Peter Konrad - EMG in Gait Analysis

Noraxon User Meeting 2011

EMG Processing & AnalysisTechniques for GaitPeter Konrad – PhD Sports Science

Lead Software & Application DesignerNoraxon USA, Inc.

Noraxon EMG Workshop 2011

Hosted by:

Dr. Chris PowersMovement Performance InstituteLos Angeles, California


1. New Hardware Aspects

2. Electrode Recommendations

3. Artifact Management

4. Signal Processing Routines

5. Time Normalized Averaging

6. On/Off Pattern Determination

7. Gait Phase Detection

8. Analysis and Interpretation

Topics


1- Hardware


EMG Milestone Telemetry

From: Ines A. Kramers-de Quervain, Edgar Stussi, Alex StacoffGanganalyse beim Gehen und Laufen

1980 1990 Cleaner Signals

Less noise interference

Easier handling


Direct Transmission Systems

Key Benefits:

Very fast/easy handling

No cable fixation

Natural movement

Very helpful for small subjects

Benefits of Latest EMG Technology

2008

Noraxon User Meeting 2011Carlo Frigo, Paolo Crenna, Clinical Biomechanics 24 (2009) p. 239

Benefits of Latest EMG Technology

...even non specialists can easily use EMG


Excellent Artifact Stability

Stable baseline even under heavy impact conditions


Long Distance Transmission

Natural and unlimited movement

Subject on 100 Metersprint parcour


2 - Electrodes


Electrode Recommendation

Typical:

• 1 cm active area

• 2 cm distance

• Parallel to fiber direction

• Wet gel Silver/Silver Chloride

Recommendations given by international research societies

The International Societyof Electrophysiology andKinesiology (ISEK)


Electrode selection relates to the intended use

Single / Dual electrodesAdhesive or Wet GelRe-usable (Beckmann style)Centralized or decentralized snap

Commercial Fine Wire orneedle electrodes

Incontinence probesNumerous styles/manufacturers

2

1

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4

2

1

3

4

Free Selection of Electrode Type

Konrad 2005; ABC of EMG


Top Mounted Transmission Probes

Problems:Probe inertia, restrictions, pressure artifacts

?

(C) BTS – Italy- www.btsbioengineering.com

(C) Delsys – www.delsys.com

(C) BTS - Italy


The connection cable allows for management of pressure areas, skinprolongation, small or thin muscles

Skin prolongation at mm. er.spinae Small muscle areas, e.g. m. interossei

Thin muscle regions (e.g. SCM) Transmission probe is moved away from pressure area

Flexible Use via Mini Cable


Fine Wire - Electrodes

1) Insert Needle 2) Remove Needle 3) Connect wires to springs

Un-isolatedEnding (red)

Steelcannula

Un-isolated Ending (red)- electrode site

Hooked electrodewires



Application Example Fine Wire


Use of Fine Wire

Copied from: Whittle, 2007,page 181

….access to deeper muscle layers


Application Example Fine Wire

Rect.FemSurface

Rect.FemFine Wire

SwitchSole

Data from Rudroff, 2008


Fine Wire vs Surface

Surface RF recording nicely matches with fine wire recording

Rect.FemFine Wire

SwitchSole


Rect.FemSurface


3 - Artifacts


Interfering 50/60 Hz power hum

Seldom appears with telemetry technology



Evaluation via Frequency Spectrum

50/60 Hz Power peaks in total power spectrum:

Only Posthoc Solution: Notch filtering



EMG Baseline Zero Offset

EMG baseline is shiftedaway from zero line:

Oftentimes seen in imported EMG data from motion capture systems!

Solution:10 or 20Hz High Pass Filter


Baseline artifacts

Temporary zero shift(typically motion artifacts)

Solution:10 or 20Hz High Pass Filter

Fine Wire Raw

Fine Wire 20 Hz HP



Cable artifacts

Motion artifact spikes:

Solution:better cable fixation!


4 - Signal Processing


Full Wave Rectification

Full wave rectification

All negative amplitude data are converted to positive data



EMG Signal Processing: Smoothing

Movag at 300 ms

RMS at 300 ms

Moving average (Movag)

Root Mean Square (RMS)

Creates the “linear envelope” or “RMS EMG”



How To Smooth?

