Seated Human Spine Response Prediction to Vertical Vibration via                       Artificial Neural Network          ...
AbstractHarmonic vibration and shock can create health problem in long term especially in heavy dutymachineries such as bu...
permanent defect in spine co lumn (Kelsey and Hardy, 1975). Vehicles such as trains, buses,trucks and automobiles are the ...
A two degree of freedo m (2DOF) nonlinear model was established by Musksian andNash (1976). Other 2DOF models were introdu...
Recently, a new approach in science and engineering was introduced which is namedArtificial Neural Network (ANN). This met...
machine gripper. On the other end o f the beam, a plate was fastened for subject sitting.Human subjects were sat on the pl...
Fig.2- Hu man subject in erect seated posture was exposed to the vertical vibration.Fig.3- (a) base acceleration (m/s 2 ),...
entering signal in artificial neural network. Due to the continuous acceleration, each po ints o fsignal were considered a...
Fig. 5- The signal broken down to separate pointsResults and Discussion       A network with 6 hidden layers and 11 neuron...
As mentioned previously, after training the network, the input signals for 5 subjectswere entered in the model, and the ou...
Actual Valuesl( Sample3)         Predic ted by ANN Mode                                                         6         ...
The linear regressio n shows better correlation ratio between actual signals andpredicted spine signals by ANN model. The ...
Due to high accuracy of spine acceleration prediction, the accuracy of seat to spinetransmissibility is over 94.85%. The h...
Acknowledge ments   The author would like to express their gratitude to University of Technology.References- Allen, G., 19...
- Frymoyer, J. W., M. H. Pope, M. C. Costanza, J. C. Rosen, J. E. Goggin, and D. G. Wilder,1980. Epidemio logic studies of...
- Muksian, R., and C.D. J. Nash, 1976. On frequency dependent damping coefficients inlumped parameter models of human bein...
- Wilder, D. G., B. B.Woodworth, J. W. Frymoyer, and M. H. Pope, 1982. Vibration and thehuman spine. Spine 7(3), 243-254.A...
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Seated Human Spine Response Prediction to Vertical Vibration via Artificial Neural Network

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Harmonic vibration 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 vibration 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 artificial neural network model was established based on experimental data to represent response of spine to the vertical vibration. 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 optimization or adaptive seat suspension control systems.

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Seated Human Spine Response Prediction to Vertical Vibration via Artificial Neural Network

  1. 1. Seated Human Spine Response Prediction to Vertical Vibration via Artificial Neural Network Abdul Aziz Naser Faculty of Engineering, University of Technology, New York, USA 1
  2. 2. AbstractHarmonic vibration and shock can create health problem in long term especially in heavy dutymachineries such as bus, truck, agricultural tractor and mine excavators. People are interestedin remo ve this undesirable vibration by seat suspensio n systems. In design of seat suspensio nbiodynamic models are necessary, and having that can help to researchers to predict humanbody behavior. Artificial neural network is a new computation method which is good for thispurpose. In this study, an artificia l neural network model was established based onexperimental data to represent response of spine to the vertical vibration. The accuracy of thismodel is high (over 90%) in co mparison to previous models like as lumped or finite elementsmodels. Also, weight and height are considered in this model as inputs. Achieved biodynamic ANN model can be used in other research purpose such as seat suspensio noptimization or adaptive seat suspensio n control systems.Key Words: Biodynamic model, artificial neural network, vibration responses of spine,who le body vibrationIntroduction Today, people beco me sensitive and conservative about shock and vibration. Vibrationnot only produces mental problems, but also leads to physical illness such as digestiveproblem, heart pulse increasing, spine co lumn disorder, back pain or weakness in visio n. Oneof the earliest studies carried out by Hamilton (1918) was the effects of vibration on mineworkers. Side effects of oscillation in seated human body may be very serious and leads to 2
