ADAPTATION TO TRANSIENT POSTURAL PERTURBATIONS

FINAL REPORT

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NASA GRANT NAG 9-291

October, 1992 i----

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Submitted by:

Robert O. Andres, Ph.D

Department of Exercise Science and

Department of Industrial Engineering and Operations Research

University of Massachusetts at Amherst

Amherst, MA 01003

Ergonomic Engineering Associates

20 Gulf Rd.

Pelham, MA 01002

(NASA-CR-190959) ADAPTATION TO

TRANSIENT POSTURAL PERTURBATIONS

Final Report (Massachusetts Univ-)

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G3/54 0127025
ADAPTATION TO TRANSIENT POSTURAL PERTURBATIONS

FINAL REPORT

NASA GRANT NAG 9-291

October, 1992

Submitted by:

Robert O. Andres, Ph.D

Department of Exercise Science and

Department of Industrial Engineering and Operations Research

University of Massachusetts at Amherst

Amherst, MA 01003

Ergonomic Engineering Associates

20 Gulf Rd.

Pelham, MA 01002
Robert 0. Andres, Ph.D. I

ACKNOWLEDGMENTS

I would like to thank the many graduate and undergraduate students in

the Department of Exercise Science at the University of Massachusetts who

worked on this project (Nancy Laurie, David Wood, Erick Peterson, Matt Webb,

Ken Holt, David Miller, Dennis O'Connor, Tim Eng, Dawn Gillis, and others). I

would also like to thank the many clerical staff people in the Department who

kept track of the administrative details. Finally, I would like to thank the people

working in the postural area out of the Vestibular Neurophysiology Laboratory

at Johnson Spce Center for supporting our efforts both scientifically and

administratively. Without the help from all of you, we would not be as far along

in understanding our data as we are.
RobertO. Andres,Ph.D. 2

I. INTRODUCTION

Thisresearch was first proposed in May, 1986, to focus on some of the

problems encountered in the analysis of postural responses gathered from

crewmembers. The ultimate driving force behind this line of research was the

desire to treat, predict, or explain "Space Adaptation Syndrome" (SAS) and

hence circumvent any adverse effects of space motion sickness on

crewmember performance. The aim of this project was to develop an easily

implemented analysis of the transient responses to platform translation that can

be elicited with a protocol designed to force sensorimotor reorganization,

utilizing statistically reliable criterion measures.

Thisreport will present: 1) a summary of the activity that took place in

each of the three funded years of the project, 2) discussion of experimental

results and their implications for future research, and 3) a list of presentations

and publications resulting from this project. Most of the figures and the two

tables referred to in the text are placed at the end of the body of the report,

before the appendices.
Robert O. Andres, Ph.D. 3

II. SUMMARY OF YEARLY ACTIVITY ON THE GRANT

Reports were submitted to NASA at the end of the first and the second

year of the grant period. The original award took place in April, 1988, so the first

year period covered 4/1/88 - 3/31/89. The first and second year information

presented here comes directly from the reports submitted:

First Year

GRANT ADMINISTRATION

The starting date for the first year was April 1, 1988. Other dates were

noteworthy: the NASA pastural measurement system was shipped to UMASS

and arrived August 16, 1988, but the LSI-11 system had no terminal for user

interaction. NASA subsequently shipped a VDT system with keyboard, which

arrived September 16, 1988. The other event of note was the execution of a

no-cost extension from April 1, 1989 to June 30, 1989, We received approval

from our institutional Human Subjects Review Board to perform the experiments

in August, 1986, and this approval was renewed for 1988-89,

ACTIVITII_S AND ACCOMPLISHMENTS

Hardware

The activities during the first year of the grant concentrated on efforts to

refurbish the LSI-11 based NASA-JSC pastural measurement system. These

efforts are chronicled first.

Upon arrival of the VDT and terminal in 9/88, the system was powered-up

for the first time. Within fifteen minutes from initial power-up, the dual floppy disk

(RX01) system spontaneously went into a rapid series of seeks, and

proceeded to crash. The controller card was visually inspected and reseated, but the floppy drive would not respond. The entire system was

rendered useless because of the reliance on the disk drives for data

acquisition and stimulus delivery software. After much digging, two discarded

LSI-11 computers were discovered in moth-balls in the Electrical Engineering

Department at UMASS, Through December, 1988 and January, 1989 the
Robert O. Andres, Ph.D. 4

components from the three LSI-11 systems were mixed and matched until a

final hybrid evolved. The hybrid system included double sided floppy drives

(RX02), an LSI-11/23 CPU, and bypassed the SMU controller from the original

system (which was suspect). This hybrid system was used to move the posture

platform and collect data from the digital line scan cameras. Two problems

still remained: 1) we had no means to inspect the data we acquired because

the software previously written for the system relied on Tektronix graphics

capabilities, and our VDT had no graphics capabilities, and 2) we had no

system or applications software to modify any of the existing software for our

purposes. These problems were solved by unearthing RT-11 v. 5.02 and

FORTRAN 77 from various sources on campus, in addition to finding emulator

software for Tektronix graphics terminals to run on IBM compatible PC's. By

February, 1989, we could finally run the NASA/JSC postural measurement

system and view the collected data. At that point in time, we felt that with

some minor alterations to the software to customize our protocols, we were

ready to start the first phase of data collection. Unfortunately, more serious

snags developed.

In the process of running pilot sessions on lab personnel, the

hardware/software system experienced intermittent failures which took the

form of either losing control of the moving platform in the midst of a trial, or

experiencing a hang-up in the software which required system reboot. The

former occurrence occasionally resulted in high speed platform motions to the

microswitched cut-off of the servomotor, an event of extreme danger to both

the subject and the drive screw mechanism. As with the case of software

hang-up, all previous data was lost and the session had to be restarted. Under

many environmental conditions, using protocols of varied length (number of

trials) and waveforms (sinusoidal, transient, and pseudorandom), between the

two types of failures approximately 50% of the data acquisition was flawed.

During this time period (1/89-3/89) we were planning the modifications

needed to the LSI-11 data acquisition programs for our needs, The major

modification was the need to acquire the EMG data at 1000 Hz for four

channels concurrently. The original software acquired the data at 25 Hz, much

too slow for our purposes. The limitations of the double-buffering scheme for

writing data to floppy disk combined with the limited RAM of the LSI-11 (128K)

made the 1000 Hz unattainable. With this realization, we decided to use the LSI11 system for stimulus delivery and digital line scan camera data acquisition
Robert O. Andres, Ph.D. 5

only, and to acquire the EMG data and platform position data with an IBM

compatible where the eventual analysis would be performed, We wanted to

use the digital line scan cameras because they provide a real-time sagittal

plane body silhouette position; unfortunately, they are mated to the LSI-11 bus

and can not be interfaced directly to the PC without new interface cards, We

were developing the software on both the LSI-11 and the PC to establish this

stimulus delivery and data acquisition scheme, when the LSI-11 refused to boot

up (6/89), Between the previous intermittent problems and the final difficulty

booting up the system, I decided to abandon the LSI-11 based approach to

our experiments at the end of the first year, Luckily, we had been developing

software for the PC to analyze our data, so that progress was made on that

front,

The other hardware related activity scheduled for the first year was the

specification and purchase of an additional DC torque motor to add an ankle

rotation degree of freedom to the existing translating platform. The

calculations of required peak torque indicated that an Industrial Drives TT-2033-

1010°A motor that produces 100 oz-in, of torque with a maximum speed of 6000

RPM will be sufficient for our purposes. The motor costs $517, and will be

purchased at the start of the second year,

Software

This next section describes the software developments that took place

throughout the first year, and those that took place at the end of the first year

when the LSI-11 was abandoned, A data analysis package was developed

to process the EMG signals. This package will accept up to 16 channels of

analog EMG data (either raw or RMS), perform post-processing if necessary

(integrate with a given time constant over a given window), and display the

data (either one channel or multiple channels simultaneously), At this point the

latency from the onset of platform motion can be manually digitized under

cursor control or automatically selected and displayed by an algorithm

adapted from previous work in our lab. The onset and duration of muscle

activity are determined this way and stored to disk for each EMG channel. This

software will operate on two different A/D boards: Metrabyte DASH-16 or

Data Translation DT-2801,
Robert O. Andres, Ph.D. 6

Without the LSI-11 system to provide the velocity commands to the

moving platform, the PC was needed for stimulus generation. The DASH-16

board has two channels of D/A, so software has been developed to output

desired velocity commands to the platform. Output from the D/A is

monopolar, so a bias voltage is used downstream to provide bidirectional

control. Unfortunately, we discovered that the maximum sample rate to attain

A/D data concurrently with D/A output under interrupt control was

approximately 200 Hz, even running Quick, Basic 4.0. After an extensive search

of available products (at a reasonable cost) that would allow us to run

concurrent A/D and D/A under interrupt control, none were found. Our solution

to this dilemma is summarized in the section on our finalized data acquisition

plans.

