Forearm Surface Electromyography

Activity Detection

Noise Detection, Identification and Quantification

Signal Enhancement

• Make myoelectric forearm prostheses more useable

• So far– Onset detection– Noise reduction

Aim of research

• Introduction to myoelectric signals, prostheses and control

• Onset and activity detection• Carleton University’s CleanEMG - Noise

detection, identification, quantification• Signal enhancement

Today

Myoelectric signals and prostheses

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

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

• 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 iLimbState-of-the-Art Forearm Prostheses

The iLimb andiLimb Digits

• 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

The Myoelectric Signal

Examples of typical sEMG signal

Multi-channelraw sEMG signal(live or recorded)

Sample Filter Windowing

Dimensionality reductionClassifierMajority vote

Class label stream

Feature extraction

Generic Pattern Recognition System

Onset/activity detection

One-Dimensional Local Binary Patterns for Surface EMG Activity Detection

• For image analysis• Spatiotemporal LBP for video analysis

2-D Local Binary Patterns

http://www.scholarpedia.org/article/File:LBP.jpg

• Take windows of signal

• Calculate LBP codes within window

• Form normalised histogram

One-Dimensional (1-D) Local Binary Patterns

Sample number

n

x[n]

0 0 1 1 0 020 21 22 23 24 25

= 12 in decimal

1-D LBP Activity Detection

𝑤 [ 𝑗 ] 𝑥 [𝑛 ]

LBP code calculation

‘Inactivity’ bins

Activity bins> Inactivity bins

YESActivity

NONo

activity

‘Activity’ bins

x[n]

1-D LBP histogram calculation

• Test on a synthetic signal (bandlimited Gaussian noise with AWGN 6dB)

1-D LBP Bin Behaviour

• Test on single gesture of real EMG recording

1-D LBP bin behaviour

• 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

Placement at Carleton University, Ottawa, Canada

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

Carleton University’s CleanEMG

• Power line (50Hz or 60Hz)• ECG• Clipping• Quantisation• Amplifier saturation

Also• Baseline wander• RF

Types of EMG noise

• 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

SQNRSNR (ECG)ENOBSMR

• 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?

• 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

• 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

PR system with a new stageRaw sEMG signal (measured or recorded)

Sample Filter Data Windowing

Dimensionality ReductionClassifierMedian Filter

(Majority Vote)

Class label

Feature Extraction

Onset Detection

Noise Detection, Identification, Quantification,

Mitigation