Electromyography (EMG) Applications
for Rehabilitation and Prosthesis
Dingguo Zhang

Associate Professor

Institute of Robotics
Shanghai Jiao Tong University
Shanghai, ChinaFeb. 26, 2016

Outline
1.

Introduction

2.

EMG for paralyzed patients3.

EMG for amputees

4.

EndingOutline
1.

Introduction

2.

EMG for paralyzed patients3.

EMG for amputees

4.

Ending

Background
Human Motor Control
Electromyography (EMG)

Background
EMG is a kind of Human-Machine Interface (HMI)

Aircraft Control

Computer Device Control

EntertainmentExoskeleton Control

Our Targets

EMG(Electromyography)

EMG(Electromyography)

Rehabilitation

Prosthesis

for Paraplegics

for Amputees

Outline
1.

Introduction

2.

EMG for paralyzed patients3.

EMG for amputees

4.

EndingFES= Functional Electrical Stimulation
An important rehabilitation techniqueControlling electricalpulses of low level tostimulate the skeletal
muscle in an attempt
to restore the motor
function and generate
the desired motions
for paralyzed patients.

Suitable Conditions for EMG+FES
1) The patient is incompletely paralyzed.
He can weakly drive some muscles voluntarily, but
the force is not enough to perform movement, or themovement is abnormal and awkward. Then the weak
EMG that can be interpreted as a desire for a certain
muscle contraction and it is recorded to stimulate thesame muscle positively.

Suitable Conditions for EMG+FES2) EMG from some normal muscles can beused to stimulate the paralyzed muscles (notsymmetric ones) in the ipsilateral side of a limb. For
example, the EMG of proximal muscles of upper
limb is employed to stimulate the distal muscles toperform grasping via FES; the EMG from the wrist
extensor muscles has been used to control thestimulation of the finger and thumb flexors in orderto obtain a stronger tenodesis; the EMG of biceps isused to modulate the stimulation for triceps.

Suitable Conditions for EMG+FES
3)

The patient is hemiplegic. The EMG
information from one side of human limbs canbe used to control the paralyzed symmetricmuscles of the contralateral side via FES.

Case Study 1

Suitable Conditions for EMG+FES
4) Master slave control. The EMG information fromhealthy persons of master side, which remotely
controlled the patients of slave side.

Case Study 2

Case Study 1

EMG in FES rehabilitation system forhemiplegic patients

General Idea
Serve for Hemiplegic Patients!

FES for the paralyzed side

EMG from normal side

Controller
Controller

Hierarchical FES Control System
Our focus: EMG based
Recognition

The current work only realizes the feedforward components(circled by red lines).

EMG Measurement

EMG Electrodes Location in Left Leg.

Six targeted muscles：
rectus femoris, vastus group, biceps femoris,Semimembranosus, gastrocnemius, soleus Mega EMG System (ME6000)

States of Motion in Lower Limb
WalkingStanding
StillStanding
UpSitting
Down

Four types of motions to be classified.

Protocol• Three healthy subjects took part in the EMG measurement.
• Six channels of EMG signals are acquired from six muscle
groups on one leg.
• The subjects perform four types of movements: sitting
down, standing up, stillness (standing quietly and keeping
body balance), and walking in sequence.

• Every type of motion will be performed 60 times. The EMG
signal acquired during the first 30 times will be used astraining data, and that of the last 30 times as testing data.

EMG Processing

EMG Processing

EMG

Segmentation:

Increment

Analysiswindow

100 ms200 msEMG Feature Extraction I
Time Domain Statistics (TDS) Features
Mean Absolute Value
Zero Crossing
Slope Sign Changes

Waveform Length

EMG Feature Extraction II
Fourier Cepstral (FC) Features

ClassificationClassifiers:
Linear Discriminant Analysis (LDA)
Quadratic Discriminant Analysis (QDA)

Note:Fisher linear discriminant (FLD) is adopted to reduce the dimension
before QDA classification in this work.

Results
Average Classification Accuracy of Three Subjects

Results
Average Classification Accuracy of Four Types of Movements

Demonstration

Offline EMG data Driven FES for Walking

Case Study 2EMG in Master-Slave Gesture
Learning System using FESGeneral Idea

Master SideEMG Signal Acquisition and Processing
EMG Armlet

Features: Time Domain Statistics (TDS)Classifier: Support Vector Machine

Slave Side
Functional Electrical Stimulation (FES)

Stimulation Electrode ArrayDemonstration

Master SideSlave Side

Outline
1.

Introduction

2.

EMG for paralyzed patients3.

EMG for amputees

4.

EndingEMG for Amputees
Case Study 3

Handwriting Recognition using EMG

Case Study 4

EMG Controlled Prosthetic Hand

Case Study 3

Handwriting Recognition using EMG

Objective• Allow a person (maybe disabled) to communicate orcontrol the computer and mobile phones without
keyboard or other input devices.
• Provide a natural, low-cost input way for HMI (HumanMachine Interface) based on EMG.

Method
1. Preprocessing
2. Onset and Offset Detecting
3. Template making
4. Template matching

Preprocessing• Notch filter of 50Hz (power frequency), andbandpass filter for 10-200Hz

Blue (removed), Red (left)

Onset and Offset Detecting••

Detecting technique plays an important role forrecognition.
We set the threshold based on the energy andits slope of the EMG signal.

