Forearm surface electromyography
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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.

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Forearm Surface Electromyography

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Forearm surface electromyography

Forearm Surface Electromyography

Activity Detection

Noise Detection, Identification and Quantification

Signal Enhancement

Aim of research

Aim of research

  • Make myoelectric forearm prostheses more useable

  • So far

    • Onset detection

    • Noise reduction



  • Introduction to myoelectric signals, prostheses and control

  • Onset and activity detection

  • Carleton University’s CleanEMG - Noise detection, identification, quantification

  • Signal enhancement

Myoelectric signals and prostheses

Myoelectric signals and prostheses

Forearm prosthesis control

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

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

The ilimb state of the art forearm prostheses

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

The ilimb and ilimb digits

The iLimb andiLimb Digits

Limitations of myoelectric prostheses

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

The myoelectric signal

The Myoelectric Signal

Examples of typical semg signal

Examples of typical sEMG signal

Generic pattern recognition system


raw sEMG signal

(live or recorded)

Generic Pattern Recognition System



Onset/activity detection


Feature extraction


Majority vote

Dimensionality reduction

Class label stream

One dimensional local binary patterns for surface emg activity detection

One-Dimensional Local Binary Patterns for Surface EMG Activity Detection

2 d local binary patterns

2-D Local Binary Patterns

  • For image analysis

  • Spatiotemporal LBP for video analysis

One dimensional 1 d local binary patterns

One-Dimensional (1-D) Local Binary Patterns

  • Take windows of signal

  • Calculate LBP codes within window

  • Form normalised histogram


Sample number


0 0 1 1 0 0

20 21 22 23 24 25

= 12 in decimal

1 d lbp activity detection


1-D LBP Activity Detection

LBP code calculation

1-D LBP histogram calculation

‘Activity’ bins

‘Inactivity’ bins


No activity

Activity bins> Inactivity bins



1 d lbp bin behaviour

1-D LBP Bin Behaviour

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

1 d lbp bin behaviour1

1-D LBP bin behaviour

  • Test on single gesture of real EMG recording

1 d lbp activity detection1

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

Placement at carleton university ottawa canada

Placement at Carleton University, Ottawa, Canada


Carleton university s cleanemg

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

Types of emg noise

Types of EMG noise

  • Power line (50Hz or 60Hz)

  • ECG

  • Clipping

  • Quantisation

  • Amplifier saturation


  • Baseline wander

  • RF



  • Signal to Quantisation Noise Ratio

  • Signal to ECG Ratio

  • Effective Number of Bits

  • Signal to Motion Artefact Ratio

  • Power line Power (Least Squares Identification)





Why a classifier

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

Work done at carleton

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

Classification accuracy

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

Pr system with a new stage

Raw sEMG signal (measured or recorded)

PR system with a new stage



Noise Detection, Identification, Quantification, Mitigation

Data Windowing

Onset Detection

Feature Extraction


Median Filter (Majority Vote)

Dimensionality Reduction

Class label

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