaGrangian force Link kinetic energy Link potential energy Resistive Energy

  17. Results Dynamic Analysis of Moment Quasistatic Analysis of Moment

  18. Experimental Method • Experiments? • Devices Used • PMD-1208LS • Sharp GP2Y0A02YK • Piezo electric plate • Sharp GP2D12

  19. Walking Scenarios • Level Walking • Up and down stairs • Up and down ramps • Long walk

  20. Terrain Characterization • Importance? • Fourier Series • Time Shifting • RANSAC • Experiments?

  21. Fourier Series • Harmonics • Period scaling • Importance of order

  22. Order • 4th order selected

  23. Time Shifting • Angle Addition Formula Scaled by Gait Speed (w) Time Shift (t- t0)

  24. RANSAC • RANdom SAmple Consensus • Starting Assumptions • Inliers • Fit to a model • Outliners • Can come from noisy data, or erroneous assumptions Image Source: http://en.wikipedia.org/wiki/RANSAC

  25. RANSAC • Input: • Data • Minimum number of data points • Added guess at the gait period • Number of iterations • Minimum error • Minimum number of points for a real model • Output: • Best model • Best consensus set • Best error

  26. Determining Gait Period Run #1 • Manually Guessing Gait Speed • No Algorithm Used

  27. Determining Gait Period Run #2 • Piezoelectric plate • Identifying the proper gait period • Pick two similar high point and test the rest of the data for similarities • Differentiation between direction change and heel strike Level Walking Gait Period Analysis

  28. When the results are not so pretty Down Stairs Gait Period Analysis

  29. Improvements? • Foot not striking in the location of the plate • Multiple Sensors • Filter

  30. Determining Gait Period Run #3 • Second IR sensor Sharp GP2D12 • Three Methods dealing with noise • Statistical • Logic Loop

  31. Product Inverse Min Step Requirement Data Hz Logic Loop 1/X .125*X While If? While Input Iteration < Size(data) Foot on Ground? Foot on Ground? Yes Yes Start Yes Output No No No Step End Output While Foot Off Ground? Yes Min Step Size Met? No Step Output No Shift Iteration Yes Min Step Size Met? No

  32. Results • Three Experiments • What are we looking to see in each scenario • What does each experiment tell us?

  33. Manual Characterization of Curves • Plausibility • Produced unique data, SD

  34. Characterization of Curves Piezo Electric Plate • Automated modeling • Erroneous Gait speeds • Produced unique coefficients

  35. Characterization of Curves Second IR Sensor • Automated modeling • Better Gait Speed Modeling • Proper Time Shifting • Highly unique Model sets

  36. Pattern Recognition • Experiment Used • Least Min Squares • 60% • Success Rate • Correct Prediction/Number of steps

  37. Level Walking Predictions Level Walking Incorrectly Interrupted as Ramp Up and Ramp Down Data Correctly Predicted as Level Walking

  38. Down Stairs Predictions Down Stairs Incorrectly Interrupted as Ramp Down Data Correctly Predicted as Walking Down Stairs

  39. Up Stairs Prediction Data Correctly Predicted as Walking Up Stairs Up Stairs Incorrectly Interrupted as Ramp Down and Ramp Down

  40. Ramp Down Prediction Data Correctly Predicted as Walking Ramp Down Ramp Down Incorrectly Interrupted as Ramp Up

  41. Ramp Up Predictions Up Ramp Incorrectly Interrupted as Ramp Down and Ramp Down Data Correctly Predicted as Walking Ramp Up

  42. Conclusions • Characterization of Curves • Reliable • Unique • Predicting Ground Types • 80% • Not done yet

  43. Future Work • Producing a truly portable version of the system • Integrating system into an AFO • Adding functionality of tracking long term change of patients’ gait characteristics for clinicians. • Showing a client their progress over time!

  44. Acknowledgments Funding was supplied by the RGHS RIT alliance Seed fund Rochester General Hospital Richard L Barbano, MD, Ph.D., FAAN Advisor Elizabeth A. DeBartolo, Ph.D. Thesis Committee Mario Gomes, Ph.D. Kathleen Lamkin-Kennard, Ph.D. Nazareth College Physical Therapy Clinic J.J. Mowder-Tinney PT, PhD, NCS Rochester Orthopedic Labs Shawn Biehler, CPO

  45. Questions?

  46. Proposed Design • Attaches to the back of existing AFO • Linear actuator shifts carriage to either side • Carriage holds two individually adjustable backstops • The actuator doesn’t have to support the foot • Infrared range finder • Detect terrain

  47. Accelerometers • Require a lot of data analysis • Drift in integration accuracy • Measuring many different things • Most of which I am uninterested in • Most of which is very noise

  48. Functional Block Diagram Multi Surface Sensing Ankle Foot Orthotic Micro-Controller Ground Identification Sensor If Ground Profile Level Ground Range of Motion Stairs or Ramp Position Power Actuator

  49. Distance Detected Level Ground vs. Descending Stairs

  50. Carbon Fiber Brace • Breaks the project up into 2 areas • Bulk reduction in weight and size will help get patients excited about their brace • Spring properties of carbon fiber Figure 7. Carbon Fiber

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