Template Making
Dynamic Time Warping (DTW) algorithm
Conventional Processing

DTW
Minimize the cumulative distance
between points m & nSix-channel
EMGInitialFinal
Template TemplateTemplate MatchingCompared with all the templates by DTW method,
system recognize the character corresponding tothe smallest distance.

Raw EMG

TemplateMatchingResults
Performance on three character sets:• digit characters from ‘0’ to ‘9’• Chinese characters from ‘一’(one) to ‘十’(ten)• capital letters from ‘A’ to ‘Z’Combined with AEVIOUS
Online EMG collectionDTW

Human-Computer Interface

Visible HCI=AEVIOUS Input: Chinese character=>phonetic symbol (Pin Yin)

上6 slide directions
in a hexagon unit

Demonstration

Experiments on an amputee

Continuous Work
• improve EMG acquisition instrument• apply on disabled with hand deficiency• control mobile phone with BluetoothCase Study 4

EMG Controlled Prosthetic Hand

Basic Theory

Block diagram of EMG controlled prosthetic hand

Our Recent WorkEMG Signal Processing based on Pattern Recognition
Algorithm
EMG
Measurement

DelsysMega
Biometrics
SelfdesignedFeatureExtraction

Classification

ProstheticHand

DBS



SELDA



SJT-3



DFC



SEQDA



SJT-4



iDFT



SJT-5



SJT-6

Our EMG Equipments

DelsysBiometrics

Mega

Self-designed EMG armlet

Discriminant Bispectrum (DBS)• N-th order cumulant function of a non-Gaussianstationary random signal x(k):• Bispectrum B(w1,w2) is defined as 2-dimensional
Fourier transform of the 3rd order cumulant function:• Direct estimation of bispectrum of K segments• Estimation of bispectrum in the k-th segment

Bispectrum Integration

1-dimentional
integration＋2-D Bispectrum Matrix is transformed to 1-D integral feature

Fisher linear
discriminant

Dimension Reduction[Chen XP, Zhu XY, Zhang DG, Medical Engineering & Physics, 2010]

Results of DBS

Time Consuming： DBS - 40ms
 AR -28ms
 PSD - 38ms
 TDS - 2ms
 RMS - 0.5ms

51

Fourier Cepstrum (FC)
• Energy spectrum is achieved via discrete Fourier transform• FC coefficientis achieved from the nonlinear magnitude of Fourier-spectrum
transform directly using discrete cosine transform (DCT).

Discriminant Fourier Cepstrum (DFC)
DFC 特征提取的特点:
通过FFT变换直接提取信号的频谱特征采用DCT压缩并去耦频谱信息
借助FFT +DCT快速算法，设计高速特征提取方法
FC coefficients (FFT + DCT):DFC feature

Fisher ratio feature selection:[Chen XP, Zhu XY, Zhang DG, Physiological Measurement, 2009]Results of DFC

Raw EMGTime Cost：
AR -28ms
TDS - 2ms
DFC - 4ms

CommandOutput

54

Less mistakes

Expected Performance
1. At least 8 classes of motions can be recognized.

Versatile functions

2. The recognition accuracy should be above 90%.

Accurate control

3. The computation time of algorithms should lessthan 300ms.

Fast responseAdaptive Classifier
Self-enhancing adaptive classifier
Increasing the training data set
Information in testing data is used to update parameters ofclassifier.
LDA and QDA is improved to self-enhancing classifiers.

[Chen XP, Zhang DG*, Zhu XY, J. NeuroEngineering & Rehabilitation, 2013]

Adaptive Classifiermean vector andcovariance matrix

Label：
Unsupervised

Conventional Classifiers
Linear Discriminant Analysis (LDA)

Quadratic Discriminant Analysis (QDA)

Note:Fisher linear discriminant (FLD) is adopted to reduce the dimension
before QDA classification in this work.

Adaptive Classifier

where z is the new EMG featureProtocols

Conventional(short-term) Protocol

Long-term Protocol

Short-term Performance
Experiment results (unsupervised method)

SEQDA（red） vs QDA（black）
SELDA（yellow） vs LDA（green）

Long-term Performance
Experiment results (unsupervised method)

Prototypes of Prosthetic Hands

SJT-2~SJT-5 Hands

SJT-6 Hand

Experiment on Amputees

Placement of Electrodes

Anterior View

Posterior View

Demonstration

SJT-6 Prosthetic Hand Controlled by
SJT-iMYO EMG Armlet

Acknowledgements
Prof.

Xiangyang Zhu
Dr. Gan Huang

Dr. Xinpu ChenShunchong Li
Jianjun Meng
Lin YaoLizhi Pan
Jiayuan He
Jianwei Liu
Fei XuPeng Yao
Qing Zhang
Weichao Guo
Kaida Chen
Ying Wang
Lei HuaChengzhang Li

Biomechatronics & BioRobotics Lab
Welcome!

http://bbl.sjtu.edu.cn

The END
Thank you for your attention!

Contact: dgzhang@sjtu.edu.cn