Thanks to everyone who replied so quickly and thoroughly to my question.

To summarize, most suggested I normalize to percent gait cycle and

average

after the trials after they have been processed,thereby elimating any

problems with sampling rates altogether.

Pam

ORIGINAL POSTING:

---------------------------------------------------------------------------

--

Hello. I am a first year graduate student currently working on software

for analyzing on and off times for EMG signals. I see that this has been

a recent topic on the biomech list, but I have a question that has yet to

be answered. I am collecting signals from 10 different muscles over the

gait cycle, for 5 trials per subject. I would like to present my

resulting on/off signal as an average for the 5 trials. To do this, I

think it is necessary to normalize the lengths of the trials (they are

all different lengths, due to natural variation in the gait cycle) so

that I can average on a point by point basis. However, if I do this,

won't I be changing the sampling frequency and therefore invalidating any

kind of averaging? I have seen kinematic data presented in the

literature as an average of x number of trials

+/- a certain standard deviation and assume the same sort of problem has

been encountered and solved in order to present the data this way. I

would very much appreciate any insight or references to literature that

might help me solve this problem. I will post a summary of replies in a

few days. Thank you.

REPLIES:

---------------------------------------------------------------------------

--

Hi Pam,

Here are three published papers you might find applicable to your

questions:

Computer Algorithms to Characterize Individual Subject EMG Profiles During

Gait - Ross A. Bogey, DO, Lee A. Barnes, MA, Jacquelin Perry, MD Arch Phys

Med Rehabil Vol 73, September 1992, Pages 835-841

A Computer Algorithm for Defining the Group Electromyographic Profile From

Individual Gait Profiles - Ross A. Bogey, Do, Lee A. Barnes, MA, Jacquelin

Perry, MD Arch Phys Med Rehabil Vol 74, March 1993, Pages 286-291

The Rancho EMG analyzer: a computerized system for gait analysis - J.

Perry, E.L. Bontrager, R.A. Bogey, J.K. Gronley, L.A. Barnes J. Biomed.

Eng. 1993, Vol. 15, November 1993, Pages 487-496

-Lee Barnes

---------------------------------------------------------------------------

--

Most either standardize to % gait cycle, or break it up as % stance phase

and

% swing phase.

Gregory Rash

Director, Gait & Biomechanics Lab

Frazier Rehab Center

gsrash01@ulkyvm.louisville.edu

---------------------------------------------------------------------------

--

Pamela:

Isn't it possible to eliminate the problem by presenting your results in

terms of per cent of gait cycle, rather than as specific time

measurement.

In other words, soleus turned on at blank % of gait and off at blank %.

Calculate results for each trial independently, then average across

trials.

Are you using heel strike of the first foot to toe off of the next as one

cycle?.

Good luck

Lou Rosenfeld

---------------------------------------------------------------------------

--

Pamela,

Very quickly (off the top of my head)... Could you not just normalize the

time to pecentage of gait cycle? That would probably be sufficient for on

and off times.

I do not think that you would have to do anything to sampling rates if you

approached it from this angle.

Good luck, feel free to drop me a line to discuss this further.

Steve

Stephen J. Kinzey, Ph.D.

Assistant Professor / Director of Biomechanics Laboratory

The University of Mississippi

Department of ESLM

University, MS 38677

e-mail: skinzey@olemiss.edu

http://www.olemiss.edu/~skinzey/biomch.htm

office: (601) 232 - 5540

fax: (601) 232 - 5525

---------------------------------------------------------------------------

--

Dear Pam:

I did almost the same thing, excpet for the upper limb during reaching

motions. The EMG signals were linear envelope at detected 10 Hz with

Butterworth Filter (Winter 1990, page 280). I then determined the onset

and

end of the EMG relative to the hand kinematics for each trial. To look at

average patterns, I then normalized to each waveform to the duration of

the

movement, which was slightly different from trial to trial. This is

possible based on the work of Shavi and Green (1983) who suggest that

interpolation retains the frequency content of the signal.

1. Gabriel, D.A. (1997). Shoulder and elbow muscle activity in

goal-directed arm movements. Experimental Brain Research, 116, 359-366.

2. Shavi, R. & Green, N. (1983) Ensemble averaging of locomotor

electromyographic patterns using interpolation. Medical & Biological

Engineering & Computing, 21, 573-578.

Best Wishes,

-d.g.

---------------------------------------------------------------------------

--

Since you are interested in activity relative to phase rather than latency

from some zero point, and assuming the conditions of each trial are

sufficiently similar to each other (and your description suggests that

they

are), then it is appropriate to normalize cycle duration for the purposes

of averaging, at least in principle. Another consideration is to ensure

that you obtain an accurate measure of signal amplitude at each point in

time. In the software package that our company produces, one can rectify

and smooth the EMG signals, using either a linear or RMS algorithm prior

to

averaging. The averaging operation employs a linear interpolation

algorithm

to estimate amplitude at percentage intervals across each defined trial.