Channel Curves Mean, uV

50

100

150

200

250

0

300

69 54 28

Processing, RMS 100, uV MEAN 100 LP 6HZ

0.5 1.0 1.5 2.0 2.50.0 3.0sec

Moving average 100ms

Root Mean Square 100 ms

Butterworth LP 6Hz

Deduction of signal energy based on selected algorithm:


Amplitude Normalization

Amplitude normalization to max. EMG (MVC)

Mic

rovo

lt

% M

VC

MVC

100%

Test Trials

StaticTest

Rescaling of uVto % of reference value



MVC Test Sequences

Compile a set of best candidates for MVC

Rect. Abd.ObliquusRect.Fem.

3 51

10

23 4 2

MVC-TestSequence

MVC - “Hit-Quotes”

Numbers indicate how many out of 10 subjects hit their MVC for a given exercise



Automatic MVC Detection

Highest MVC Window (500ms)within the overall sequence of MVC contractions/exercises



Normalization Data from Perry

Perry 2007, page 33

Data for normal walking -already at 80% MVC?

Inappropriate MVC testscreate unrealistic data in gait trials


Jönhagen, S.; Ericson, M.O.; Nemeth, G.; Eriksson, E.: Amplitude and timing of electromyographic activity during sprinting. Scand. J. Med Sciences Sports 1996, page19

Supra- Maximal EMG

Inappropriate MVC tests create supra-maximal EMG data


Supra- Maximal EMG

Possible Causes:

Subjects are not familar with MVC contraction

Inappropriate arrangement of MVC exercise

Static vs Dynamic condition

Better synchronization of motor units indynamic conditions => more electrical signal superposition


MVC-Test-Positions



MVC-Test-Recommendations

Subjects need some MVC training

Full body support

Always ask for full body tension

Use heavy exercise machines with solid constructions, fixation belts, muscle isolation (single joint tests)

Normalization with patient populations may add moreerror than benefit => change to other analysis designs

Provide external motivation


ISEK Recommendation MVC

Without training, the MVC could be as much as 20-30 % less than thatobtained after appropriate training and lead to incorrect conclusions orinterpretation of data

Estimates of MVC may be performed in different conditions that shouldbe described (e.g. with/without biofeedback, position of the subject, condition of the joint proximal to the one of interest, etc.)

(C) 1999 by International Society of Electrophysiology and Kinesiology


To Mean Normalization

Winter, D. A.: The Biomechanics and Motor Control of Human Gait: Normal, Elderly, and Pathological. Waterloo -Ontario: University of Waterloo Press, 1987

Normalization to the internal mean shows lowest variability in group curves:

Drawback: loss of any amplitudemagnitudeinformation


Impact on Analysis

Curve shape of (averaged) EMG is not altered by normalization

“EMG shapes” describe the behavior of innervations inmotion cycles and do not need MVC

Concentrate on direct comparison designs within muscles(no MVC needed, data are expressed in % difference)

Observe the muscle over the course of treatment and testmodules by using qualitative criteria

Sometimes the MVC can add more error than benefit

Alternatives:


4 - Time NormalizedAveraging


Stride Variability

M. Tibialis Anterior:

Smoothed rectified EMGActivation patterns in gait

Individual EMG patternin gait has highvariability


Averaging of Motion Cycles

Time normalized cycle

0%

Repetitive Movement Cycles in ms =>

100%

A sequence of repetition cycles is averaged in a time normalized window

Mean curve+/- 1 SD range



Time Normalization

Movement Cycle 100%0%

Repetition 1

Repetition 2

Data of each stride are expressed in a vector of 100 data points:



Averaged EMG Curves

Standardized time format allows easy record comparisons



EMG Normative Data by Winter

Gastrocnemius Medialis

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Zeitnormalisierter Zyklus

Mik

rovo

lt

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Tibialis Anterior

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700


Mik

rovo

lt

Glutaeus Maximus

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100


Mik

rovo

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Semitendinosus

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Mik

rovo

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Rectus Femoris

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120

140


Mik

rovo

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Vastus Lateralis

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Mik

rovo

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Vastus Medialis

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Mik

rovo

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Glutaeus Medius

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140


Mik

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Soleus

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Mik

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G

Noraxon User Meeting 2011Copied from: Smidt, G.L. (ed.), Gait in Rehabilitation 1990

