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Automobile seat comfort prediction: statistical model vs. artificial neural network
ARTICLE IN PRESS
Applied Ergonomics 35 (2004) 275–284
Automobile seat comfort prediction: statistical model vs. artificial neural network M. Kolich*, N. Seal, S. Taboun Department of Industrial & Manufacturing Systems Engineering, University of Windsor, Windsor, Ont., Canada N9B-3P4 Received 20 January 2003; accepted 28 January 2004
Abstract The current automobile seat comfort development process, which is executed in a trial and error fashion, is expensive and outdated. The prevailing thought is that process improvements are contingent upon the implementation of empirical/prediction models. In this context, seat-interface pressure measures, anthropometric characteristics, demographic information, and perceptions of seat appearance were related to an overall comfort index (which was a single score derived from a previously published 10-item survey with demonstrated levels of reliability and validity) using two distinct modeling approaches—stepwise, linear regression and artificial neural network. The purpose of this paper was to compare and contrast the resulting models. While both models could be used to adequately predict subjective perceptions of comfort, the neural network was deemed superior because it produced higher r2 values (0.832 vs. 0.713) and lower average error values (1.192 vs. 1.779). r 2004 Elsevier Ltd. All rights reserved. Keywords: Automobile seat; Comfort; Neural network
1. Introduction The typical approach to automobile seat comfort development is to first select a target from the appropriate vehicle segment. The target, which is usually a competitive vehicle, is selected through the joint efforts of engineering, marketing, and program management. One of the primary considerations is positive consumer comfort ratings. Next, the target seat is obtained and benchmarked. As part of this exercise a subjective evaluation is performed. This involves an extended duration ride and drive with a highly structured survey, which directs occupants to assign feelings of discomfort to specific regions of the seat. The nature of the subjective evaluation methodology makes it necessary to investigate the opinions of large groups of occupants in order to determine the impact of various design characteristics on perceived seating comfort (Manenica and Corlett, 1973). Nevertheless, this feedback, in terms of likes and dislikes *Corresponding author. Johnson Controls, Inc., Automotive Systems Group, 49200 Halyard Drive, Plymouth, MI 48170, USA. Tel.: +1-734-254-5911; fax: +1-734-254-6277. E-mail address: [email protected] (M. Kolich). 0003-6870/$ - see front matter r 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.apergo.2004.01.007
attributable to the seat design, is used to drive comfort development for the remainder of the program. That is, prototypes are built and evaluated using the same subjective evaluation approach. The subjective ratings obtained from the prototype seat are compared to the subjective ratings obtained from the target seat. Design decisions are taken to address the shortcomings of the prototype seat. This process continues until the prototype seat exceeds the comfort level offered by the target seat. Any improvement, no matter how slight, is considered success. The purported strength of this process lies in the A to B comparison of seats. Since a typical seat program takes 3–4 years to execute (it is not uncommon for a program to require 15 separate subjective evaluations), by the time the product is launched, it is slightly more comfortable than the best seat in the market 3–4 years ago (assuming that the design team was successful). From the perspective of providing design direction, seat system design teams struggle with subjective evaluations because, while offering credible evaluations in terms of face validity, the output is poor in terms of experimental rigor. This creates a scenario in which prototypes built to address specific comfort issues (i.e. those arising from one set of subjective evaluations) fail
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to produce the expected results in future subjective evaluations. That is, design decisions appear suspect/ faulty even though they were appropriate given the available data. The current trial and error approach to seat comfort development is inefficient and outdated. It is extremely time consuming (the excessively long development time hinders advances in comfort), expensive, and prone to measurement error (associated with reliability and validity (described in the next paragraph). These limitations could in some ways be justified if the process could guarantee a comfortable seat. This is, unfortunately, not the case. Since good seats are an exception and not a rule, the seat comfort development process needs to be augmented with more efficient and modern evaluation techniques. Measurement error associated with subjective evaluations can, at least, partly be addressed through survey design. Given the automotive seating industry’s propensity to use subjective evaluations, it is surprising that so little attention has been paid to the quantitative aspects of survey design and analysis. This lack of attention is best demonstrated through the shortage of published literature pertaining to this topic (noteworthy exceptions include Reed et al., 1991; Shen