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IPSN 2013 NSLab study group 2013/06/17 Presented by: Yu-Ting

MARS: A Muscle Activity Recognition System Enabling Self-configuring Musculoskeletal Sensor Networks. IPSN 2013 NSLab study group 2013/06/17 Presented by: Yu-Ting. Outline. Introduction System Architecture Evaluation Conclusion. Motivation. Correct motion & prevent injury Non-intrusive

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IPSN 2013 NSLab study group 2013/06/17 Presented by: Yu-Ting

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  1. MARS: A Muscle Activity Recognition System Enabling Self-configuring Musculoskeletal Sensor Networks IPSN 2013 NSLab study group 2013/06/17 Presented by: Yu-Ting

  2. Outline • Introduction • System Architecture • Evaluation • Conclusion

  3. Motivation • Correct motion & prevent injury • Non-intrusive • Scalable (autonomous setup) • Accurate

  4. Disadvantage of Related Works • Vision-based: LOS, clothing & skin cover • Needles: painful, low level activity • Larger sensors with contact gels:low level activity

  5. Sensing of Muscles • Accelerometer • Tremors & oscillations: 3.85 Hz ~ 8.8 Hz • Internal vibration: 10 Hz ~ 40 Hz

  6. System Overview

  7. Outline • Introduction • System Architecture • Evaluation • Conclusion

  8. Sensor Node Network • Provide error detection checksum • Anti-alias filter for the accelerometer • Wired to mobile data aggregator • SPI interface, 1Mbps • 10 Hr for 2200mAh battery

  9. Mobile Data Aggregator • On Yellow Jacket board • Support 6 sensors & 2.5 meters • Receive data from all nodes by TDMA • Decode checksum • Reasons of errors • Damaged sensors • Out of sync nodes • Postpone data sampling until the next cycle • Wi-Fi to backend server

  10. Backend Server – Muscle Activity Recognition • 10Hz high pass filter: avoid signal from tremors • Feature extraction in Matlab using algorithms from WEKA • 6 time domain features • RMS:related to the intensity of an action • Cosine correlation:relation of vibrations at different axes • 15 frequency domain features • Apply DFT (Discrete Fourier Transform) • 3 information entropy of DFT magnitude • 3*4 bands PSD (Power Spectral Density) • N sensors, M=21 • J48 decision tree classifier

  11. Backend Server – Motion Tracking & Visualization • Complimentary filter fusion of sensor data • Obtain accurate orientations of the sensors • By quaternion-based complimentary filter [19,25] • Range of motion limitation • Visualization and rendering • Java & Unity Gaming Engine

  12. Outline • Introduction • System Architecture • Evaluation • Conclusion

  13. Vibration Signature Feature Ranking • Muscle vibrations are directional • Current MARS assume the orientation of sensors doesn't change • Future MARS will try to use polar coordinates

  14. Detection of Muscle Vibration • PSD of accelerometer • Large difference in PSD • PSD is unique for different person

  15. User Study • 4 females & 6 males from different background • Isolated and compound muscles • Compare three classfiers

  16. Precision & Recall • Precision: positive predictive value • Recall: as sensitivity

  17. Result of User Study – Isolation Type

  18. Result of User Study – Compound Type

  19. Outline • Introduction • System Architecture • Evaluation • Conclusion

  20. Conclusion • Pros • Fine-grained muscle activity monitoring • Fast personalized system setup • Sensors can be moved/changed afterwards • Real time processing with visualization • Cons • Not convenient enough to wear the system • Need to be trained individually • The accuracy of the system may still vary with placement

  21. Q&A

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