The number of percentage intervals is user-selectable. Standard deviation

at each point is also calculated. Averaged results can be displayed

graphically, subjected to a burst analysis to determine on and off times

for each muscle, and the results of the average and/or burst analysis can

be exported in the form of ASCII files for further statistical analysis,

if

desired. Let me know if I can be of further assistance.

Sincerely,

RUN Technologies

---------------------------------------------------------------------------

--

Dear Pamela Joy Wise,

You are correct that time normalization will be required.

Typically one normalizes to either 100% of stance or 100% of stride

(stance and swing). A simple linear interpolation algorithm is often

adequate. You must decide how many data points you desire in your

output curves which is dependent upon the time resolution you need in

reporting your on/off EMG data.

Best of luck,

Howard J. Hillstrom, Ph.D.

Director, Gait Study Center

Temple University School of Podiatric Medicine

Eighth and Race St.

Philadelphia, PA 19107

phone: (215) 625-5366

fax: (215) 629-1622

email: hhillstrom@pcpm.edu

---------------------------------------------------------------------------

--

Hi:

Have a look at Banato et al " A statistical method for the measurement of

muscle activation intervals...", IEEE Trans in Biomed Engg March 1998 vol

45 (3), pp 287-299.

Cheers,

Ed Biden

---------------------------------------------------------------------------

--

You could either resample, choosing poitnats pre-defined intervals of your

normalized time frame and interpolating between raw data, using a fitting

technique like Woltring's, or a far better solution in my opinion is to

transform your data into the frequency domain via FFT and average the

harmonic coefficients.

__________________________________________________ _______________

Pamela Joy Wise _/ _/ _/ _/

Biomedical Engineering Graduate Student _/_/ _/_/ _/ _/

Marquette University _/ _/_/ _/ _/ _/

1504 W. Kilbourn Ave. Apt. D _/ _/ _/ _/ _/

Milwaukee, WI 53233 _/ _/ _/ _/

(414)344-6907 _/ _/ _/_/ _/

__________________________________________________ ______________

-------------------------------------------------------------------

To unsubscribe send UNSUBSCRIBE BIOMCH-L to LISTSERV@nic.surfnet.nl

For information and archives: http://www.bme.ccf.org/isb/biomch-l

-------------------------------------------------------------------

To summarize, most suggested I normalize to percent gait cycle and

average

after the trials after they have been processed,thereby elimating any

problems with sampling rates altogether.

Pam

ORIGINAL POSTING:

---------------------------------------------------------------------------

--

Hello. I am a first year graduate student currently working on software

for analyzing on and off times for EMG signals. I see that this has been

a recent topic on the biomech list, but I have a question that has yet to

be answered. I am collecting signals from 10 different muscles over the

gait cycle, for 5 trials per subject. I would like to present my

resulting on/off signal as an average for the 5 trials. To do this, I

think it is necessary to normalize the lengths of the trials (they are

all different lengths, due to natural variation in the gait cycle) so

that I can average on a point by point basis. However, if I do this,

won't I be changing the sampling frequency and therefore invalidating any

kind of averaging? I have seen kinematic data presented in the

literature as an average of x number of trials

+/- a certain standard deviation and assume the same sort of problem has

been encountered and solved in order to present the data this way. I

would very much appreciate any insight or references to literature that

might help me solve this problem. I will post a summary of replies in a

few days. Thank you.

REPLIES:

---------------------------------------------------------------------------

--

Hi Pam,

Here are three published papers you might find applicable to your

questions:

Computer Algorithms to Characterize Individual Subject EMG Profiles During

Gait - Ross A. Bogey, DO, Lee A. Barnes, MA, Jacquelin Perry, MD Arch Phys

Med Rehabil Vol 73, September 1992, Pages 835-841

A Computer Algorithm for Defining the Group Electromyographic Profile From

Individual Gait Profiles - Ross A. Bogey, Do, Lee A. Barnes, MA, Jacquelin

Perry, MD Arch Phys Med Rehabil Vol 74, March 1993, Pages 286-291

The Rancho EMG analyzer: a computerized system for gait analysis - J.

Perry, E.L. Bontrager, R.A. Bogey, J.K. Gronley, L.A. Barnes J. Biomed.

Eng. 1993, Vol. 15, November 1993, Pages 487-496

-Lee Barnes

---------------------------------------------------------------------------

--

Most either standardize to % gait cycle, or break it up as % stance phase

and

% swing phase.

Gregory Rash

Director, Gait & Biomechanics Lab

Frazier Rehab Center

gsrash01@ulkyvm.louisville.edu

---------------------------------------------------------------------------

--

Pamela:

Isn't it possible to eliminate the problem by presenting your results in

terms of per cent of gait cycle, rather than as specific time

measurement.

