Seated Human Spine Response Prediction to Vertical Vibration via

Artificial Neural Network

Abdul Aziz Naser

Faculty of Engineering, University of Technology, New York, USA

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Abstract

Harmonic vibrat ion and shock can create health problem in long term especially in heavy duty

machineries such as bus, truck, agricultural tractor and mine excavators. People are interested

in remove this undesirable vibrat ion by seat suspension systems. In design of seat suspension

biodynamic models are necessary, and having that can help to researchers to predict human

body behavior. Artificial neural network is a new computation method which is good for this

purpose. In this study, an artificia l neural network model was established based on

experimental data to represent response of spine to the vert ical vibrat ion. The accuracy of this

model is high (over 90%) in comparison to previous models like as lumped or finite elements

models. Also, weight and height are considered in this model as inputs. Achieved bio

dynamic ANN model can be used in other research purpose such as seat suspension

optimizat ion or adaptive seat suspension control systems.

Key Words: Biodynamic model, artificial neural network, vibrat ion responses of spine,

who le body vibrat ion

Introduction

Today, people become sensit ive and conservat ive about shock and vibration. Vibrat ion

not only produces mental problems, but also leads to physical illness such as digest ive

problem, heart pulse increasing, spine co lumn disorder, back pain or weakness in vision. One

of the earliest studies carried out by Hamilton (1918) was the effects of vibrat ion on mine

workers. Side effects of oscillat ion in seated human body may be very serious and leads to

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permanent defect in spine co lumn (Kelsey and Hardy, 1975). Vehicles such as trains, buses,

trucks and automobiles are the main sources of vibration to human being. The drivers for

most of these vehicles are exposed to whole body vibrat ion during their jobs. Also, these

drivers have to sit for long durat ion with constant posture, which may contribute to the

occurrence of low back injury and pain (Bovenzi, et al, 2002). In another research, Bovenzi

and Betta (1994) studied the occurrence o f low back pain among male agricultural tractor

drivers with 1155 subjects compared to a control group of male o ffice workers with 220

subjects. The response rates among the tractor drivers and controls were 91% and 92%,

respectively.

The effects of who le body vibrat ion on the disc component of the lumbar spine have

also been explored (Frymo yer et al., 1980; Sandover, 1983; Wilder et al., 1982). The

degeneration o f discs or end plates from prolonged who le body vibrat ion is most commo n in

the lower lumbar spine.

In addit io n to back pain problem, exposure to long duration of vibrat ion leads to rise

of heart rate and increase in blood pressure (Kubo et al. 2001). Plus, osteoarthrit is o f hip is

another physical problem due to long exposure to vibration (Jacobsson et al. 1987; Thelin

1990; Croft et al. 1992; Axmacher and Lindberg 1993).

In studying the negat ive influences o f undesirable vibrat ion, the nature and model o f

vibration need to be known. Thus various biodynamic models were created to predict human

body responses to who le body vibration. The main models are grouped to lumped, finite

element and mult i body models.

The lumped models are consist ing of masses, spring and damper elements which

simulated the human body parts. The earliest lumped model was one degree of freedom mode l

(Coermann et al.,1962) but it unable to reproduce human response in all of body parts.

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A two degree of freedom (2DOF) nonlinear model was established by Musksian and

Nash (1976). Other 2DOF models were introduced by Allen (1978) and Wei and Griffin

(1998). Both of these models were linear with total weight around 50 kg in spite o f Musksian

model which has total weight of 79.83 kg. Suggs et al., 1969 established a three degree o f

freedom (3DOF) lumped model which was similar to Allen model, but it had extra degree of

freedom for upper torso.

Four degrees o f freedom models considered extra parts and organs of human body

such as torso (upper and lower), viscera and neck. The accuracy is relat ively high, and those

are suitable for seat suspension optimizat ion. Wan and Schimmels (1995), Boileau and

Rakheja (1998) and Liu et al. (1998) developed various 4DOF models which were created to

focus on some internal organs.

Patil (1977) developed a 7 degree of freedo m model for measuring tractor drivers’

human body response to vibrat ion. There are now many lumped models available in the

literature and most are developed for vertical vibration and without considering human body

characterist ics like as weight and height. This limited the usage of lumped models for various

subjects’ properties.

A 2DOF finite elements models (FEM) was used by Belytschko et al. (1974) for

modeling the lumbar disc-body unit. The dynamics behavior of L4 and L5 in spine co lumn

was simulated in 3DOF by Ueno and Liu (1987). By this model, it is possible to conduct

static loading and dynamic loads analysis. In another study, the lower lumbar structure was

simulated by Toh Yen Pang (2006) by employing the 3DOF finite element model. However,

the mechanical properties o f human body parts need to be known. Bones, live t issues and

ligaments are rhyeo logic material, and mechanical characterist ics of them are variable in

different situations and different persons. Thus with this limitation, researchers are eager to

obtain whole body model for vibration to be extendable for any bodies.

