1. University of Wisconsin - Madison Biomedical Engineering Design Courses All information provided by individuals or Design Project Groups during this or subsequent presentations is the property of the researchers presenting this information. In addition, any information provided herein may include results sponsored by and provided to a member company of the Biomedical Engineering Student Design Consortium (SDC). Anyone to whom this information is disclosed: 1)Agrees to use this information solely for purposes related to this review; 2)Agrees not to use this information for any other purpose unless given written approval in advance by the Project Group, the Client / SDC, and the Advisor. 3)Agrees to keep this information in confidence until the relevant parties listed in Part (2) above have evaluated and secured any applicable intellectual property rights in this information. 4)Continued attendance at this presentation constitutes compliance with this agreement. All information provided by individuals or Design Project Groups during this or subsequent presentations is the property of the researchers presenting this information. In addition, any information provided herein may include results sponsored by and provided to a member company of the Biomedical Engineering Student Design Consortium (SDC). Anyone to whom this information is disclosed: 1)Agrees to use this information solely for purposes related to this review; 2)Agrees not to use this information for any other purpose unless given written approval in advance by the Project Group, the Client / SDC, and the Advisor. 3)Agrees to keep this information in confidence until the relevant parties listed in Part (2) above have evaluated and secured any applicable intellectual property rights in this information. 4)Continued attendance at this presentation constitutes compliance with this agreement.

2. GAIT INSTABILITY DETECTOR Team: Ann Sagstetter, Karissa Thoma, Tim Balgemann, Kelvin Ng Client: Dr. Bryan Heiderscheit, Dept of Orthopedics & Rehabilitation Advisor: Professor Paul Thompson

3. Problem StatementProblem Statement Background InformationBackground Information Client’s Needs: The PDSClient’s Needs: The PDS BrainstormingBrainstorming Design MatrixDesign Matrix Current ProgressCurrent Progress Future WorkFuture Work References & QuestionsReferences & Questions Problem StatementProblem Statement Background InformationBackground Information Client’s Needs: The PDSClient’s Needs: The PDS BrainstormingBrainstorming Design MatrixDesign Matrix Current ProgressCurrent Progress Future WorkFuture Work References & QuestionsReferences & Questions PRESENTATION OUTLINE

4. PROBLEM STATEMENT http://www.sciencenews.org/articles/20040221/a4564_1594.jpg http://i.a.cnn.net/cnn/2005/TECH/07/18/ spark.fall/story.elderly.jpg http://www.healthsystem.virginia.edu/uvahealth/ news_DiabetesHealth/images/up_1099.jpg ?

5. BACKGROUND INFO http://www.hanpmr.com/images/gait.gif = http://www1.istockphoto.com/file_thumbview_approve/ 576681/2/istockphoto_576681_caution_stairs_icon.jpg + http://www.pediatric-orthopedics.com/ LookBack_Transp.gif http://www.laboratorium.dist.unige.it/~piero/Teaching/Gait/Malanga%20and%20DeLisa%20Clinical%20Observation_files/mala-f01.jpg

6. BACKGROUND INFO To measure stride/step parameters:To measure stride/step parameters: - 1 biaxial accelerometer on each heel - stride/step duration & length To measure degree of sway:To measure degree of sway: - 1 triaxial accelerometer at S2 level - trunk acceleration To measure stride/step parameters:To measure stride/step parameters: - 1 biaxial accelerometer on each heel - stride/step duration & length To measure degree of sway:To measure degree of sway: - 1 triaxial accelerometer at S2 level - trunk acceleration http://www.sandia.gov/archimedes/examples/accel.gif

7. CLIENT’S NEEDS: THE PDS Data logger (sampling rate ≥ 60 Hz)Data logger (sampling rate ≥ 60 Hz) High resolution in low-g range (± 10g)High resolution in low-g range (± 10g) Data processing at hardware levelData processing at hardware level Adaptable for clinical & home settingsAdaptable for clinical & home settings No movement constraintsNo movement constraints Potential development:Potential development: - self-monitoring device Data logger (sampling rate ≥ 60 Hz)Data logger (sampling rate ≥ 60 Hz) High resolution in low-g range (± 10g)High resolution in low-g range (± 10g) Data processing at hardware levelData processing at hardware level Adaptable for clinical & home settingsAdaptable for clinical & home settings No movement constraintsNo movement constraints Potential development:Potential development: - self-monitoring device

