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

Outline Introduction System Architecture Evaluation Conclusion 2

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

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

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

System Overview 6

Outline Introduction System Architecture Evaluation Conclusion 7

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 8

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 9

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 10

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 11

Outline Introduction System Architecture Evaluation Conclusion 12

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 13

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

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

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

Result of User Study – Isolation Type 17

Result of User Study – Compound Type 18

Outline Introduction System Architecture Evaluation Conclusion 19

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 20

Q&A 21