  3. 3. 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 formost of these vehicles are exposed to whole body vibration during their jobs. Also, thesedrivers have to sit for long duration with constant posture, which may contribute to theoccurrence of low back injury and pain (Bovenzi, et al, 2002). In another research, Bovenziand Betta (1994) studied the occurrence o f low back pain among male agricultural tractordrivers with 1155 subjects compared to a control group of male o ffice workers with 220subjects. The response rates amo ng the tractor drivers and controls were 91% and 92%,respectively. The effects of who le body vibration on the disc component of the lumbar spine havealso been explored (Frymo yer et al., 1980; Sandover, 1983; Wilder et al., 1982). Thedegeneration o f discs or end plates from prolonged who le body vibration is most commo n inthe lower lumbar spine. In additio n to back pain problem, exposure to long duration of vibration leads to riseof heart rate and increase in blood pressure (Kubo et al. 2001). Plus, osteoarthritis o f hip isanother physical problem due to long exposure to vibration (Jacobsson et al. 1987; Thelin1990; Croft et al. 1992; Axmacher and Lindberg 1993). In studying the negative influences o f undesirable vibration, the nature and model o fvibration need to be known. Thus various biodynamic models were created to predict humanbody responses to who le body vibration. The main models are grouped to lumped, finiteelement and multi body models. The lumped models are consisting of masses, spring and damper elements whic hsimulated 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. 3
  4. 4. A two degree of freedo m (2DOF) nonlinear model was established by Musksian andNash (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 Musksia nmodel which has total weight of 79.83 kg. Suggs et al., 1969 established a three degree o ffreedo m (3DOF) lumped model which was similar to Allen model, but it had extra degree offreedo m for upper torso. Four degrees o f freedom models considered extra parts and organs of human bodysuch as torso (upper and lower), viscera and neck. The accuracy is relatively high, and thoseare suitable for seat suspension optimization. Wan and Schimmels (1995), Boileau andRakheja (1998) and Liu et al. (1998) developed various 4DOF models which were created tofocus on so me internal organs. Patil (1977) developed a 7 degree of freedo m model for measuring tractor drivers’human body response to vibration. There are now many lumped models available in theliterature and most are developed for vertical vibration and without considering human bod ycharacteristics like as weight and height. This limited the usage of lumped models for varioussubjects’ properties. A 2DOF finite elements models (FEM) was used by Belytschko et al. (1974) formodeling the lumbar disc-body unit. The dynamics behavior of L4 and L5 in spine co lumnwas simulated in 3DOF by Ueno and Liu (1987). By this model, it is possible to conductstatic loading and dynamic loads analysis. In another study, the lower lumbar structure wassimulated by Toh Yen Pang (2006) by emplo ying the 3DOF finite element model. However,the mechanical properties o f human body parts need to be known. Bones, live tissues andligaments are rhyeo logic material, and mechanical characteristics of them are variable indifferent situations and different persons. Thus with this limitation, researchers are eager toobtain whole body model for vibration to be extendable for any bodies. 4