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Recently, a new approach in science and engineering was introduced which is named

Art ificial Neural Network (ANN). This method is based on human brain learning, and is

useful for modeling and approximations. Linear and nonlinear problems modeling are

possible. Hence, because of limitations in FE and lumped models for human body responses

modeling, ANN method was selected as a novel procedure for this purpose.

Methodology

Experiments methods

In this study, the seated human body is considered as a mechanical system. Input

acceleration is applied at the seat and output point is at the spine. As shown in Fig.1, this

structure is equivalent to a mathematical model which can predict output responses fro m input

signals.

Fig. 1: ANN human vibration body based on simulated experiments

A tensile test machine with 2000 kN capacity in force was modified for human subject

exposing to the harmonic vibrat ion. Special jig was made to attach I-beam to the tensile

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machine gripper. On the other end o f the beam, a plate was fastened for subject sitting.

Human subjects were sat on the plate and excited using harmo nic function. The frequency

range was between 1Hz to 14Hz. Three accelerometers were attached to the plate, spine and

head to record base excitat ion and subject’s spine and head reactions according to ISO 2631

standard. Bruel & Kjaer (B&K) low frequency accelerometer (50Hz) was chosen to measure

the acceleration. In addit ion, a B&K data logger was used for signal condit ioning and no ise

filtering. Data sampling rate of data logger was set to 0.001 Hz. The raw data were recorded

as vert ical speed and accelerat ion was saved for post processing.

Five healthy males, in various weight and height, were selected as test samples for the

test. The weight and height of the human subjects are listed in Table.1. Human subjects were

exposed to the vert ical vibrat ion in low frequency range from 1Hz to 14Hz and at 10mm

displacement. The posture was erect without backrest, and feet were supported as illustrated

in Fig.2. The accelerat ion responses o f spine, head and pelvis were recorded by the data

acquisit ion system with the sampling rate set to 1000 sample per second. The frequency o f

harmonic excitation was increased by 0.5 Hz increment to 5Hz, and then by 1 Hz to 14 Hz.

The data were recorded for 30 seconds. The Dewsoft 9.9 software was used for data gathering

and Fast Fourier transformation. A sample of data recorded is shown in Fig.3.

Table1. Height and weights of human subjects.

No. of subject 1 2 3 4 5

Weight (kg) 65 85 56 60 70

Height (cm) 170 160 170 165 167

BMI= Height/Weight 2.61 1.88 3.03 2.75 2.38

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Fig.2- Human subject in erect seated posture was exposed to the vertical vibration.

Fig.3- (a) base acceleration (m/s2), (b) spine accelerat ion (m/s

2), (c) head acceleration (m/s

2), (d) Fast Fourier

transformed of base (Hz), (e) Fast Fourier transformed of spine (Hz) and (f) Fast Fourier transformed of head

(Hz).

Modeling by Artificial Neural Network

After preprocessing and filtration were done to the raw data, each point of input and

outputs (pelvic, spine and head) were broken down and considered as separate point for

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entering signal in artificial neural network. Due to the continuous accelerat ion, each po ints o f

signal were considered as an input value with 0.125 second interval. By using this method, the

range of 0 to 10s is separated to 80 points. Similar to this method, 80 points were considered

for output spine signal. A schematic picture is shown in Figure 4 and 5.

In this model, spine signal was considered as function of pelvic signal, human weight

and height.

[aspine ] T ain ,W , H

Where T is transfer funct ion which can calculate spine accelerat ion and head accelerat ion, W

and H are subject weight and height, respectively.

A feed forward artificial neural network with back propagation was used for this

model. Networks with various numbers o f hidden layers were tried to earn best accuracy. In

addit ion, various learning algorithms, error functions and thresho ld funct ions were tested.

After training and adaptation was applied in the network, the outputs of model were simulated

by same input. Finally, best ANN model which has best fitting to the desired output values

was selected.

Fig. 4- The relationship between input signal and output signals in ANN model

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Fig. 5- The signal broken down to separate points

Results and Discussion

A network with 6 hidden layers and 11 neurons showed the best correlat ion ratio

between output set and input set, thus it is selected as proposed ANN model. Fig.6 illustrates

R value in test, train, validat ion and all. R value was obtained as 0.981.

Fig.6- The regression between output and target in training, validation, test steps and in overall.

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S

pin

e A

ccele

rati

on

R.M

.S.

Sp

ine A

cce

lera

tion R

.M.S

.

As mentioned previously, after training the network, the input signals for 5 subjects

were entered in the model, and the outputs were compared to the actual outputs. The results

showed good agreements between predicted signals and actual signals. In Fig.7 to Fig.11

actual values and output values for spine responses were represented in 4Hz frequency due to

crit ical resonance occurring in this range.

Actual Values (Sample1) Predicted by ANN Model

6

5

4

3

2

1

0

-1 0 2 4 6 8 10 12

-2

-3

-4

-5

Time (s)

Fig.7- The comparison between actual acceleration of spine and predicted acceleration for subject No.1, in 4Hz.