In other words, soleus turned on at blank % of gait and off at blank %.

Calculate results for each trial independently, then average across

trials.

Are you using heel strike of the first foot to toe off of the next as one

cycle?.

Good luck

Lou Rosenfeld

---------------------------------------------------------------------------

--

Pamela,

Very quickly (off the top of my head)... Could you not just normalize the

time to pecentage of gait cycle? That would probably be sufficient for on

and off times.

I do not think that you would have to do anything to sampling rates if you

approached it from this angle.

Good luck, feel free to drop me a line to discuss this further.

Steve

Stephen J. Kinzey, Ph.D.

Assistant Professor / Director of Biomechanics Laboratory

The University of Mississippi

Department of ESLM

University, MS 38677

e-mail: skinzey@olemiss.edu

http://www.olemiss.edu/~skinzey/biomch.htm

office: (601) 232 - 5540

fax: (601) 232 - 5525

---------------------------------------------------------------------------

--

Dear Pam:

I did almost the same thing, excpet for the upper limb during reaching

motions. The EMG signals were linear envelope at detected 10 Hz with

Butterworth Filter (Winter 1990, page 280). I then determined the onset

and

end of the EMG relative to the hand kinematics for each trial. To look at

average patterns, I then normalized to each waveform to the duration of

the

movement, which was slightly different from trial to trial. This is

possible based on the work of Shavi and Green (1983) who suggest that

interpolation retains the frequency content of the signal.

1. Gabriel, D.A. (1997). Shoulder and elbow muscle activity in

goal-directed arm movements. Experimental Brain Research, 116, 359-366.

2. Shavi, R. & Green, N. (1983) Ensemble averaging of locomotor

electromyographic patterns using interpolation. Medical & Biological

Engineering & Computing, 21, 573-578.

Best Wishes,

-d.g.

---------------------------------------------------------------------------

--

Since you are interested in activity relative to phase rather than latency

from some zero point, and assuming the conditions of each trial are

sufficiently similar to each other (and your description suggests that

they

are), then it is appropriate to normalize cycle duration for the purposes

of averaging, at least in principle. Another consideration is to ensure

that you obtain an accurate measure of signal amplitude at each point in

time. In the software package that our company produces, one can rectify

and smooth the EMG signals, using either a linear or RMS algorithm prior

to

averaging. The averaging operation employs a linear interpolation

algorithm

to estimate amplitude at percentage intervals across each defined trial.

The number of percentage intervals is user-selectable. Standard deviation

at each point is also calculated. Averaged results can be displayed

graphically, subjected to a burst analysis to determine on and off times

for each muscle, and the results of the average and/or burst analysis can

be exported in the form of ASCII files for further statistical analysis,

if

desired. Let me know if I can be of further assistance.

Sincerely,

RUN Technologies

---------------------------------------------------------------------------

--

Dear Pamela Joy Wise,

You are correct that time normalization will be required.

Typically one normalizes to either 100% of stance or 100% of stride

(stance and swing). A simple linear interpolation algorithm is often

adequate. You must decide how many data points you desire in your

output curves which is dependent upon the time resolution you need in

reporting your on/off EMG data.

Best of luck,

Howard J. Hillstrom, Ph.D.

Director, Gait Study Center

Temple University School of Podiatric Medicine

Eighth and Race St.

Philadelphia, PA 19107

phone: (215) 625-5366

fax: (215) 629-1622

email: hhillstrom@pcpm.edu

---------------------------------------------------------------------------

--

Hi:

Have a look at Banato et al " A statistical method for the measurement of

muscle activation intervals...", IEEE Trans in Biomed Engg March 1998 vol

45 (3), pp 287-299.

Cheers,

Ed Biden

---------------------------------------------------------------------------

--

You could either resample, choosing poitnats pre-defined intervals of your

normalized time frame and interpolating between raw data, using a fitting

technique like Woltring's, or a far better solution in my opinion is to

transform your data into the frequency domain via FFT and average the

harmonic coefficients.

__________________________________________________ _______________

Pamela Joy Wise _/ _/ _/ _/

Biomedical Engineering Graduate Student _/_/ _/_/ _/ _/

Marquette University _/ _/_/ _/ _/ _/

1504 W. Kilbourn Ave. Apt. D _/ _/ _/ _/ _/

Milwaukee, WI 53233 _/ _/ _/ _/

(414)344-6907 _/ _/ _/_/ _/

__________________________________________________ ______________

-------------------------------------------------------------------

To unsubscribe send UNSUBSCRIBE BIOMCH-L to LISTSERV@nic.surfnet.nl

For information and archives: http://www.bme.ccf.org/isb/biomch-l

-------------------------------------------------------------------