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

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

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

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

2 d local binary patterns

2-D Local Binary Patterns Activity Detection

  • For image analysis

  • Spatiotemporal LBP for video analysis

One dimensional 1 d local binary patterns

One-Dimensional (1-D) Local Binary Patterns Activity Detection

  • 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

x Activity Detection[n]

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 Activity Detection

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

1 d lbp bin behaviour1

1-D LBP bin behaviour Activity Detection

  • Test on single gesture of real EMG recording

1 d lbp activity detection1

1-D LBP Activity Detection 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

Carleton university s cleanemg

Carleton University’s Activity DetectionCleanEMG

  • 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 Activity Detection

  • Power line (50Hz or 60Hz)

  • ECG

  • Clipping

  • Quantisation

  • Amplifier saturation


  • Baseline wander

  • RF


Features Activity Detection

  • 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? Activity Detection

  • 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 Activity Detection

  • 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 Activity Detection

  • 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) Activity Detection

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