Actual Values (Sample2) Predic ted by A NN Model

4

3

2

1

0

0 2 4 6 8 10 12 -1

-2

-3

Time (s)

Fig.8- The comparison between actual acceleration of spine and predicted acceleration for subject No.2, in 4Hz.

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Sp

ine

Ac

cele

rati

on

R.M

.S.

Sp

ine

Accele

rati

on

R.M

.S.

Sp

ine A

ccele

rati

on R

.M.S

.

Actual Valuesl(Sample3) Predic ted by ANN Mode

6

5

4

3

2

1

0

-1 0 2 4 6 8 10 12

-2

-3

-4

Time (s)

Fig.9- The comparison between actual acceleration of spine and predicted acceleration for subject No.3, in 4Hz.

Actual Values(Sample4) Predic ted by A NN Model

4

3

2

1

0

-1 0 2 4 6 8 10 12

-2

-3

-4

-5

Time (s)

Fig.10- The comparison between actual acceleration of spine and predicted acceleration for subject No.4, in 4Hz.

Actual Values (Sample5) Predicted by ANN Model

5

4

3

2

1

0

-1 0 2 4 6 8 10 12

-2

-3

-4

Time (s)

Fig.11- The comparison between actual acceleration of spine and predicted acceleration for subject No.5, in 4Hz.

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Pre

dic

ted S

pin

e

Acc

eler

atio

n

by M

od

el

(m.s

^-2

)

Pre

dic

ted

Sp

ine

Accele

rati

on

b

y M

od

el

(m.s

^-2

)

Pre

dic

ted

Spin

e

Accele

rati

on

by

Mo

del

(m.s

^-2

)

Pre

dic

ted

Spin

e

Accele

rati

on

by M

od

el

(m.s

^-2

) P

red

icte

d S

pin

e

Accele

rati

on

b

y M

od

el

(m.s

^-2

)

The linear regressio n shows better correlat ion ratio between actual signals and

predicted spine signals by ANN model. The correlat ion rat ios for five human subjects vary

from 0.911 to 0.979 which were depicted in Fig.12.

A N N M o d e l Outp ut VS A c tu al O utp ut

(S a mp le 1)

y = 0.969x + 0.0315 6

R2

= 0.9385

2

0

ANN M od e l Output VS A ct u al O ut p ut

(Sample 2)

y = 0.0427x

2 + 1.0165x - 0.0537

R2

= 0.9794 3

2

1

0 -6 -4 -2 -2 0 2 4 6

-4

-6

-2 -1 0 2 4

-2

-3

A c t u al Sp ine Acceleration (m.s ̂ -

2)

A c t u al Sp ine Acceleration ( m.s ̂ -2)

ANN Model Outp ut VS Act ual Outp ut

(Samp le 3)

y = 0.9914x + 0.0336

4

2

0

ANN Model Outp ut VS Act ual Outp ut

(Sample 4)

y = 1.0214x + 0.1681 6

R2

= 0.9111 4

2

0

-5 -2 0 5 10

-4

-6 -4 -2 -2 0 2 4

-4

-6

Actual Spine Acceleration (m.s ̂ -2)

Actual Spine Acceleration (m.s ̂ -2)

ANN Model Output VS Actual Output

(Sample 5)

-4 -2

y = 0.916x + 0.0087

6 R

2 = 0.9561

4

2

0

-2 0 2 4

-4

Actual Spine A cceleration (m.s ̂ -2)

Fig.12- The relationship between predicted spine acceleration and actual spine acceleration for five subjects.

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Due to high accuracy of spine accelerat ion prediction, the accuracy of seat to spine

transmissibility is over 94.85%. The highest values of goodness- of- fit in previous models for

STH (seat to head vibrat ion transmissibility) is belong to Wan-Schimmels model, and it was

91.0%. Compared to Wan-Schimmels model, achieved ANN model can est imate body

acceleration with higher accuracy. That model was a two degree of freedom which the

correlat ion ratio in that ANN model was 0.9577. This experimental ANN model has lower

average value in regression ratio (0.9485), but same as previous study goodness-of- fit for seat

to head transmissibility and seat to spine transmissibility is higher than 90%. Another

advantage o f this model is the effect of weight and height of human body in responding to the

vibration which did not consider in other biodynamic models.

Conclusion

This new model showed that ANN has acceptable accuracy for biodynamic modeling.

The main characteristics o f this novel model, in contrast lumped models with fixed weight, is

considering weight and height of human body in responding to vibrat ion. Plus, the complexity

of achieved model is low, and this issue made it suitable for modeling and predict ing

acceleration and force in both of time and frequency do main. In spite o f other biodynamic

models like as Wan-Schimmel (1995), Mertens (1978), Muksian and Nash (1976), Allen

(1978), this ANN model has better accuracy near to 95%. Thus, this model is very suitable for

design and optimizing in suspension systems.

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Acknowledgements

The author would like to express their gratitude to University of Technology.

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