When the use of the digital line scan cameras was ruled out, we began

to develop the software to perform batch analysis of body sway motion

obtained with a video based system (MotionAnalysis System, Santa Rosa,

CA). Our system is a 2-D video processor hosted by a SUN 3-160 Workstation,

and uses two 60 Hz video cameras. Programs have been developed in the

ExpertVision environment to provide sagittal plane coordinates of the fifth

metatarsal head, the calcaneus, the lateral malleolus, the knee, the hip, the

shoulder, the mastoid process, and the elbow. Preliminary experiments

indicate a resolution of 0.1 cm.

One advantage of this increase in the amount of body position data

(from two points with the digital line scan cameras to eight points with the

video) is that the data will be compatible with a dynamic biomechanical

model developed over the past year to predict joint reactive moments and

L5/$1 loading. This will enhance our data analysis and interpretation.

FINALIZED DATA ACQUISITION PLAN

The decisions about the final data acquisition system have been made

and will be implemented in the second year. We will use two 60 Hz video

cameras and our video-processing system to acquire the body sway data.

The laboratory IBM-XT will provide the velocity commands to the moving

platform with the DASH-16 D/A channel output. The Zenith 386 system

purchased to analyze the data will have a DT-2801 A/D board installed, and

will then acquire the platform position and the EMG data at 1200 Hz. The IBM-XT
RobertO. Andres;Ph.D. 7

will also output synchronizing signals to both the video processor and the Z-386

to allow data synchronization.

Subjects for the first phase of the experiments are being recruited from

two large undergraduate Exercise Science courses, with the understanding

that we are looking for a two year availability if possible.

RI_VISE_D TIME-LINE FOR SECOND YEAR ACTIVITIES

Given the delays in establishing a viable data acquisition and stimulus

delivery system, the second year will be more intensive for actual

experimentation. Phase I experiments with males will take place in November

and December, 1989. Phase II experiments with males will be conducted in

February and March, with the intervening time used to analyze Phase I results

and to finalize the software for Phase II data acquisition. Meanwhile, the

females for Phase I experiments will be selected in March, and Phase I

experiments with females will begin at the end of March (after spring break). In

April the software for running the Phase III experiments will be completed so

that Phase III male data can be acquired in May, before the semester ends.

June of 1990 will be spent analyzing acquired data and preparing the second

year report. Manuscripts concerning results from Phase I and Phase II will be in

preparation at this point.

BUDGET ACTIVITY IN THE FIRST YEAR

The budget for the first year went according to plan, with some slight

stretching due to the delay in starting the second year. The equipment budget

still contains about $2600, but this will be spent early in the second year to

purchase the second axis DC torque motor (approximately $600) and the A/D

board and screw terminal for the Zenith 386 (approximately $1500). Many

supplies purchased during the first year will be carried over into the second

year (particularly the EMG electrodes and the computer storage media), so

that the heavy experimental activity of the second year should still remain

within budget.
Robert O. Andres, Ph.D. 8

SUMMARY

First year activity concentrated on the development of a reliable stimulus

delivery and data acquisition system. With the loan of the NASA/JSC postural

measurement system (providing the recent model moving platform and the

servoamplifier to add a second degree of freedom) and existing laboratory

systems (the video-based MotionAnalysis System, the EMG measurement

systems, and the IBM-XT with the DASH-16 A/D and D/A board), a two degree

of freedom, dynamic postural measurement system has been assembled for

essentially the cost of a Zenith 386, a DT-2801 A/D board, and a second DC

torque motor. Actual hardware and software costs to assemble such a system

from scratch would easily exceed $200,000; the system now is truly state of the

art. Second year activity will take advantage of this system to run the

experiments which we hope to use to design a dynamic posture testing

protocol to systematically probe the sensorimotor adaptations to postural

perturbations.

Year 2

GRANT ADMINISTRATION

The starting date for the second year was July 1, 1989. We received

renewed approval from our institutional Human Subjects Review Board to

perform the experiments.

ACTIVITIES ANO ACCOMPLISHMENTS

Hardware

The data acquisition system described in the First Annual Report was fully

implemented in the second year. The block diagram in Figure 1 presents the

final configuration. PC-1 in this figure is the IBM-XT with the Metrabyte DASH-16

A/D and D/A board. D/A channel I drives the platform servo-amplifier, whi_e

D/A channel 2 sends a trigger signal to both the data acquisition board of PC2 and to each of the VCR's (on audio channel 2, at 400 Hz). PC-2 is the Zenith

386 with the Data Translations DT-2801A A/D board. Five channels of data are
RobertO. Andres,Ph.D. 9

acquired at 600 Hzfor 4 seconds: platform potentiometer, and RMS-EMG's

from ankle flexors and extensors and knee flexors and extensors. Data are

saved in packed binary format to conserve disk space. Video records are

transferred from videotape to digitized marker trajectories by the

MotionAnalysis System (Santa Rosa, CA) VP-110 video processor working with a

SUN3-160 based workstation and Expert Vision software, These sagittal plane

marker trajectories are then downloaded to a PC as ASCII files to allow further

analysis and to be synchronized with the muscle activity patterns.

PC-2

EMG amp

& RMS

converter

EMG

Trigger

Video VCRs

processor

Posture platform

Figure 1: Block diagram of data acquisition system.

Software

A data analysis package was developed to process the EMG signals.

This package will accept up to 16 channels of analog EMG data (either raw or

RMS), perform post-processing if necessary (integrate with a given time

constant over a given window), and display the data (either one channel or

multiple channels simultaneously). At this point the latency from the onset of

platform motion can be manually digitized under cursor control or

automatically selected and displayed by an algorithm adapted from
Robert O. Andres, Ph.D. 10

previous work in our lab. The onset and duration of muscle activity are

determined this way and stored to disk for each EMG channel. This software

will operate on data collected by two different A/D boards: Metrabyte DASH16 or Data Translation DT-2801. Another analysis approach that has been

implemented is the calculation of means and standard deviations of RMSEMG"s within particular time windows. The first experiment used three time

windows: before platform motion, during platform motion, and after platform

motion.

Experiment I

The first experiment was completed in the second year. This experiment

represented a combination of Phase I testing for both males and females.

Sixteen subjects (8 male, 8 female) participated in the experiment which

consisted of three days of testing. Subjects were translated backwards at a

peak velocity of 25 cm/sec for either 0.3 s or for 1.5 s. Ten consecutive trials

were acquired for each duration. Subjects also stood either on the level

platform or on a slant board tilted backwards by 10 degrees. Therefore each

session consisted of 40 dynamic trials. The first day was for acclimation

purposes; the order of tilt and duration presentation for each of the two

remaining sessions was balanced for order across the subjects. All trials were

performed with eyes closed, arms folded gently across the chest, and knees

straight but not locked.

Analysis of the EMG data is almost complete, but the video data is still

being processed. Once ASCII files of all the marker trajectories are complete,

this sway data will be analyzed for settling time (at the shoulder) and will also

be used as input to a biomechanical model which predicts joint reactive

forces and moments, particularly at L5/$1.

REVISED TIME-LINI_ FOR THIRD YEAR ACTIVITIES

The second experiment will require rearward rotation of the standing

human, so the first activity will be to add this capability to the platform. The

servo-motor will be purchased and mechanically coupled to the platform (the

motor has been selected, as described in the First Annual Report). The

second experiment will be completed by December, 1990, at which time the
RobertO. Andres,Ph.D. 11

resultsof the first experiment will be in manuscript form. The final 6 months of the

third year will be spent developing the criteria for an adaptive protocol based

on Experiments 1 and 2, with Experiment 3 demonstrating the feasibility of the

approach. At the end of the year specific recommendations for

implementation of the protocol pre- and post-flight will be made.

BUDGET ACTIVITY IN THI_ SE_COND YE_AR

The budget for the second year went according to plan, with no. major

deviations from the proposal to report.