  5. 5. Recently, a new approach in science and engineering was introduced which is namedArtificial Neural Network (ANN). This method is based on human brain learning, and isuseful for modeling and approximations. Linear and nonlinear problems modeling arepossible. Hence, because of limitations in FE and lumped models for human body responsesmodeling, ANN method was selected as a novel procedure for this purpose.MethodologyExperiments methods In this study, the seated human body is considered as a mechanical system. Inputacceleration is applied at the seat and output point is at the spine. As shown in Fig.1, thisstructure is equivalent to a mathematical model which can predict output responses fro m inputsignals. Fig. 1: ANN human vibration body based on simulated experiments A tensile test machine with 2000 kN capacity in force was modified for human subjectexposing to the harmo nic vibration. Special jig was made to attach I-beam to the tensile 5
  6. 6. 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 frequenc yrange was between 1Hz to 14Hz. Three accelerometers were attached to the plate, spine andhead to record base excitation and subject’s spine and head reactions according to ISO 2631standard. Bruel & Kjaer (B&K) low frequency accelero meter (50Hz) was chosen to measurethe acceleration. In addition, a B&K data logger was used for signal conditio ning and no isefiltering. Data sampling rate of data logger was set to 0.001 Hz. The raw data were recordedas vertical speed and acceleration was saved for post processing. Five healthy males, in various weight and height, were selected as test samples for thetest. The weight and height of the human subjects are listed in Table.1. Human subjects wereexposed to the vertical vibration in low frequency range fro m 1Hz to 14Hz and at 10mmdisplacement. The posture was erect without backrest, and feet were supported as illustratedin Fig.2. The acceleration responses o f spine, head and pelvis were recorded by the dataacquisition system with the sampling rate set to 1000 sample per second. The frequency o fharmo nic 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 gatheringand 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 6
  7. 7. Fig.2- Hu man subject in erect seated posture was exposed to the vertical vibration.Fig.3- (a) base acceleration (m/s 2 ), (b) spine accelerat ion (m/s 2 ), (c) head acceleration (m/s 2 ), (d) Fast Fouriertransformed 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 andoutputs (pelvic, spine and head) were broken down and considered as separate point for 7
  8. 8. entering signal in artificial neural network. Due to the continuous acceleration, each po ints o fsignal were considered as an input value with 0.125 second interval. By using this method, therange of 0 to 10s is separated to 80 points. Similar to this method, 80 points were consideredfor 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 weightand height.[aspine ] T ain ,W , HWhere T is transfer function which can calculate spine acceleration and head acceleration, Wand H are subject weight and height, respectively. A feed forward artificial neural network with back propagation was used for thismodel. Networks with various numbers o f hidden layers were tried to earn best accuracy. Inaddition, various learning algorithms, error functions and thresho ld functions were tested.After training and adaptation was applied in the network, the outputs of model were simulatedby same input. Finally, best ANN model which has best fitting to the desired output valueswas selected. Fig. 4- The relationship between input signal and output signals in ANN model 8
  9. 9. Fig. 5- The signal broken down to separate pointsResults and Discussion A network with 6 hidden layers and 11 neurons showed the best correlation ratiobetween output set and input set, thus it is selected as proposed ANN model. Fig.6 illustratesR value in test, train, validation 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. 9
  10. 10. As mentioned previously, after training the network, the input signals for 5 subjectswere entered in the model, and the outputs were compared to the actual outputs. The resultsshowed good agreements between predicted signals and actual signals. In Fig.7 to Fig.11actual values and output values for spine responses were represented in 4Hz frequency due tocritical resonance occurring in this range. Actual Values (Sample1) Predicted by ANN Model 6 5 Spine Acceleration R.M.S. 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 Spine Acceleration R.M.S. 3 2 1 0 0 2 4 6 8 10 12 -1 -2 -3 Tim e (s)Fig.8- The comparison between actual acceleration of spine and predicted acceleration for subject No.2, in 4Hz. 10