EMG Normative Data by Eberhart


MVC Normalized Gait Data

Ericson et al. 1986: Quantified electromyography of lower limb muscles during level walking. Scand. J. Rehab. Med. 18, page 160


5 - On/Off PatternDetermination


Onset Determination

Multiple SD of baseline

% of local peak or MVC

Fixed amplitude value

SD

peak

fixed


Classical Concept: Multiple SD of Baseline

OnsetTime

OffsetTime

Based on e.g. multiple SD of baseline noiseKonrad 2005; ABC of EMG

Noraxon User Meeting 2011LeVeau, B.; Andersson, G. Output Forms: Data Analysis and applications. In : Selected topics in SurfaceElectromyography for use in the occupational Setting: Expert Perspectives, page 70

Pitfalls of Onset determination


M. vastus medialis

M. vastus lateralis

M. rectus femorisM. biceps f.- c. brev.

M. gracilis

M. sartorius

M. biceps f.- c. long.M. semimembranosus

M. semintendinosus

M.vastus intermedius

Knee joint angle(Degree flexion)

60°

40°

20°

Gait cycle % 50 1000 12 31 62 75 87

IC LR MST TST PSW ISW MSW TSW

EMG On/Off Patterns

Redrawn from Perry


Comparison Fine Wire vs Surface

Surface and fine wire activation pattern are nearly identical for rectus femoris:



Rose, S.A.; Ounpuu, S.; DeLuca, P.A: Strategies for the assessment of Pediatric gait in theClinical setting. Phys Ther Vol 71, No. 12 1991

EMG On/Off Patterns

On/Off patterns help to visualize the center activity of muscles in the gait cycle


EMG On/Off Patterns - Conclusions

Data reduction to On/Off suggests true On/Off behavior of muscles which in the real world is not the case

No objective algorithms/methods are found yet that givesstandardized and reliable results in clinical conditions

(1) Shiavi, R. Electromyograhpic Patterns in Normal Adult Locomotion, page 99. In Smidt, L.S. 1990

Citation: “.... . this report and subsequent publications cogently argue that the best method is the ensemble average of the time normalized linear envelope.” (Shiavi (1) )


5 - Gait PhaseDetection


Gait Analysis – Gait Phases

Heelstrike

Toeoff

2. Heel strikeEv

ents

Phases

Stance Phase Swing Phase

InitialContact

LoadingResponse

Pre‐Swing

Initial/Mid/TerminalSwing

Gait Cycle %: 12 62 100

DoubleSupport Single Support Double

Support

Stance Phase Swing Phase

Stance Phase Stance PhaseSwing Phase

Mid/TerminalStance

50

Contralateral Leg


Gait Phase Detection

Footswitches

3D ForcePlate

PressureSoles

PressurePlates

Instrumentedtreadmills

OpticalSystems

ForceSoles

KinematicCalculation

Accelero-meter

Technologies to detect gait events:

Noraxon User Meeting 2011Perry, 2007, page 129

Foot and Pressure Switches

Switch-based signal inconsistencies:


Winter: Shifted Normative Curves

Vastus Medialis

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Time Normalized Cycle

Mic

rovo

lt

Data published by Winter cannot be reproduced with modern gait phase detection technology.

Winter Data

Reproduced data

Time Shift


Mobile Sensor Based Gait Analysis

Inclinometer

Goniometer

Accelerometer

Foot Switch

EMG


3D - Accelerometer

Heelstrikes

Very helpful for fast running and jumping activities

Difficult to detect Toe Off


Foot Switch Based Gait Analysis

Foot Switchor Sole

Multi-modeor multi-stairsignal:each sensorswitches withits own voltageincrement

Difficult to mount and fragile

Many artifacts


Integrated Insole Pressure Distribution

Telemetric Insole System Medilogic: 3D Visualization of Foot Pressure:

Flexible thin insoles Color-coded foot pressure data


Full Contact Area detection

EMG or biomech. sensor recording up to 16 ch.