SUMMARY

Second year activity on the grant concentrated on the actual

implementation of the reconfigured data acquisition system, culminating in the

completion of the first major experiment aimed at developing an adaptive

protocol for astronaut screening. Third year activities will complete the stimulus

delivery system by adding rotation to the platform, and then complete the

experiments needed to recommend a new protocol which will assess the

standing human"s capability to reorganize their sensorimotor behavior.

Year 3

GRANT ADMINISTRATION

The activity summarized here under Year 3 actually covers the final year

of grant fundJng and a one year no-cost extension on the project. One item of

note was that not all of the money was spent from the budget; money was left

in to support the construction of the rotating addition to the translating platform

because of a lack of access to facilities that could machine metal to the

required tolerances within our department. To have the work done in another

department, an excessive delay was unavoidable because of staffing

cutbacks at UMASS and departmental priorities, Hence our efforts were

concentrated on the analysis and interpretation of the data we had acquired

with the translating platform.
Robert O, Andres, Ph.D. FRF.CED_NG P,_GE _ ,_:!OT _'L_D 22

Figure 2: CV's for a 0.3 s translation with no tilt

CV

0.45

0.4

0.35

0.3

0.25

0.2

0.15

0.1

0.05

0

Gastrocnemius Tibialis Anterior Hamstrings Quadriceps

• Pre

[] Per

[] Post

Figure 3: CV's for a 0.3 s translation with rearward

tilt

CV

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

Gastrocnemius Tibialis Anterior Hamstrings Quadriceps

• Pre

[] Per

[] Post
Robert O. Andres, Ph.D. 23

Figure 4: CV's for a 1.5 s translation with no tilt

CV

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

/

Gastrocnemius Tibialis Anterior Hamstrings Quadriceps

• Pre

[] Per

[] Post

Figure 5: CV's for a 1.5 s translation with rearward

tilt

CV

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

Gastrocnemius Tibialis Anterior Hamstrings Quadriceps

• Pre

[-]Per

[] Post
RobertO. Andres,Ph.D. 24

Figure 6: Average RMS-EMG's for a 0.3 s translation with no

tilt

MEAN RMS

EMG (%)

8O

70

60

50

40

3O

20

10

0

Gas_ocnemius "l'ibialis Hamstrings

Anterior

• Pre

[] Per

Quadriceps
RobertO. Andres,Ph.D. 25

Figure 7: Average RMS-EMG's for a 0.3 s translation with

rearward tilt

9O

8O

7O

60

MEAN RMS 5o

EMG (%) 40

30

20

10

0

Gastrocnemius Tibialis

Anterior

Hamstrings Quadriceps

[] Pre

Per

[] Post
Robert O. Andres, Ph.D. 26

Figure 8: Average RMS-EMG's for a 1.5 s translation with no

tilt

MEAN RMS

EMG (%)

60

50

40

30

20

10

0

Gastrocnemius Tibialis Hamstrings

Anterior

/

Quadriceps

[] Pre

[] Per

[] Post
Robert O. Andres, Ph.D. 27

Figure 9: Average RMS-EMG's for a 1.5 s translation with

rearward tilt

50

45

4O

35

MEAN RMS 30

25

EMG (%) 20

15

10

5

0

T

Gastrocnemius Tibialis

Anterior

Hamstrings

I

Quadriceps

• Pre

[] Per

[] Post
Robert O. An dres,Ph.D. 28

Figure 10: Pre-translation CV's

0.16

0.14

0.12

0.1

CV o.o6

0.06

0.04

0.02

0

Gastrocnemius

[] 0.3 s, no tilt

[] 0.3 s, tilted

[] 1.5 s, no tilt

[] 1.5 s, tilted

Tibialis Hamstrings Quadriceps

Anterior
Robert O. Andres, Ph.D. 29

Figure 11: Per-translation CV's

CV

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

T

Gas_ocnemius Tibialis

Anterior

Hamstrings Quadriceps

[] 0.3 s, no tilt

[] 0.3 s, tilted

[] 1.5 s, no tilt

[] 1.5 s, tilted
RobertO. Andres,Ph.D. 30

Figure 12: Post-translation CV's

0.9

0.8

0.7

0.6

0.5

CV

0.4

0.3

0.2

0.1

0

Gastrocnemius Tibialis Hamstrings Quadriceps

Anterior

• 0.3 s, no tilt

[] 0.3 s, tilted

[] 1.5 s, no tilt

[] 1.5 s, tilted
Robert O, Andres, Ph.D, 31

Figure 13: Pre.translation Average RMS-EMG's

9O

8O

70

60

MEAN RMS 50

EMG (%) 40

30201 10 b,I 0 n

Gastrocnemi "ribialis Hamstrings Quadriceps

us Anterior

• 0.3 s, no tilt

[] 0.3 s, tilted

[] 1.5 s, no tilt

[] 1.5 s, tilted
RobertO. Andres,Ph.D. 32

Figure 14: Per-translation Average RMS-EMG's

MEAN RMS

EMG (%)

60

5O

4O

3O

20

10

0

Gastrocnemi "ribialis Hamstrings Quadriceps

us Anterior

[] 0.3 s, no tilt

[] 0.3 s, tilted

[] 1.5 s, no tilt

[] 1.5 s, tilted
Robert O. Andres, Ph.D. 33

35

30

25

MEAN RMS 20

EMG (%) 15

10

5

0

Figure 15: Post-translation Average RMS-EMG's

Gastrocnemi Tibialis Hamstrings Quadriceps

us Anterior

• 0.3 s, no tilt

[] 0.3 s, tilted

[] 1.5 s, no tilt

[] 1.5 s, tilted
Robert O. Andres, Ph.D. 34

Window Variable Greater on day:

Pre-translation MEAN G 3

CV TA 2

MEAN TA 3

MEAN H 3

Per-translation MEAN G 3

CV TA 2

MEAN TA 3

CV H 2

MEAN H 3

Post-translation CV G 2

MEAN G

CV TA

MEAN TA

CV H

CV Q

3

2

3

2

3

Table 1: ANOVA analysis results with post hoc tests for significant day effects.

CV=coefficient of variation, MEAN=average RMS-EMG value scaled to

the maximum for that muscle on that day, G=gastrocnemius, TA=tibialis

anterior, H=hamstrlngs, Q=quadriceps.
Robert O, Andres, Ph.D, 35

Window Variable Effects

Gender Duration Tilt

Pre-translation CV G

MEAN G

M>F Y>N

N>Y

CV TA F>M L>S Y>N

MEAN TA F>M L>S

:CV H F>M L>S

_M>F

N>Y

CV Q

MEANH

CV Q F>M L>S

MEAN Q N>Y

Per-translation CV G M>F !Y>N

MEAN G ;>M S>L N>Y

CV TA F>M S>L Y>N

MEAN TA F>M L>S Y>N

CV H VI>F S>L Y>N

MEAN H M>F L>S Y>N

Y>N

MEAN Q F>M

Post-translation CV G M>F

MEAN G

S>L

M>F

S>L N>Y

L>S Y>N

N>Y

CV TA

MEAN TA F>M

MEAN Q M>F

L>S Y>N

Y>N

CV H L>S

MEAN H F>M L>S

CV Q L>S

S>L

Y>N

Table 2: ANOVA analysis results with post hoc tests to indicate significant

gender, tilt, and stimulus duration effects. CV=coefficient of variation,

MEAN=average RMS-EMG value scaled to the maximum for that muscle

on that day, G=gastrocnemius, TA=tibialis anterior, H=hamstrings,

Q=quadriceps, M=male, F=female, L=long duration stimulus (1.5 s),

S=short duration stimulus (0.3 s), Y=tilted platform (10 degrees of

dorsiflexion, and N=no tilt.
Robert O. Andres, Ph.D. 36

APPENDIX A:

Manuscript from the presentation at the Xlth International Symposiom of the

Society for Postural and Gait Research, Portland, OR, May 24-27, 1992.

PRECEDING PPlGE BLANK NOT FILMED

J

IIII
IO

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Robert O. Andres, Ph.D. 37

APPENDIX B:

Table B-I' The calculation of intraclass correlation coefficients.
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Robert O. Andres, Ph.D. 38

APPENDIX C:

Manuscript from the Journal of Biomechanics that presents the dynamic

biomechanical model that has been modified for this project.
J. Oiom_kanws Vol. 24, No. II, pp. 1033-1045, 1991. 0021-9290/91 S3.00+.00

Pnnted in Great Britain C) 1991 Perpmon Preu pie

VALIDATION OF A BIODYNAMIC MODEL OF PUSHING

AND PULLING

! / '

ROBERT O. ANDRES* and DON B. CHAFFINt

* Department of Exercise Science, 21 Totman Building, University of Massachusetts, Amherst,

MA 01003, U.S.A. and tCenter for Ergonomics, The University of Michigan, Ann Arbor, Michigan,

U.S.A.

