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Forearm Surface Electromyography

Forearm Surface Electromyography. Activity Detection Noise Detection, Identification and Quantification Signal Enhancement. Aim of research. Make myoelectric forearm prostheses more useable So far Onset detection Noise reduction. Today.

mallory-daniels
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Forearm Surface Electromyography

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  1. Forearm Surface Electromyography Activity Detection Noise Detection, Identification and Quantification Signal Enhancement

  2. Aim of research • Make myoelectric forearm prostheses more useable • So far • Onset detection • Noise reduction

  3. Today • Introduction to myoelectric signals, prostheses and control • Onset and activity detection • Carleton University’s CleanEMG - Noise detection, identification, quantification • Signal enhancement

  4. Myoelectric signals and prostheses

  5. Forearm Prosthesis Control • None (passive) • Realistic looking • Has a few basic uses • Body powered • User shrugs to open and close claw • Proprioception • Limited orientation • Myoelectric • Pick up muscle signals and interpret them into open and close commands • Mostly claw/pincer-type • First commercial limb in 1964

  6. What myoelectric prostheses are not • No sensory feedback • No proprioception • One gesture at a time • Not as dextrous as natural hands • - No direct control of fingers • Not part of your body • Doff every night to charge • Takes a while to don the socket every morning

  7. The iLimbState-of-the-Art Forearm Prostheses • Made by Touch Bionics in Livingston • Individually articulated fingers • Motors stall when ‘enough’ grip has been applied • Monitored by microprocessor • Clever re-use of open/close to allow more gestures • Can ‘pulse’ the motors to increase grip

  8. The iLimb andiLimb Digits

  9. Limitations of myoelectric prostheses • iLimb shares limitations with all modern commercial myoelectric prostheses: • Amplitude-based commands do not directly relate to desired gesture • Not all users can do all ‘double impulse’-type commands • Cannot address individual fingers • Manual thumb rotation for pinch and grip • Limited battery life – a day of normal use

  10. The Myoelectric Signal

  11. Examples of typical sEMG signal

  12. Multi-channel raw sEMG signal (live or recorded) Generic Pattern Recognition System Sample Filter Onset/activity detection Windowing Feature extraction Classifier Majority vote Dimensionality reduction Class label stream

  13. One-Dimensional Local Binary Patterns for Surface EMG Activity Detection

  14. 2-D Local Binary Patterns • For image analysis • Spatiotemporal LBP for video analysis http://www.scholarpedia.org/article/File:LBP.jpg

  15. One-Dimensional (1-D) Local Binary Patterns • Take windows of signal • Calculate LBP codes within window • Form normalised histogram x[n] Sample number n 0 0 1 1 0 0 20 21 22 23 24 25 = 12 in decimal

  16. x[n] 1-D LBP Activity Detection LBP code calculation 1-D LBP histogram calculation ‘Activity’ bins ‘Inactivity’ bins NO No activity Activity bins> Inactivity bins YES Activity

  17. 1-D LBP Bin Behaviour • Test on a synthetic signal (bandlimited Gaussian noise with AWGN 6dB)

  18. 1-D LBP bin behaviour • Test on single gesture of real EMG recording

  19. 1-D LBP Activity Detection • Once activity is detected, pattern recognition can be started • Can sum the LBP codes from multiple channels within a window to get a single decision

  20. Placement at Carleton University, Ottawa, Canada CleanEMG

  21. Carleton University’s CleanEMG • Access to an expert to manually identify and/or mitigate noise is not always possible • EMG can be contaminated with several types of noise • For each type, do some or all of these: • Detect • Identify • Quantify • Mitigate

  22. Types of EMG noise • Power line (50Hz or 60Hz) • ECG • Clipping • Quantisation • Amplifier saturation Also • Baseline wander • RF

  23. Features • Signal to Quantisation Noise Ratio • Signal to ECG Ratio • Effective Number of Bits • Signal to Motion Artefact Ratio • Power line Power (Least Squares Identification) SQNR SNR (ECG) ENOB SMR

  24. Why a classifier? • Contaminants can be mistaken for each other if a single feature type is used • Motion artefact and ECG • Clipping and quantisation • Training a classifier should help to address this

  25. Work done at Carleton • Improved Prof Chan’s and Graham Fraser’s CleanEMG Matlab code • Trained classifiers to identify contaminants using artificially-contaminated real and synthetic EMG • Indicated that detection and identification are harder for signals with higher SNR

  26. Classification accuracy • The techniques lead to improvements in classification accuracy for noisy data • Data Set 1 (Recorded at Strathclyde) – a little, especially Channel 2 • Data Set 2 (Prof Chan’s) – improved • Data Set 3 (Italian) – improvement in some subjects • Classification accuracy is improved for noisy data

  27. Raw sEMG signal (measured or recorded) PR system with a new stage Sample Filter Noise Detection, Identification, Quantification, Mitigation Data Windowing Onset Detection Feature Extraction Classifier Median Filter (Majority Vote) Dimensionality Reduction Class label

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