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A Body Sensor Network for Tracking and Monitoring of Functional Arm Motion

A Body Sensor Network for Tracking and Monitoring of Functional Arm Motion. Kim Doang Nguyen I-Ming Chen Zhiqiang Luo Song Huat Yeo Henry Duh School of Mechanical and Aerospace Engineering Nanyang Technology University, Singapore

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A Body Sensor Network for Tracking and Monitoring of Functional Arm Motion

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  1. A Body Sensor Network for Tracking and Monitoring of Functional Arm Motion Kim Doang Nguyen I-Ming Chen Zhiqiang Luo Song Huat Yeo Henry Duh School of Mechanical and Aerospace Engineering Nanyang Technology University, Singapore 2009 IEEE/RSJ Int’l Conf on Intelligent Robots and Systems (IROS) St Louis, MO, USA October 12-14, 2009

  2. Motivation • Objective measurement of motor recovery in patients • Assessment & evaluation of patient’s strength, range of motion, muscular activation patterns in recovery • (Subjective) Measures: impairment (Fugl-Meyer assessment), dailyactivity (Barthel index) • Compact, user-friendly measurement devices • Accurate, easy to wear, ergonomic, hygienic, inexpensive • For clinical and personal uses • Motivating patients for rehab program/sessions with suitable interactive applications

  3. Outlines • Motivations and background • OLE sensing module • Body sensor network for OLE & SmartSuit • Sensor placement and arm kinematic model • Experimental results and statistical tests • Conclusion

  4. Principle of joint flexion angle measurement sensor Opto-mechanical strip encoder in bend guard (soft exo-skeleton) Single axis linear motion (synthesis for multi-dof) Wire Encoder Reflective linear code strip Flexible base structure Optical Linear Encoder Sensor – Joint Flexion Angles 300 lpi encoder = 0.1 deg res. (avg R = 50mm)

  5. Prototype of OLE Sensor • Encoder: AEDR-8400-132 (Avago) • Accelerometer: LIS3LV02DQ (STMicroelectronics) (640Hz) • Microcontroller : dsPIC33FJ32MC204 (Microchip) • CAN controller : MCP2515 (Microchip) Operating voltage: 3.3V Sampling frequency: 15kHz Accuracy: 0.1°flexion angle Operating current: 7 mA Maximum reader speed: 1.5 m/s

  6. Optical Linear Encoder & SmartSuit

  7. Body Sensor Network SmartSuit sensor network for capturing arm motion: three sensor nodes and one data concentrator, connected via CAN bus RF station

  8. Human Arm Kinematic Model 3 OLE + accelerometer to obtain 7-DOF Arm Shoulder (3); Elbow (1); Wrist (2) 1 • At shoulder, θ0=θw0. θw1 from Acc-1 • θ1 calculated coord transf • At elbow, θ2 from OLE-1;θ3 fromOLE-2 2 • Expand trans matrix from world to frame 4 givesθ4 • At wrist,θ5 from OLE-3;θw4 & θw6 from Acc-3 3 • Expand trans matrix from world to frame 6 gives θ6

  9. Smart Suit App Demo

  10. Validation of OLE Sensing Modules To testify working principle of OLE: To appraise performance with rigid links and joints, known joint diameter and center. Jig diameter: 63 mm PowerCube: 0.180/pulse Encoder: 2000 counts/rev

  11. Validation of OLE Sensing Modules • Three sets of measurements • Good repeatability with correlation coefficient: 0.99 • Linearity: 99.2% • Joint diameter computed from data is 62.8 mm, very close to measured diameter of 63 mm

  12. Validation of OLE Sensing Modules • On-body tests: wearing OLE on human against BOPAC’s Goniometer and ShapeWrap from Measurand. • Goniometer measures change of resistance in strain gauges • ShapeWrap measure bend and twist angle of fiber-optic tape via light difference between inlet and outlet

  13. Validation of OLE Sensing Modules • Close relation of OLE’s performance v.s. Goniometer and ShapeWrap. • OLE able to handle high frequency excitation, but better with low frequency excitation • Average RMS error • OLE versus Goniometer is 3.8° with average correlation coefficient of 0.990; • OLE versus ShapeWrap is 3.1° with average correlation coefficient of 0.992. normal flexion of 0.6 Hz fast flexion of 2Hz

  14. Benchmark with VICON • Mean of angle difference μ = -1.835˚ • Standard Deviation of Vicon and OLE : σ = 3.332˚

  15. Validation of SmartSuit system • To examine repeatability and reliability of SmartSuit • To testify SmartSuite as a complete arm motion capture system Experimental procedure adapted from a therapy section of stroke rehabilitation

  16. Validation of SmartSuit system • One data block file contains 10 trials of arm-reaching task • 5 data blocks to produce 5 avg readings for each sensor • Range and SD for each subject computed • average range: 2.819° • average standard deviation: 0.697°

  17. Validation of SmartSuit system ICC describes relative magnitude of two components of the variability. Approximation of ICC in short form: is variability of random errors is variability among their average values computed over each repeated measure As decreases, measurement error explains a decreasing percentage of variance in data, reliability increases, and ICC approaches maximum value of 1. As increases, measurement error explains an increasing percentage of variance in data, reliability decreases, and ICC approaches minimum value of 0.

  18. Validation of SmartSuit system • ICC analysis performed for each sensor using Statistical Package for Social Sciences (SPSS) • Average ICC for each sensor ranged from 0.959 to 0.975 • Overall average: 0.967±0.08 • Average ICC is close to 1.00, indicating high reliability. • High ICC values for all channels showing ability to perform and maintain its functions in routine circumstances, with different biometric subjects. INTRA-CLASS CORRELATION COEFFICIENT OF RELIABILITY

  19. Conclusion • Low cost high performance joint flexion sensor • Patented optical encoding strip technology • Accuracy of ± 0.1º and sampling rate of 1000Hz • Flexible configuration and usages • SmartSuit (Arm) based on OLE + Accelerometer • OLE sensor user validation • SmartSuit user validation • Clinical test bedding & rehab application development

  20. Thank You for Your Attention ! Team Members: A/P Yeo Song Huat A/P Ling Keck Voon Dr. Peter Luo Dr. Zhongqiang Ding Dr. Chee Kian Lim Dr. Yan Liang Funding support: School of MAE, NTU ASTAR SERC ASTAR EHS II Program ASTAR MedTech Program ASTAR – NKTH NRF IDM (MDA) Collaborators: A/P Henry Duh (NUS) Prof T-Y Li (NCCU, TW) Prof M. Ceccarelli (Uni Cassino, IT) Prof G. Stepan (BME, HG) Wei-Ting Yang John Nguyen Kang Li Wei Ni Chao Gu Ke Yen Tee

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