  11. 11. Actual Valuesl( Sample3) Predic ted by ANN Mode 6 5 Spine Acceleration R.M.S. 4 3 2 1 0 -1 0 2 4 6 8 10 12 -2 -3 -4 Tim e (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 Spine Acceleration R.M.S. 2 1 0 -1 0 2 4 6 8 10 12 -2 -3 -4 -5 Tim e (s)Fig.10- The co mparison between actual acceleration of spine and predicted acceleration for subject No.4, in 4Hz. Actual Values (Sample5) Predicted by ANN Model 5 4 Spine Acceleration R.M.S. 3 2 1 0 -1 0 2 4 6 8 10 12 -2 -3 -4 Time (s)Fig.11- The co mparison between actual acceleration of spine and predicted acceleration for subject No.5, in 4Hz. 11
  12. 12. The linear regressio n shows better correlation ratio between actual signals andpredicted spine signals by ANN model. The correlation ratios for five human subjects var yfro m 0.911 to 0.979 which were depicted in Fig.12. ANN M od e l Output VS A ct u al Out p ut A N N M o d e l Outp ut VS Ac tu al Outp ut (Sample 2) (S a mp le 1) y = 0.969x + 0.0315 y = 0.0427x2 + 1.0165x - 0.0537 6 2 R = 0.9794 R2 = 0.9385 Acceleration by Model 3 Accel erati on by Model Predicted Spine 2 Predict ed S pi n e 2 (m. s^-2) 1 (m.s^-2) 0 0 -6 -4 -2 -2 0 2 4 6 -2 -1 0 2 4 -4 -2 -6 -3 A c t u al Sp ine Acce leration (m.s ^- A c t u al Sp ine Acceleration ( m.s ^-2) 2) ANN Model Outp ut VS Act ual Outp ut ANN Mode l Outp ut VS Act ua l Outp ut (Sample 4) (Sa mp le 3) y = 0.9914x + 0.0336 6 Acceleration by M odel y = 1.0214x + 0.1681 Acceleration by Mode l 2 4 Predicted Spine 4 R = 0.9111 Predicted Spine 2 (m.s^-2) (m.s^-2) 2 0 0 -6 -4 -2 -2 0 2 4 -5 -2 0 5 10 -4 -4 -6 Actual Spine Acce leration (m.s ^-2) Actual Spine Acce leration (m.s ^-2) ANN Model Output VS A ctual Output (Sample 5) y = 0.916x + 0.0087 6 R2 = 0.9561 Acceleration by Predicted Spine Model (m.s^-2) 4 2 0 -4 -2 -2 0 2 4 -4 A ctual Spine A cceleration (m.s ^-2) Fig.12- The relationship between predicted spine acceleration and actual spine acceleration for five subjects. 12
  13. 13. Due to high accuracy of spine acceleration prediction, the accuracy of seat to spinetransmissibility is over 94.85%. The highest values of goodness- of- fit in previous models forSTH (seat to head vibration transmissibility) is belong to Wan-Schimmels model, and it was91.0%. Compared to Wan-Schimmels model, achieved ANN model can estimate bod yacceleration with higher accuracy. That model was a two degree of freedom whic h thecorrelation ratio in that ANN model was 0.9577. This experimental ANN model has loweraverage value in regressio n ratio (0.9485), but same as previous study goodness-of- fit for seatto head transmissibility and seat to spine transmissibility is higher than 90%. Anotheradvantage o f this model is the effect of weight and height of human body in responding to thevibration 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, isconsidering weight and height of human body in responding to vibration. Plus, the co mplexityof achieved model is low, and this issue made it suitable for modeling and predictingacceleration and force in both of time and frequency do main. In spite o f other biodynamicmodels like as Wan-Schimmel (1995), Mertens (1978), Muksian and Nash (1976), Alle n(1978), this ANN model has better accuracy near to 95%. Thus, this model is very suitable fordesign and optimizing in suspensio n systems. 13
  14. 14. Acknowledge ments The author would like to express their gratitude to University of Technology.References- Allen, G., 1978. A critical look at bio mechanic al modeling in relation to specifications forhuman tolerance o f vibration and shock. AGARD Conference Proceedings No. 253, PaperA25-5, Paris, France, pp: 6–10.- Axmacher, B. and H. Lindberg, 1993. Coxarthrosis in farmers. Clin. Orthop. 287:82–86.Available fro m: http://www.ncbi.nlm.nih.gov/pubmed/8448964.- Bovenzi, M., I. Pinto, and N. Stacchini, 2002. Low back pain in port machinery operators.Journal of Sound and Vibration, 253(1): 3-20.Available fro m: http://www.sciencedirect.com/science/article/pii/S0022460X01942464.