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

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Presentation on theme: "Forearm Surface Electromyography Activity Detection Noise Detection, Identification and Quantification Signal Enhancement."— Presentation transcript:

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

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

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

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 part of your body Doff every night to charge Takes a while to don the socket every morning Not as dextrous as natural hands - No direct control of fingers

7 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 The iLimb State-of-the-Art Forearm Prostheses

8 The iLimb and iLimb Digits

9 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 Limitations of myoelectric prostheses

10 The Myoelectric Signal

11 Examples of typical sEMG signal

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

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

14 For image analysis Spatiotemporal LBP for video analysis 2-D Local Binary Patterns

15 Take windows of signal Calculate LBP codes within window Form normalised histogram One-Dimensional (1-D) Local Binary Patterns Sample number n x[n]x[n] 0 0 1 1 0 0 2 0 2 1 2 2 2 3 2 4 2 5 = 12 in decimal

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

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

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

19 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 1-D LBP Activity Detection

20 Placement at Carleton University, Ottawa, Canada CleanEMG

21 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 Carleton University’s CleanEMG

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

23 Signal to Quantisation Noise Ratio Signal to ECG Ratio Effective Number of Bits Signal to Motion Artefact Ratio Power line Power (Least Squares Identification) Features

24 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 Why a classifier?

25 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 Work done at Carleton

26 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 Classification accuracy

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


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