Synchronized DVVideo up to 50 HZ

Vertical force for left/right side

Pressure prints for left andright side


Based on the ankle velocity computed in the reference frame of the sacrum’s anterior axis

At ground contact foot’s velocity will become negative relative to the sacrum

Similarly, at toe-off the foot’s velocity will be positive relative to the sacrum.

Gait Events via Kinematic Calculation

Left anklevelocity

Left verticalforce

Right anklevelocity

Right verticalforce

Virtual Footswitch event


Optical Foot Switch/Contact System

LED Light beams, appr. 0.5 cm above ground

Distance between beams: 1 cm

Max floor width: 8 meters

Max floor length: 100 meter

Technical Specifications:


Contact Free Virtual Foot Switch

Can be used on treadmill and for floor walking


Long Pressure Plates

Foot Rotation RT Left and Right Overlap

1 calibrated sensor per square cm

Up to 9 meter long

Technical Specifications:

True pressure distribution


Pressure Plate Instrumented Treadmill

Synchronized DVVideo up to 50 HZ

Foot print detection with3D presentation of data

SynchronizedEMG recordings

Resulting verticalforce left/right


3D Force Plate Treadmill

3D Instrumented Dual Belt Treadmill Bertec

EMG

Accelerometer

Inclinometer

3D Forces

3D Moments


Gait Analysis Report

Left-RightComparison

Eventsignal

Gait Phases


6 – Analysis & InterpretationStrategies


Key Strategy For Interpretation -1

Try to create reasonable comparisons:

Pre - test versus Post -test

Focus on changes in EMG parametersafter treatment, intervention, surgery etc…

Activity 1 versus Activity 2

Observe muscle activation in differenttest/treatment conditions

Left side versus Right side

Identify differences between healthy andinjured side



Patient/Subject versus Norm curve

Identification of abnormal patterns

Muscle A versus Muscle B

Coordinative aspects in muscle groups, co-activation, reciprocal firing, symmetry

Test portion 1 versus Portion 2

Time domain changes of parametere.g. fatigue studies

Key Strategy For Interpretation -2

....continued



GaitResearch …aims to improve our understanding of gait

Whittle, 2007,page 177

Operational Background

Clinical GaitAssessment …has the aim of helping individual patients directly

The background defines economical and technical frame

Noraxon User Meeting 2011Whittle, 2007,page 177 – modified text

Clinical Gait Assessment(Whittle)

Gait Assessment Full clinical historyPhysical exam

Hypothesis Formulation Expected cause ofobserved abnormality

Hypothesis Testing Appropriate selectionof analysis tools


Derive Tools for Analytical Questions

Observation of a“problem” or phenomenom

Formulation of hypothesis or expectation

Need for diagnosisor improved understanding

“Translation” to analysis questions

Selection of the right“sensor” / method

Adjustment and finetuning of analysis questions



Analysis Based Questions for EMG

Type of questioning Type of answer Type of scaling

1) Is the muscle active? Yes/No and On/Off Nominal

2) Is the muscle more or less active? Ranking between tests in qualitative terms Ordinal

3) When is the muscle on/off? Onset/Offset calculations, firing orders Metric

4) How much is the muscle active? Expressed in e.g. % MVC Metric

5) Does the muscle fatigue? Slope calculation of EMG parameters Metric

EMG answers these questions:



Clinical Analysis Questions

Is there amplitude symmetry between synergist (VMO/VLO)?

Does the muscle go off when not needed?

Is the timing of firing appropriate/symmetric?

Is there increased co-activation?


Clinical Analysis Questions 2

Does the muscle show a constant firing over the courseof repetitions?

Is the muscle fire phase specific?

Is muscle activation absent or inhibited?


Structure of Clinical EMG Analysis Items

Amplitudecharacteristics

Activityelevated

Activitydiminished

Asymmetriesbetween sides

Left sidegreater thanRight side

Right sidegreater thanleft side

Timing characteristics

Delayed

Premature

Out ofphase

Time domainchanges

Amplitudeincrease

Inconsistencyin amplitude

Inconsistencyin timing

Amplitudedecrease

Coordinationbetween muscles

IncreasedCo-activation

MissingCo-activation

DysfunctionalTiming


Faulty motor programs and skills

Pain sensations or expectations

Tissue damage

Neurophysiological /CNS/peripheral disabilities

Biomechanical problems related to joint & muscles structures

Metabolic problems

Psychological aspects like stress

Dysfunctional EMG: Why ?