- Bovenzi, M., and Betta, A. (1994). Low-back disorders in agricultural tractor driversexposed to whole-body vibration and postural stress. Applied Ergonomics, 25(4): 231-241.Available fro m: http://www.ncbi.nlm.nih.gov/pubmed/15676973.- Cho, Y., Y.S. Yoon, 2001. Biomechanical model o f human on seat with backrest forevaluating ride quality. International Journal o f Industrial Ergono mics, 27: 331–345.Available fro m:http://www.ingentaconnect.com/content/els/01698141/2001/00000027/00000005/art00061.- Croft, P., D. Coggon, M. Cruddas, and C. Cooper, 1992. Osteoarthritis o f the hip: a noccupational disease in farmers. B. M. J. 304:1269–1272.Available fro m: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1881870/. 14
  15. 15. - Frymoyer, J. W., M. H. Pope, M. C. Costanza, J. C. Rosen, J. E. Goggin, and D. G. Wilder,1980. Epidemio logic studies of low-back pain. Spine 5(5): 419-423.Available fro m: http://www.ncbi.nlm.nih.gov/pubmed/6450452.- Hamilton, A., 1918. Reports of physicians for the bureau o f labor statistics—a study o fspastic anemia in the hands of stone cutters. Bulletin 236, Industrial Accidents and HygieneNational Technical Information Service, NTISPB-254601: 53-66.Available fro m: www.chaseergo.com/research_02.html.- Jacobsson B, Dalen N, Tjornstrand B (1987) Coxarthrosis and labour. Int. Orthop. 11:311–313. Available fro m: http://www.springerlink.co m/content/hx02v172k65l63m3/- Kelsey, L.J. and R.J. Hardy, 1975. Driving o f motor vehicles as a risk factor for collieriesacute herniated lumbar intervertebral disc. American Journal o f Epidemio logy 102 (1): 63–73.Available fro m: www.ncbi.nlm.nih.gov/pubmed/1155438.- Kubo, M., F.Terauchi and H. Aoki, 2001. An investigation into a synthetic vibration mode lfor humans: an investigation into a mechanical vibration human model constructed accordingto the relations between the physical, psycho logical and physio logical reactions o f humansexposed to vibration. Int. J. Ind. Ergon. 27:219–232.Available fro m: http://www.sciencedirect.com/science/article/pii/S0169814100000524.- Mertens, H., 1978. Nonlinear behavior of sitting humans under increasing gravity. Aviation,Space, and Environmental Medicine, 49(2): 287-298.Available fro m: http://www.ncbi.nlm.nih.gov/pubmed/623596. 15
  16. 16. - Muksian, R., and C.D. J. Nash, 1976. On frequency dependent damping coefficients inlumped parameter models of human beings. Journal o f Bio mechanics, 9(5):339-342.Available fro m: http://www.sciencedirect.com/science/article/pii/0021929076900555.- Liu, X.X., J. Shi, and G.H. Li, 1998. Biodynamic response and injury estimation of shippersonnel to ship shock motion induced by underwater explosio n. Proceeding o f 69th Shockand Vibration Symposium, vo l. 18, St. Paul, pp: 1–18.- Patil, M.K., M.S. Palanichamy, and D.N.Ghista, 1977. Dynamic response o f human bodyseated on a tractor and effectiveness of suspensio n systems. SAE Paper 770932, pp: 755–792.Available fro m: http://papers.sae.org/770932/- Qassem, W., M.O. Othman, , S. Abdul-Majeed, 1994. The effects of vertical and horizonta lvibrations on the human body. Medical Engineering Physics, 16: 151–161.Available fro m: http://www.sciencedirect.com/science/article/pii/1350453394900280- Sandover, J. 1983. Dynamic loading as a possible source of low-back disorders. Spine 8(6):652-658.Available fro m: http://www.ncbi.nlm.nih.gov/pubmed/6228022.- Suggs, C.W., C.F. Abrams, and L.F. Stikeleather, 1969. Application o f a damped spring-mass human vibration simulator in vibration testing of vehicle seats. Ergonomics, 12, 79–90.Available fro m: http://www.tandfonline.co m/do i/abs/10.1080/00140136908931030.- Thelin, A. 1990. Hip jo int arthrosis: an occupational disorder among farmers. Am. J. Ind.Med., 18:339–343.Available fro m: http://onlinelibrary.wile y.co m/do i/10.1002/ajim.4700180316/pdf.- Wan, Y. and J.M. Schimmels, 1995. A simple model that captures the essential dynamics o fa seated human exposed to whole body vibration. Advances in Bioengineering, ASME, BED31: 333–334. 16
  17. 17. - Wilder, D. G., B. B.Woodworth, J. W. Frymoyer, and M. H. Pope, 1982. Vibration and thehuman spine. Spine 7(3), 243-254.Available fro m:http://www.ncbi.nlm.nih.gov/pubmed/6214030. 17

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