8. BRAINSTORMINGBRAINSTORMING OUR MAIN CONCERNS Type of Data LoggerType of Data Logger Sensor SignalsSensor Signals Memory StorageMemory Storage Attachments to BodyAttachments to Body OUR MAIN CONCERNS Type of Data LoggerType of Data Logger Sensor SignalsSensor Signals Memory StorageMemory Storage Attachments to BodyAttachments to Body

9. MODEL #1 Type of Data LoggerType of Data Logger Commercial (Medical Research Ltd) Sensor SignalsSensor Signals Transmitted through LEMO wires Memory StorageMemory Storage Built-in 512MB memory Attachments to BodyAttachments to Body Customized belt MODEL #1 Type of Data LoggerType of Data Logger Commercial (Medical Research Ltd) Sensor SignalsSensor Signals Transmitted through LEMO wires Memory StorageMemory Storage Built-in 512MB memory Attachments to BodyAttachments to Body Customized belt BRAINSTORMINGBRAINSTORMING http://www.mie-uk.com/datalogger/index.html

10. MODEL #2 Type of Data LoggerType of Data Logger Self-built microcomputer Sensor SignalsSensor Signals Wireless; BlueTooth technology Memory StorageMemory Storage 1GB SD card Attachments to BodyAttachments to Body Sticky pads MODEL #2 Type of Data LoggerType of Data Logger Self-built microcomputer Sensor SignalsSensor Signals Wireless; BlueTooth technology Memory StorageMemory Storage 1GB SD card Attachments to BodyAttachments to Body Sticky pads BRAINSTORMINGBRAINSTORMING http://www.dsl-warehouse.co.uk/images/ wirelessnetworking-bluetooth-main.jpg http://www.kopfweb.de/images/K376.jpg

11. MODEL #3 Type of Data LoggerType of Data Logger NIKE iPod Sensor SignalsSensor Signals Wireless; modified RFID Memory StorageMemory StorageiPod-dependent Attachments to BodyAttachments to Body NIKE runner shoes & iPod band MODEL #3 Type of Data LoggerType of Data Logger NIKE iPod Sensor SignalsSensor Signals Wireless; modified RFID Memory StorageMemory StorageiPod-dependent Attachments to BodyAttachments to Body NIKE runner shoes & iPod band BRAINSTORMINGBRAINSTORMING http://www.sparkfun.com/commerce/ present.php?p=Nike_iPod-Internals

12. DESIGN MATRIX

14. CURRENT PROGRESS Contacted various manufacturersContacted various manufacturers Obtained triaxial accelerometerObtained triaxial accelerometer Now awaitingNow awaiting - biaxial accelerometers - evaluation board - data logger Researched on accelerometry & gaitResearched on accelerometry & gait Sampled data from client’s labSampled data from client’s lab Contacted various manufacturersContacted various manufacturers Obtained triaxial accelerometerObtained triaxial accelerometer Now awaitingNow awaiting - biaxial accelerometers - evaluation board - data logger Researched on accelerometry & gaitResearched on accelerometry & gait Sampled data from client’s labSampled data from client’s lab

15. CURRENT PROGRESS Changes in Acceleration During Normal Gait

16. FUTURE WORK Custom-order appropriate data loggerCustom-order appropriate data logger Build device once components are inBuild device once components are in Test deviceTest device Obtain & analyze dataObtain & analyze data Refine & improve deviceRefine & improve device Custom-order appropriate data loggerCustom-order appropriate data logger Build device once components are inBuild device once components are in Test deviceTest device Obtain & analyze dataObtain & analyze data Refine & improve deviceRefine & improve device

17. REFERENCESREFERENCES Detecting balance deficits in frequent fallers using clinical & quantitative evaluation Tools; Cho & Kamen, 1998 Footswitch system for measurement of temporal parameters of gait; Hausdorff, Ladin & Wei, 1995 Gait variability: methods, modeling, & meaning; Hausdorff, 2005 Assessment of spatio-temporal gait parameters from trunk accelerations during human walking; Zijlstra & Hof, 2003 Increased gait unsteadiness in community-dwelling elderly fallers; Hausdorff, Edelberg, Mitchell, Goldberger & Wei, 1997 Detecting balance deficits in frequent fallers using clinical & quantitative evaluation Tools; Cho & Kamen, 1998 Footswitch system for measurement of temporal parameters of gait; Hausdorff, Ladin & Wei, 1995 Gait variability: methods, modeling, & meaning; Hausdorff, 2005 Assessment of spatio-temporal gait parameters from trunk accelerations during human walking; Zijlstra & Hof, 2003 Increased gait unsteadiness in community-dwelling elderly fallers; Hausdorff, Edelberg, Mitchell, Goldberger & Wei, 1997 Questions???