Almract--Pnshing and pulling during manual material handfing can increase the compressive forces on the

lumbar disc region while creating high shear forces at the shoe-floor interface. A sagittal plane dynamic

model derived from previous biomechanical models was developed to predict L5/$1 compressive force and

required coefficients of friction during dynamic cart pushing and pulling. Before these predictions could be

interpreted, however, it was necessary to validate model predictions against independently measured values

of comparable quantities. This experiment used subjects of disparate stature and body mau, while task

factors such as cart resistance and walking speed were varied. Predicted ground reaction forces were

compared with throe measured by a force platform, with correlations up to 0.67. Predicted erector spinae

and rectus abdominus muscle forces were compared with muscle forces derived from RMS.EMGs of the

respective muscle grouln, using a static force build-up regreuion relationship to transform the dynamic

RMS-EMGs to trunk muscle forces. Although correlations were low, this was attributed in part to the use of

surface EMG on subjects of widely varied body mass. The biodynamic model holds promise as a tool for

analysis of actual industrial pushing and pulling tasks, when carefully applied.

IN'rBODUCTION

Carts of various sizes, weights and configurations are

frequently pushed or pulled manually in many industrial situations. Among these are the tyre manufacturing industry, the fiberglass manufacturing industry,

commercial laundries, and the airline industries. A

large proportion of these tasks involve the worker

pulling on doors and hoses, pushing on carts, and

generally attempting a task which imparts a high

shearing force to the feet, with resulting slip and fall

injuries (Safety Sciences, 1977). It is estimated that

over 20*/. of the worker's compensation costs each

year are a result of fall or slip related injuries in the

U.S. (Szymusiac and Ryan, 1982). A study concerning

a large manufacturing operation in England reported

that 36--45% of back pain was caused by a slip or fall

(Manning, 1983). These statistics paint a dangerous

picture for workers involved with pushing or pulling

tasks, which increase the risks of slipping and falling or

overexerting the back.

Several investigators have addressed the performance aspects of push/pull tasks. Dempster (1958)

studied static pull forces during standing. Kroemer

measured maximal isometric pushing forces in 65

different positions and assessed the effects of varied

foot friction during pushing (Kroemer, 1969, 1971).

Ayoub and McDaniel (197,1) studied the loading of the

lumbar spine during static pushes and pulls against a

wall, in various body postures. One Swedish study

(Winkel, 1983) concerned the manual handling of food

and beverage carts on wide-body airplanes and measured only the hand forces exerted on stationary carts.

Several recommendations were made about cart conReceived in final form 25 March 1991.

figuration and loading as a result of this study. However, none of these studies considered the dynamic

case where the worker moves during the push or pull

task.

Pushing and pulling hand forces have been measured while the subject walked on a treadmill (Snook et

al., 1970) with different handle heights and adjustable

treadmill resistance. Strindberg and Peterson (1972)

used psychophysical methods to study force perception while pushing trolleys. These studies began to

approach more realistic dynamic simulations of actual

industrial situations. A German group studied the

load on the spine during the transport of dustbins

(Jager et al., 1984). These authors utilized a simple

static model of L5/S1 torques, and they also measured

the EMG activity of back, leg, and hand muscles.

However, no validation of their model was offered,

and the EMG information for the back muscles was

not compared with the L5/SI torque predictions.

The doctoral research of Lee (Lee, 1982; Lee et al.,

1989) formulated a dynamic biomechanical model of

cart pushing and pulling. The. inputs to the model

included subject anthropometry, body postures during dynamic tasks, and hand forces exerted on the cart

handle. The specific model predictions were horizontal and vertical foot forces and gross torso muscle and

vertebral column loadings when pushing or pulling.

Laboratory validation of the model took place with a

cart simulator. Dynamic foot forces, hand forces required to move the cart, body motions at various

speeds, and back muscle actions were measured while

six subjects pushed or pulled the cart simulator. Foot

force predictions were compared to measured (by

force platform) foot forces, while predicted torso

muscle forces were converted to 'equivalent' integrated electromyograms (IEMGs) and then compared

1033
1034 R.O. ANDRES and D. B. CHAFFIN

with the measured IEMGs. These validation experiments were performed at three handle heights (66, 109,

152 cm) and three cart resistances (light, medium,

heavy). Two speeds (approximately 1.8 and

3.6kmh -I) were used at only the middle handle

height. The four male and two female subjects ranged

in stature from 161 to 175 cm, and the body mass

range was 50-80 kg.

Lee et al. (1989) reported the results of static calibrations performed for the dynamic experiment, but the

dynamic results have not been published. The purpose

of our investigation was to validate a model (based on

Lee's model) in the laboratory with a broader anthropometric population.

METHODS

Model development

The biomechanical model used for this study was

based on that used by Lee (1982). The mathematical

relationships and assumptions which allow the prediction of the desired outputs (foot reactive forces and

LS/SI compressive forces) will be described. There

were several intermediate steps before the predictions

were made; these will also be examined.

Two different sets of link lengths were calculated.

The distance between the joint markers (LEDs), in the

sagittal plane, was measured by the position detection

system. The Y and Z coordinates were calibrated first,

corrected for distortion, and smoothed with a digital

filter with optimal cut-off frequencies [determined for

each data record as described by Jackson (1979)]

before calculating relative link lengths (link lengths in

the sagittai plane, possibly foreshortened if the link

was out of plane). Absolute link lengths were based on

ratios to total stature (Drillis and Contini, 1966).

The position of L5/S 1 was calculated because there

was no marker on that location. These calculations

were based on Chaffin and Andersson (1984, Chap. 6).

The link center of gravity (CG) positions were calculated with respect to relative link length. Ratios based

on Dempster (1955) were used.

Link masses were calculated as a ratio to total body

mass. A major assumption made at this point was that

the left arm position was the same as the right arm, as

viewed from the sagittal plane. When markers were

placed on the inside of the left arm, they were never

detected by the position detection system because they

were blocked by the right arm. Therefore, the masses

of the forearm and hand link and the upper limb link

were doubled to represent both arms acting in the

same sagittal plane position. The ratios were again

from Dempster (1955).

Joint angles were calculated from joint marker

coordinates with respect to the horizontal. The joint

angles correspond to the angle from the horizontal of

respective links (i.e. joint angle No. 1 was the angle

from the horizontal of link No. 1, the forearm and

hand; see Fig. 1).

BW

Hy

The whole body CG position was calculated using

the segmental moments method (Miller and Nelson,

1976). The location of the upper body CG was calculated with the same approach using only links above

L5/SI (hands, forearms, upper arms, and trunk from

shoulder to LS/SI).

Mass moments of inertia were calculated for each

link as the product of the link mass and K 2, where K

was the product of relative link length and a constant

representing the radius of gyration (Chandler et al.,

1975; Plagenhoef, 1966).

Link CG, whole body CG, upper body CG, and

joint angle data were differentiated twice so that the

respective accelerations could be used for calculation

of inertial forces. The derivatives were calculated with

a finite-impulse-response digital recursive filter (Oppenheim and Schafer, 1975; Lanshammar, 1982). Optimal filter coefficients were derived for these data and

subsequently applied uniformly.

The linear and rotational inertial forces resist body

linear and angular accelerations. These inertial forces

were calculated for each link as the negative of the

product of the link mass and the linear acceleration in

Y and Z directions. Whole body inertial forces acting

at the whole body CG were calculated as the negative

of the product of the whole body mass and the Yand Z

accelerations of the whole body CG. Since there was

no overall body angular acceleration, the whole body

rotational inertial force was calculated as the sum of

the individual link rotational inertial forces.

LF t RF, t

L_ e_

,*-.-DFv _

• oCO_ ,,

Fig. 1. Free body diagram of double support stance with

joint angles used by the model and locations of joint center

markers. Variables and resulting equilibrium equations are

given in the text.
Abiodynamic model of pushing and pulling 1035

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ion

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Calculation of ground reaction forces at the feet

Two different situations exist during normal walking gait: a single support phase, when only one foot

contacts the support surface while the other foot

swings through to its next placement; and a double

support phase, when both feet are in contact with the

support surface (see Fig. I). Foot force calculations are

detailed below for each case.

