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
• 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
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 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
• 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
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
Examples of typical sEMG signal
Generic Pattern Recognition
System
Multi-channel
raw sEMG signal
(live or recorded)
Sample
Filter
Onset/activity
detection
Windowing
Majority vote
Classifier
Dimensionality
reduction
Feature
extraction
Class label
stream
One-Dimensional Local Binary
Patterns for Surface EMG Activity
Detection
2-D Local Binary Patterns
• For image analysis
• Spatiotemporal LBP for video analysis
http://www.scholarpedia.org/article/File:LBP.jpg
One-Dimensional (1-D) Local Binary
Patterns
• Take windows of
signal
• Calculate LBP codes
within window
• Form normalised
histogram
x[n]
n
0 0 1
20 21 22
Sample
number
1 0 0
23 24 25
= 12 in decimal
1-D LBP Activity Detection
x[n]
𝑤 𝑗 𝑥[𝑛]
LBP code calculation
1-D LBP histogram calculation
‘Inactivity’ bins
No
activity
NO
‘Activity’ bins
Activity bins>
Inactivity bins
YES
Activity
1-D LBP Bin Behaviour
• Test on a synthetic signal (bandlimited
Gaussian noise with AWGN 6dB)
𝐻𝐵−1
𝐻2 𝑃 2 −1
1-D LBP bin behaviour
• Test on single gesture of real EMG
recording
𝐻𝐵−1
𝐻2 𝑃 2 −1
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
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
• Power line (50Hz or 60Hz)
• ECG
• Clipping
• Quantisation
• Amplifier saturation
Also
• Baseline wander
• RF
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
𝑃60𝐻𝑧
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
• 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
• 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)
Sample
Filter
Noise Detection,
Identification,
Quantification,
Mitigation
Data Windowing
Onset Detection
Median Filter
(Majority Vote)
Class label
Classifier
Dimensionality
Reduction
Feature
Extraction