The EMG signal itself never tells you the case of dysfunction, possible reasons for a dysfunctional EMG response or behavior are:

Noraxon User Meeting 2011Copied from: Whittle, 2007,page 181

Clinical Decision Making


Clinical Decision Making

Copied from: Whittle, 2007,page 181

Observation Expectation Analysis


Is EMG Needed At All?

Example: A hip extension does not necessarily mean that the main hip extensor (glut max) has the most activation.


The Neuromuscular Level

A complex system of agonist, antagonists, synergists and muscle layers allows several ways of solving motion tasks

The neural “plasticity” in CNS & ascending and descending pathways allows “re-education” of muscle innervations

Muscle Dysfunction is oftentimes not the cause butthe reaction and compensation of underlying problems

Muscle dysfunction may reflect “functional” adjustments


The Neuromuscular Level

“Many gait abormalities are a compensation for some problems experiencedby the patient and, although abnormal, they are nonetheless useful”(page 47)

“One of the interesting things about gait is the way in which the same movement may be achieved in a number of different ways and this particularly applies to the use of muscles, so that two people may walk with the same normal gait pattern but using different combinations of muscles” (page 62)

Whittle, 2009


Visual Inspection of Raw Data

Stroke: Comparison of right (unaffected) vs left (affected) side

Vastus med

Vastus Lat

Glut. Max

Adduktor

Vastus med

Vastus Lat

Glut. Max

Adduktor

Rig

ht u

naffe

cted

400u

VLe

ftaf

fect

ed20

0 uV


Objective Analysis of Muscle Function

Clinical diagnosis before EMG-gait analysis:

Spastic dysfunction of abductors and knee flexors

Red: Patient

Grey: Norm

EMG-Gait analysis reveals:

Prolonged excessive innervation of quadriceps muscles in stand phase

Normal ROM on knee joint

Somewhat normal innervation pattern for hamstrings, increase co-activation in stance

Reduced stance phase

8 time normalized strides at free speed


Suggested Terminology for EMG Patternsby Perry

Adapted from: J. Perry 2003

Addressing time dependent changeswith the EMG activation

Mic

rovo

lt

Premature

Time normalized cycle [%]

Prolonged Continuous Delayed

Curtailed Absent Out of phase

Noraxon User Meeting 2011Adapted from: J. Perry 2003

Addressing amplitude intensityM

icro

volt

Time normalized cycle [%]

Excessive Inadequate Absent

Hyper-active Hypo-active Inhibited

Suggested Terminology for EMG Patternsby Perry

Noraxon User Meeting 2011Perry, 2008. Page 251 – wih modified/adjusted text

Clinical EMG Findings

Continuous Activity Tib.Ant.

Premature activity of soleusat stance phase

Inadequate activation level ofTib.Post. in terminal stance

Prolonged activity of Tib.Ant.at Swing

Premature, inadequateinnervation of soleus in stance

Inadequate/absent innervationof Gastroc. and Tib.Post


Lower EMG on the paretic limb

Prolonged muscle burst duration

Tonic rather than phasic activity at gait transitions

Periods of peak muscle activity that do not coincide with requirements

of a normal gait pattern

Variability of the EMG increases at very slow walking speed

In: Smidt, 1990. Page

Hemiplegic EMG FindingsGiuliani

Typical observations/expectations:

Noraxon User Meeting 2011Davis, R.B. Reflections on the Clinical Gait Analysis, pag 254J. Electomyogr. Kinesiol. Vol 7, No 4, 1997

Literature DataDavis


From EMG Findings to Treatment

Identification of key muscles (“Guilty Muscles”(1))

Uptraining of atrophic, weak, inhibited muscles

Downtraining of overused, hyperactive muscles

Stabilization Training of segmental and joint stabilizer muscles

Training of body awareness

Re-education of faulty motion patterns/programs

Therapeutic consequences based on EMG investigation:

Adjustment of gait aids, insoles, orthosis

Adjustment of medical drug use and dosage

Decision base of surgeries


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