Single support. Assume:

(I) two arms act as one (sum Y and Z forces from

the separate handles);

(2) quasi-static equilibrium,

F.F_=O=RFy-H,-B.

R F,= Hy + By,

F.F,=O=R F,-H:-BW-B.,

RF,=H.+BW+B,,

where R F, is the Y reactive force of the right foot; R F,

is the Z reactive force of the right foot; H, is the total Y

hand force; II. is thc total Z h;md force; B W is Ihe

Iol{tl body weight; ll, is Ihc Y body inertial force; and

B, is the Z body inertial force.

Moments at the foot during single support were not

used by the model and were not needed because there

were only two unknowns with two equations.

Double support. Assume:

(1) quasi-static equilibrium;

(2) two arms act as one;

(3) moment arms from heel marker for pushing,

from toe marker of re, r fool forpiillinR.

Y_F,=O= L F, + R F,- H,- B,.

E.F.=O=LF.+RF:-H.-B.-BW.

Left foot back.

_Mx--0= -R F, ,(D F,)+(B:+ BW)* DCG,

- By * (DCG:) - Hy * (DH:)

+ H, * (DHy) + Br

= DCG, * (B, + B 14/)+ DH, * (H,) + B,

-- D F, , (R F,)- DCG. . (B,)

- DH, • (Hy),

RF. = (DCG, * (B. + B IV) + DH, • (H.) + B,

- DCG. * (B,)- DH. • (H,))/D F.

LF.= H. + B. + BW- R F..

where L Fy is the Y reactive force of the left foot; L F, is

the Z reactive force of the left foot; DCG, is the Y

distance from the rear heel to the whole body center of

gravity; D F, is the Ydistance from the rear heel to the

Z foot force of other foot; DH, is the Y distance from

the rear heel to the handle; DCG, is the Z distance

from the floor to the whole body center of gravity; DH:

is the Z distance from the floor to the handle; and B, is

the rotational body inertial force.

Similar equations result when the right foot is back

for the other portion of double support during right

foot stance, with appropriate exchange of right and left

foot reactive forces. At this stage, the system is indeterminate because we have three equations (M,, F, and

F,) and four unknowns (LF,, RF,, LF, and R F,). The

solution requires another equation to become determinate; the model assumed that the friction utilization

under both feet was the same, and hence the ratios of

horizontal (y) to vertical (z) foot forces at each foot

were equal (Lee, 1982).

Calculation of trunk muscle forces

The calculation of L5/S 1 compressive fot;ces and the

muscle forces contributing to these forces begins with

the calculation of abdominal pressure, because this

pressure counteracts some of the contraction force of

the erector spinae muscles (see Fig. 2). An empirical

prediction of abdominal pressure was performed using

previously reported equations (Lee et aL, 1989; Chaffin

and Andersson, 1984) derived from work done by

Morris et al. (106l).

The momcnl arm a! which I"A. . acl_ ha_ hecn

assumed by Chaflin (1975) to vary as Ihc sine of hip

angle, with an erect position having a moment arm of

7 crn, increasing to about 15 cm when stooped over at

4>, = 90° from vertical (where _, = the angle from the

hip-to-shoulder link to vertical). The argument that

F4e _ acts parallel to the compressive force on L5/S !

was presented by Chaflin and Andersson (1984). The

line of action of rectus abdominus has also been

parallel to the compressive force on L5/SI in other

studie,_ (_hullz trod Andersstm, lOgl', (.'hailh_ and

Andersson, 1984). This model assumes that all muscle

forces act normal to the shear force to create compression only. Reactive shearing forces are then produced by lumbar facet joints, as described in Chaffin

and Andersson (1984).

The following equations were used by the model to

calculate back and muscle forces (see Fig. 2):

Fc = ESMF + RAMF-- FAst_

+ sin (r,). (B HI. + H, + U B,)

+ cos (_) * (U B, + H,),

F s = cos (_) * (B 14,'.+ H, + U B,) + sin (*,) * (U By + Hy),

where Fc is the L5/SI compressive force; Fs is the

L5/SI shear force due to external forces only (assuming that all muscles act to create compression only);

ESMF is the erector spinae muscle force (when resultant moment at L5/SI was negative), calculated as

the resultant moment at L5/SI divided by the moment

arm [0.06 m for males and females, based on Kumar

(1988)]; RAMF is the rectus abdominus muscle force

(when resultant L5/SI moment was positive), calculated as the resultant moment divided by the moment

arm [0.10m for males and females, from Kumar

(1988)']; F_so is the abdominal force due to intraabdominal pressure; a is the angle from horizontal to

L5/Sl-shoulder link; BW, is the body weight above

OF POf-_R " '_ "'rv
1036 R.O. ANDRES and D. B. CHAFFZ_

ESM V

Fig. 2. Free body diagram of the body above the hips, used

to calculate L5/SI shear and compressive forces and erector

spinae and rectus abdominus muscle forces. All trunk foroes

are acting parallel or normal to the compressive force. (Note:

it is assumed these muscles act to provide only compression

forces on the spinal discs.)

L5/S1; UB, is the upper body Z inertial force; and UBy

is the upper body Y inertial force.

Static calibration experiments

Linear regressions were formed between the predicted torso muscle forces (erector spinae muscle forces

[ESMFs] and rectus abdominus muscle forces

[RAMFs]) and the measured RMS-EMGs from both

the right and the left groups of these muscles during a

gradual increase of push or pull forces. The subject

either pushed or pulled in a free static posture. Starting

with no exerted hand force, the subject built up to a

near maximum push or pull force in a 5 s period. The

regression coefficients were used subsequently to

transform dynamic RMS-EMG values to muscle force

values to provide a basis for comparison to predicted

dynamic muscle force values.

Experimental design and statistical analysis

A fractional factorial design was used to examine

the accuracy and precision of the model predictions as

a function of the independent variables, described

below. In particular, 20 subjects were selected according to a design which emphasized anthropometric

extremes ( < 20th percentile, > 80th percentile for both

stature and body weight) and included subjects of 50th

percentile stature and weight, with two males and two

females in each of the five cells (see Table 1). Healthy

young subjects (18-31 yr) volunteered for this experiment. After signing an informed consent form, consistent with University policy, they were weighed and

their heights were measured.

Model validation involved three types of analyses:

(i) correlations of measured vs predicted variables;

(ii) ANOVA analysis of measured and predicted variables; and (iii) ANOVA analysis of residuals formed by

taking the difference between measured and predicted

values. Scheffe multiple comparison tests were performed post hoc, when appropriate.

Linear statistical models were formed for the

ANOVA analysis with the following factors (and

corresponding levels): subject (n=20), sex (male, female), height (short, average, tall), weight (light, average, heavy), cart resistance (low, high), walking speed

(60, 100 stepsmin-t), and direction of resistance

(push, pull). These factors will be abbreviated SN, SX,

HT, WT, CR, WS, and DR, respectively. Only the

main effects are presented in this report; first order

interactions were not significant.

Table 1. Subject stature and weight descriptions for experimental design to emphasize

anthropometric extremes

Weight (percentile)

<20 50 >80

Males

Height (percentile)

Females

Height (percentile)

<20 0.170 m -- !.75m

626.0 N -- 885.5 N

50 -- 1.78 m --

-- 712.0 N --

> 80 1.85 m -- 1.84 m

689.0 N -- 878.0 N

<20 1.54 m -- 1.58 m

438.0 N 845.0 N

50 -- 1.62.m --

-- 572.5 N --

>80 1.72 m -- 1.76 m

569.5 N -- 689.0 N

Stature and weight percentiles were derived from National Health Survey (1965). There

were two subjects in each cell, so only the mean values are given.
Abiodynamic model of pushing and pulling 1037

Analytical methods

The differences between independently measured or

derived variables and the same variables predicted by

the biodynamic model provided the most critical

assessment of model performance. Beyond visual comparisons, one technique to assess differences between

predicted and measured quantities was to compare the

mean values of the predicted and measured dependent

variables for each of the two time-windows (single and

total support period for the right leg).

Another method of comparing measured and predicted quantities involved the formation of residuals.

The GRF residuals were calculated on a point by

point basis throughout the support phase by summing

the model predicted force exerted on the foot by the

ground and the measured force exerted on the ground

by the foot. This was equivalent to subtracting measured values of force exerted on the foot by the ground

from the model predicted forces. These residuals were

actually intra-subject comparisons. Interpretation of

these results requires some explanation: the average

represents the sum of the differences between predicted and measured values divided by the number of

differences taken. These residual parameters were

submitted subsequently to ANOVA analysis to determine which subject and task factors contributed significantly to the errors in the predictions.

Data acquisition hardware

The cart simulator had handles 0.5 m apart and

oriented so that they were horizontal; hence the

subject gripped each handle with the hands prone. The

simulator travelled on teflon bushings over aluminum

rails, while the cart resistance came from a strap which

passed over a variable number of dowels which were

affixed to the bottom of the simulator. By changing the

tension on this strap, cart resistance varied from 88 to

128 N (the horizontal force necessary to keep the cart

moving at approximately 0.5 m s- _). The electronics

for the portable handles and the EMGs were carried

by the cart (see Fig. 3).

*Z

I

+V _MULATOR

ADJUSTABLE II \\\\\

HEIGHt" HANOLE \\tX' 

L_'DCONTROL_ __--"I. 1\\ \\

\\\

J r3--x.\\

J I

i SLIDE RAIL

BUSHING

k. I°'_'_';" I leT._'iff.'_ IF- --7 -A_XA-p,otO

"..J I I _ j_ .._ o,"r,'c'ro, c-,,'s,

WALKWAY_.

ii

Fig. 3. Diagram of laboratory equipment configuration and the coordinate system used for kinematics and

forces exerted on the body.

BM 24:11-E
I038 R.O. ANI)KESand D. B.CH^_'IN

Approximated sagittal plane joint center coordinates were sensed by a single camera SELSPOT system

(Seicom Selective Electronic Inc., Vaidese, North Carolina, U.S.A.) sampling at 50 Hz. The 10 LEDs were

placed on the subject as shown in Fig. 1. To prevent

distortion the following conditions were placed on

LED position detection: (1) the data window was in

the central portion of the camera viewing field; (2) lens

distortion was compensated for by calibration; and {3)

reflection problems were minimized by keeping the

reflective laboratory floor out of the viewing window.

Hand forces were measured with portable handles

which used columns instrumented with strain gauges

to detect the horizontal and vertical components of

force. The force platform was a Kistler force platform

(Kistler lnstrumente AG, Winterthur, Switzerland,

model 9231A) with six Kistler charge amplifiers (5001)

and a Kistler central control unit (5671A). The surface

of the force platform was covered with painted plywood, as was the rest of the runway. The static

coefficient of friction(COF) of the surface when rubber

shoes were worn was approximately 0.7, while the

dynamic COF was about 0.6 (see Andres et al., 1984).

The myoelectrical activity of the torso muscles was

detected by bipolar surface electrodes (Hewlett Packard, Andover, Massachusetts, U.S.A., model 14445C

disposable electrodes). The electrodes were placed

3 cm lateral to the midline on both sides of the spine at

the L2 and L3 level, about 5 cm apart so that the

activity of one side vs the other could be observed. The

rectus abdominus activity was recorded by placing the

electrodes in a similar manner on the abdomen (centered 3 cm lateral to the linea alba, above the navel).

The electrode signals were sent to preamplifiers in

a small box attached to the subject's belt (input

impedance=109f,_, common mode rejection ratio

> 120 db), then to a custom amplifier which converted the raw EMGs to derived RMS values (with a

time constant of 55 ms) for the four channels of information (left and fight erector spinae, and left and

right rectus abdominus).

RESULTS

Figure 4 displays an example of measured vs model

predicted results. Quantitative results follow from

correlation, ANOVA, and residual analyses.

Correlation analysis of measured vs model predicted

variables

Both foot force and trunk force variables were

compared by forming a simple linear regression between measured and predicted values. Comparisons

were made for the average values of the variables over

both single and total right leg support phases of the

gait cycle (see Table 2). Notice that the vertical foot

force during single support had the slope parameter

closest to unity. However, for total right leg support

the predicted average vertical foot force did not correlate nearly as well with its measured counterpart. This

implies that predictions during double support (that

portion of the total right leg support period excluding

single support) were not as valid as those during single

support.

Another analysis of the relationships depicts the

grand mean of the average value of the variable during

single support by subject body weight category for

each direction of exertion. The greatest discrepancy

emerged for the horizontal GRFs during pushing,

with consistent model overprediction. Model overprediction was also apparent for the vertical GRFs

during pushing.

Another parameter selected for analysis was the

maximum value of the variable within the timewindow, because the direct comparison between

measured and predicted values possible with the

GRFs may have been more sensitive to changes in

maximum values than to average values {see Table 2).

Notice again that the slope of the relationship was

closest to unity (0.7) for the vertical GRF during single

support. Less of the variability in the data was explained by these regressions (using maximum values)

compared with the average values shown earlier in

Table 2.

Comparisons of the trunk muscle forces derived

from RMS-EMGs with model predicted trunk muscle

forces indicated that even less of the variability in the

data was explained by the simple linear regression

model. Most of these regressions were not significant

at the 0.05 level. The following section will present

Table 2. Average and maximum GRF correlation results

with linear regression coefficients

Variable R-square ' Intercept Slope

AverageGRFs

Single support

R F, 0.63 - 10.9 0.59

R Fz 0.66 27 0.93

COF 0.I 0.15 0.I

Total support

R Fy 0.67 - !2.9 0.55

R F_ 0.I2 357 0.26

CO F NS -- --

Maximum GRFs

Single support

R Fy 0.45 14.3 0.3

R F: 0.45 164.4 0.78

CO F 0.06 0.27 0.05

Total support

RF, 0.32 55.4 0.18

R F, 0.21 435 0.42

CO F NS -- --

R F, = horizontal GRF, R F, = vertical GRF, CO F = ratio

of R Fwto R F,. Notice that the correlations during single

support exceeded those from the total support phase, demonstrating the improved performance of the model during single

support. Also note that the slope parameters were closest to

unity during single support with the vertical GRFs.
Z

A biodynamic model of pushing and pulling

Vertical GRF, pulling

1ooo

600 'J

400 t

/'_ I MeasuredZ _.

! I _ Predictedz

00 0 2 04 06 08 f.O

Time (see)

1039

--L

loo

Horizontal GRF, pulling

o

-1oo

-200

-3oo

Predicted Y V

-,-¢00 . , , , . .,

0 0 0'2 04 0'6 018 1 0

Time (see]

Fig. 4. Measured and predicted ground reaction forces for an example pulling trial Notice the underprediction of the vertical GRF and the peak horizontal GRF for this particular trial with this subject. This

discrepancy was more marked during double support (at either end of the right leg support GRF curves).

more detailed comparisons of derived and predicted

trunk muscle forces by performing ANOVA analysis

with subject and task factors included in the statistical

models.

A NO VA analysis of model predicted variables

The biodynamic model predicted variables and the

measured variables were analyzed with the inclusion

of subject (SN, $X, HT, WT) and task (CR, WS, DR)

factors. Predicted values exceeded measured values for

both horizontal and vertical GRFs, whether for single

or total right leg support (as seen by comparing grand

means in Table 3). Horizontal GRFs were dependent

always on direction of exertion, while all vertical

GRFs were affected by individual subject factors. Only

the mean measured and predicted vertical GRFs

during the total right leg support phase had the same

factors in the ANOVA analysis.

Predicted trunk muscle forces were subjected to the

same analysis (see Table 3). The model underpredicted

the ESMFs, but the predicted RAMFs fell between the

derived values for the right and left side. Since a wide

range of subject anthropometries were purposefully

used in these experiments, similar analyses were performed separately for each of the three weight categories (see Table 4). One noticeable difference be-
1040 R. O. Am3RES and D. B. CHXFFIN

Table 3. Average foot and trunk muscle force ANOVA summary

Variable R-square Mean Subject factors Task factors

Foot forces

Single support

Meas. R F_ 0.84 - 12.18 -- DR

Pred. R Fy 0.69 1.49 SN DR

Me,as. R Fs 0.66 582.15 SN, SX, HT DR

Pred. R F, 0.86 609.38 SN --

Meas. COF 0.14 1.15 -- --

Pred. CO F 0.14 0.29 -- --

Total support

Meas. R F, 0.88 - 12.82 HT WS, DR

Pred. R F, 0.69 1.93 SN DR

Meas. R F, 0.8 490.71 SN, SX, HT --

Pred. R F, 0.7 518.93 SN, SX, HT --

Meas. CO F 0.18 0.76 -- --

Pred. CO F 0.29 0.22 -- CR, WS

Trunk forces

Single support.

£SM F 0.39 561.62 SN CR

FML 0.73 765.2 SN DR

F MR 0.71 858.06 SN DR

RAM F 0.32 71.01 SN

F RL 0.36 35.48 SN

F RR 0.51 88.59 SN DR

Total support

ESM F 0.4 511.23 SN CR

F ML 0.74 742.29 SN DR

F MR 0.69 860.35 SN DR

RAM F 0.28 85.79 SN DR

F RL 0.36 32.94 SN

F RR 0.51 88.06 SN DR

R

"Pred.'before the variable denotes the predicted value, ar_ "Meas.' denotes the measured va2ue, R Fy

= horizontal GRF, RF, = vertical GRF, CO F ---ratio of R F, to R F_, EgM F = erector spinae muscle force

predicted by the model, F ML = erector spinae muscle force derived from the RMS-EMG of the left erector

spinae muscle, F MR =erector spinae muscle force derived from the RMS-EMG of the right erector spinae

muscle, RAM F = rectus abdominus muscle force predicted by the model, F RL = rectus abdominus muscle

force derived from left rectus abdominus muscle, and F RR =rectus abdominus muscle force derived from

right rectus abdominus muscle. Subject and task factors are explained in the text. Horizontal GRFs all

depended on the direction of exertion, with differences in the subject factors involved. Vertical GRFs were

affected by more subject factors. The trunk forces all depended on the individual subject factor; the only task

factors of importance were the direction of exertion and the cart resistance. Notice that the variability in the

measured variables was more readily explained by the selected subject and task factors than the variability in

the predicted variables.

tween derived and predicted means was that direction

of exertion was not a significant factor in the model

predicted values. Model predicted mean ESMFs were

less than their derived counterparts, most notably for

the subjects in the average weight category. The model

predicted that mean RAMFs exceeded the derived

values for light and average weight categories, but fell

between derived values for the heavy subjects.

Analysis of the differences between measured and model

predicted variables

The residuals between measured and predicted

horizontal GRFs wfre affected by subject, walking

speed, and direction of resistance (SN, WS, and DR, as

shown in Table 5). The positive residual means for

both horizontal and vertical GRFs reaffirm the biomechanical model's bias towards overprediction. The

vertical GRF residuals were affected by gender and

height (SX and HT). The horizontal to vertical GRF

ratio residuals were not explained by the linear statistical model. Average residual values were partitioned

by subject body weight category for both single and

total right leg support for the horizontal and the

vertical GRFs. The horizontal GRF residuals were

closest to zero for the subjects in the average weight

category (WT = 2) when pulling. During pushing the

residuals stayed positive across body weight. The

vertical GRF residuals were minimized with the light

subjects (WT= 1) for both pushing and pulling.
A biodynamic model of pushing and pulfing

Table 4. Average trunk muscle force ANOVA summary partitioned by subject body weight category

1041

Weight Variable R.square Mean Subject factors Task factors

Trunk extensors

Single support

Light ESM F 0.37 504.5 SN --

Light F MR 0.86 760.4 SN DR

Light F ML 0.95 620,4 SN DR

Average ESM F 0.4 534.2 SN --

Average F MR 0.79 1141.2 SN DR

Average F ML 0.65 1038.9 SN DR

Heavy ESM F 0.38 622 SN --

Heavy F MR 0.69 810.9 SN DR

Heavy F ML 0.77 761.8 SN WS, DR

Total support

Light ESM F 0.39 476.6 SN OR

Light F MR 0.87 748.2 SN DR

Light F ML 0.95 620 SN DR

Average ESM F 0.4 481.6 SN --

Average F MR 0.79 I 151 SN DR

Average F ML 0.67 1011.2 SN DR

Heavy ESM F 0.39 561.3 SN --

Heavy F MR 0.67 821.8 SN DR

Heavy F ML 0.78 722.2 SN WS, DR

Trunk flexors

Single support

Light RAM F 0.14 35.8 -- --

Light F RR 0.52 3.8 SN DR

Light F RL 0.4 5.3 SN DR

Average RAM F 0.24 37 -- --

Average F RR 0.53 19.7 SN DR

Average F RL 0.52 7 SN DR

Heavy RAM F 0.3_ 116 SN --

Heavy F RR 0.55 19t. I SN DR

Heavy F RL 0.37 73.8 SN --

Total support

Light RAM F 0.13 39.1 -- --

Light F RR 0.52 4 SN DR

Light F RL 0.42 5.9 SN DR

Average RAM F 0.31 37.7 SN --

Average F RR 0.51 17.8 SN DR

Average F RL 0.5 6.5 SN DR

Heavy RAM F 0.28 146.8 SN h

Heavy F RR 0.55 190.6 SN DR

Heavy F RL 0.37 67.6 SN

Note that the predicted trunk muscle forces (ESM F and RAM F ) were not dependent on the direction of

exertion, and that the model underpredicted the ESMF for the subjects of average weight.

Residuals were formed between model predicted

trunk muscle forces and derived left and right trunk

muscle forces for the total right leg support phase only

(Table 5). All of the trunk force residuals were affected

significantly by the individual subject factor (SN).

Residuals between predicted extensor muscle force

and derived muscle forces for each side were large and

negative. However, the residuals for the trunk flexors

were smaller. Right side ESMF residuals were closest

to zero for the heavy subjects. However, on the left side

the ESMF residuals were closest to zero for the

subjects in the average weight category. Right and left

side residual averages for RAMF resembled each

other more closely, with minimum residuals for subjects in the average weight category performing pulls.

DISCUSSION

The novelty of this particular model is the capability

to calculate dynamic parameters that relate to either

back overexertion risk or foot slip risk. Previous

investigations of pushing and pulling tasks used static

analysis, and predictions of internal back forces or

ground reaction forces were not attempted. Limitations of the present model will be discussed first in

light of the major assumptions required. The effects of
1042 R. O. ANDRES and D. B. CHAFFIN

Table 5. Average GRF and trunk muscle force residual ANOVA summaries

Variable R-square Mean Subject factors Task factors

GRF residuals

Single support

R F, 0.38 12.74 SN WS, DR

R F, 0.48 29.19 SX, HT DR

CO F 0.09 - 0.34 -- --

Total support

RF, 0.39 13.15 SN WS, DR

RF, 0.6 31.62 SN, SX, HT --

CO F 0.09 - 0.41 -- --

Trunk force residuals

l

Total support

ESM F (L) 0.29 - 2785.5 SN --

ESM F (R) 0.87 - 857.8 SN DR

RAM F(L) 0.76 - 13.1 SN DR

RAM F (R) 0.75 - 20.2 SN DR

The positive means for the GRF residuals indicate the model's tendency to overpredict,

although these average residuals are small. Note that residuals were larger for the trunk

forces.

these assumptions on model performance will then be

described. Finally, the validation approach used here

will be compared with other validations of biomcchanical models.

Model performance can not exceed the quality of

the input to the model; hence the input to the model

must be considered as a source of discrepancy between

measured and predicted values. Sagittal plane joint

center position measurement with the SELSPOT realtime position measurement system had two inherent

problems: firstly, the location of joint centers was only

grossly estimable with markers placed on the skin or

clothing at an approximated joint center, as determined by palpation. This problem has been documented extensively before (see Zahedi et al., 1987 for

relevant arguments) and will not be probed here.

Secondly, the use of a lateral photodetector caused

distortion problems due to the lens, the detector, and

signal reflection (Gustafsson and Lanshammar, 1977).

The steps described in the data acquisition section

compensated for these distortions.

Kinematic considerations played a large role in the

determination of single vs total right leg support.

Model GRF prediction differed depending on the type

of support used; without the use of foot switches or a

second force plate, the determination of left foot

contact with the ground relied on a foot LED floor

clearance which had a 1.0 cm tolerance and a Y

coordinate velocity of < 10% of maximum Y foot

velocity. Confounding this determination was the

subject's tendency to drag the fcct along the floor,

perhaps to maximize proprioceptiv¢ cues, particularly

when going backwards during a pull. Uncertainty in

selecting double vs single support could cause discontinuities in GRF predictions in the transition

region.

Several anthropometric variables were based on

previous work done on limited samples of cadavers

Flink lengths and masses from Dcmpster (1955) and

Clauser et al. (1969); radii of gyration from Chandler et

al. (1975); diaphragm area from Morris et al. (1961)

and Fisher (1967)]. The individual subjects performing

the current experiments may not be described accurately by these proportionality constants and average

areas, particularly because of the wide range of anthropometries selected. Detailed individual anthropometric measures would have been required to circumvent these innaccuracies, but these were beyond the

scope of this study.

The GRF calculations during double support came

from moment equilibrium equations. The selection of

the reference point for moments taken around the

foot-ground interface was an estimate because the

center of foot force application in the sagittal plane

could not be determined without recording moments

from the force platform. Therefore the proper location

of the foot contact reference point for horizontal

moment arms could have been anywhere between the

heel and the toe marker (the model assumed the

reference points were the heel marker for pushing and

the toe marker for pulling). It was possible that

inaccuracies in the horizontal moment arm estimates

contributed to model prediction errors during double

support.

The other assumption about double support was

that the friction utilization was the same under both

feet. Since only one force platform was available, this

could not be directly verified. When overcoming cart

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A biodynamic model of pushing and pulling 1043

inertia (or exerting push or pull forces maximally

against a stationary object) most subjects concentrated their foot forces on one foot, using the other

foot for balance (Chaffin er al. (1983); also based on

observation of the volitional postures and foot forces

used by subjects in the static calibration experiments).

This concentrated the vertical forces on a smaller

surface area, and thus increased the contact pressure

at that particular shoe-floor interface. How these

static exertions relate to the dynamic case remains

unclear.

Since the linear regression coefficients derived during the static calibration experiments were used to

transform the RMS-EMGs (obtained during the dynamic experiments) into derived muscle forces for

comparison with predicted muscle forces, they played

a role in evaluating model performance, and so are

discussed here. Muscle activity, as described by

EMGs, is highly variable. Processed EMGs (i.e. integrated, average, RMS) have been shown to describe

more accurately levels of muscle force output than raw

EMGs, particularly in isometric situations (see

Redfern (1989) for a complete review of these arguments). It is also well known that surface EMGs are

susceptible to signal decrements due to adipose tissue

interposed between the sensor and the muscle tissue

(Basmajian and DeLuca, 1985). The subjects in these

experiments were not of uniform somatotype, and so

the quality of surface EMG obtained from the heavy

subjects was suspect. Another factor may have been

the use of isometric exertions for the calibrations.

Most previous work correlating RMS-EMGs with

muscle force utilized discrete isometric force levels

only (Basmajian and DeLuca, 1985), as opposed to

our ramp of increasing force.

Vertical and horizontal GRFs were compared for

both single and total right leg support because of the

sensitivity of model predictions to the transition from

one support phase to the other. The most rigorous

validation of the model GRF predictions came from

the single support phase, since none of the assumptions necessary to make the double support solutions

determinate were required.

Measured average horizontal GRF values throughout the total right leg support phase were less than the

model predictions. However, inspection of the residual

analysis results revealed that larger intra-subject discrepancies existed when the double support phase was

included, as opposed to those during single support.

Performing the same comparisons for the vertical

GRFs, single support will be discussed first. The

amount of variability explained by the regression

analyses was similar for measured (66%) and predicted (86%) vertical GRFs. Considering the residuals

formed by taking the difference between predicted and

measured vertical GRFs during single support, the

mean average residual value was 29.2 N, indicating

that the model overpredicted more than it underpredicted.

During total right leg support, the mean average

values were again greater for predicted than for measured vertical GRFs. The intra-subject comparison

disclosed that the mean average force residual for the

total right leg support phase was essentially the same

as for the single support phase; this implied that the

patterns of model over- or underprediction during

double support were not consistent from subject to

subject, and hence were counteracted when combined

across subjects. The statistical consensus was that

model predictions of GRFs during double support

were not as valid as those during single support. This

must be attributed to both the limitations of the

moment arms used and the assumption ot"the equal

friction utilization by both feet during double support.

Since the derived ESMFs came from transformations of measured RMS-EMG values with regression coefficients obtained from the static experiments, any limitations in the regressions will be reflected in the derived ESMFs. The ANOVA analysis of

derived ESMFs indicated that anywhere from 68 to

74% of the variability in the data was explained by the

independent main factors.

Considering the analysis of residuals formed by

subtracting the derived ESMFs from the predicted

ESMFs, the activity of the right side erector spinae

group was predicted better than the activity of the left

side muscle group. On an intra-subject basis, the

predictions underestimated average ESMFs over the

total right leg support phase. The previous discussion

of surface EMGs taken from varied somatotypes is

also germane, but, given all of these qualifiers, predicted ESMF performed qualitatively the same as right

side derived ESMF. It should be stressed that the

support phase used throughout this study occurred on

the right foot, and therefore it was not surprising that

right side derived ESMFs seemed more realistic than

left side values; the left side muscles were involved to

some degree in the swing phase of the left leg, which

occurred during right leg support.

The analysis by subject body weight category provided better insight into the indirect validation of

predicted muscle forces compared with muscle forces

derived from dynamic RMS-EMGs. Perfect correspondence of derived and predicted values would yield

residuals equal to zero. In most cases the ESMF

residuals were closest to zero for the medium weight

subjects. Adding the consideration that the erector

spinae are trunk extensors, they were expected to be

most active during pulling. Indeed, in most cases, the

residual parameters were closer to zero for the pulling

tasks in this investigation.

Derived and predicted RAMFs were examined in a

similar manner. Based on the argument that the left

side of the muscle group may have some involvement

as a synergist during left leg swing, only the right side

rectus abdominus muscle group will be discussed here.

Minimal anterior muscle activity was seen during the

pulling tasks. As was the case with the predicted

erector spinae forces, more subject factors (SX, HT,

WT) were present in the ANOVA results than for the
1044 R.O. ANDRESand D. B. CHAFFIN

derived forces in the anterior muscles. It should be

stressed that the linear regressions derived from the

static calibration experiments did not, in general,

explain as much of the variability in the RMS-EMGs

of the rectus abdominus as for the erector spinae. This

was attributed partly to the variation in adipose tissue

distribution in the anterior part of the trunk as

opposed to the posterior low back region.

The RAMF residual parameters were closest to zero

for either the light or the average weight subjects. The

rectus abdominus was most active during pushing; i.e.

the residual averages during pushing were low. However, residual maxima (all positive) during pushing

increased as weight increased, reflecting an increasing

model overprediction with heavy subjects.

Lee (1982) also reported much better model performance in predicting GRFs than EMGs. He found

that predicted vertical GRFs correlated with measured vertical GRFs (r2 _0.65) and predicted horizontal GRFs correlated with measured horizontal GRFs

(r2=0.56). Mean errors were small (60 and 19 N,

respectively). This performance was similar to that

found in the current investigation. Lee found that

there was a subject effect in the differences between

predicted and measured GRFs. This was again found

in the current investigation in the residual analysis.

The validation approach in this investigation was

similar to Lee (1982), with the exception that he used

the regression relationships between isometric RMSEMGs and exerted trunk forces to transform predicted dynamic trunk muscle forces to predicted dynamic RMS-EMGs, which were then compared with

the measured dynamic RMS-EMGs. Since there have

been no other dynamic biomechanical model analyses

of pushing and pulling; related validation techniques

can only be found in different tasks. Static predictions

of mean spine compression acting at L3 have been

correlated with mean intradiscal pressure measurements (Schultz and Andersson, 1981), with a correlation coefficient of 0.91. This same group later developed a model to predict trunk muscle forces during

isometric weight-holding and force resistance tasks

(Schultz et al., 1982), which they validated by comparing their calculated trunk muscle tensions with

mean myoelectric signal levels. Correlation coefficients ranged from 0.34 to 0.92, depending on the

muscle group and on the function used to predict

muscle force. More germane to the current investigation were predictions of dynamic trunk loading; ,lager

and Luttman (1989) developed a dynamic 19-segment

model to assess lumbar stress during load lifting. Their

validation consisted of comparing their model calculations with intradiscal pressure measurements taken

from the literature; only static holds were compared.

Direct comparisons of predicted and measured

ground reaction forces have been performed by others

(Pandy and Berme, 1988, 1989). These studies simulated GRFs by assuming joint moment trajectories

and performing open-loop (single support) or closedloop (double support) analyses during normal or

pathological walking. However, cart pushing or

pulling tasks have not been similarly examined.

Previous biomechanical model validation approaches have not systematically varied subject anthropometry and gender. The evidence from this

investigation suggests that this particular dynamic

biomechanical model was valid when a wide range of

anthropometries was studied. Model predictions, including both GRFs and trunk muscle forces, were

better during single support phases than during

double support phases.

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