and Parsons, 1997; Kolich, 1999). A survey should not be used as the basis for design decisions unless it: (1) provides consistent ratings and is relatively free of random error (reliable); and (2) reflects exactly what the seat system design team intends to measure (valid). To satisfy these criteria, the survey must be designed with special emphasis on the wording of the survey items, the type and number of rating scale categories, the verbal tags associated with the categories, the method of quantification, and the interest and motivation of the respondent (as a function of survey length). Kolich (1999) has published such a survey to be used for automobile seat comfort development. It represents a significant improvement over the surveys included as part of traditional seat comfort development processes because it has proven levels of test-retest reliability, internal consistency, criterion-related validity, and constructrelated validity (refer to Kolich (1999) for details). The survey can also be reduced to a single, overall comfort index (OCI), which minimizes the bias (e.g. vehicle nameplate, type of trim material (cloth vs. leather), etc.) thought to plague seat comfort ratings (Kolich, 1999). Recent advances in seat comfort evaluation technologies can reduce the time and expense associated with subjective evaluations, which stems from both scheduling/organizing the subjective evaluation and building the required prototypes, if, and only if, the output from the advance (i.e. objective measure) can be linked to perceptions of comfort. Examples of objective measures include electromyography (Bush et al., 1995; Lee and Ferraiuolo, 1993; Sheridan et al., 1991), disc pressure
measurement (Andersson et al., 1974), vibration transmissibility (Ebe and Griffin, 2000), and microclimate at the occupant–seat interface (Diebschlag et al., 1988). One of the better-developed approaches is based on pressure measurement at the occupant–seat interface (Hertzberg, 1972; Kohara and Sugi, 1972; Chow and Odell, 1978; Kamijo et al., 1982; Bader et al. 1986; Diebschlag et al., 1988). Some basic research regarding the interpretation of pressure distribution profiles has already been conducted (beginning with Akerblom (1948) and extending to Reed et al. (1991) and Park and Kim (1997)). The implication is that pressure distribution characteristics correspond to perceptions of comfort. The ideal solution, from an automobile seat development viewpoint, is to formalize the implication through a model that can be used to predict subjective perceptions of comfort from quantitative measures. In this regard, the previously described OCI makes for an ideal model output. There are several modeling approaches available. In practice, stepwise, multiple, linear regression is the most common. Artificial neural networks are another option. They have not been applied as extensively by ergonomists and human factors professionals. Neural networks take previously solved examples and look for patterns, learn these patterns, and develop the ability to correctly classify new patterns (i.e. provide forecasts/predictions). The basic building block of neural network technology is the simulated neuron. Independent neurons are of little use, however, unless they are interconnected in a network of neurons. The network processes a number of inputs from the outside world to produce an output (i.e. the network’s predictions). The neurons are connected by weights and grouped into layers by their association to the outside world. For example, if a neuron receives data from outside of the network, it is considered to be in the input layer. If a neuron contains the network’s predictions, it is in the output layer. Neurons in between the input and output layers are in the hidden layer, which serve to: (1) add non-linearity to the system; and (2) address interactions between input variables. There can be many hidden layers (i.e. many levels of nonlinearity and many interactions). It should, at this point, be stated that the addition of hidden layers is only one of many available neural network based approaches. The interested reader is referred to Chen (1996), Gately (1996), Caudill and Butler (1990), and Goldberg (1989) for additional detail. The current automobile seat comfort development process is replete with shortcomings. The common belief is that seat system design teams need more efficient and modern evaluation techniques. One alternative may take the form of prediction models relating comfort ratings to seat-interface pressure measures, anthropometric characteristics, demographic information, and perceptions of seat appearance (the belief is that occupants are
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more likely to provide a positive seat comfort rating if the seat is aesthetically pleasing (Branton, 1969)). The purpose of this paper is to compare and contrast two distinct types of modeling approaches; namely stepwise, multiple, linear regression and artificial neural network.
2. Method Five different front driver bucket seats, representing a range of good and bad seats (based on seat comfort ratings provided by J.D. Power & Associates (1997)), were obtained from local rental agencies. Only seats from the compact car segment were selected. This decision was based on the assumption that seats from the same segment have comparable H-Point to heel point relationships (i.e. similar packages/environments). The H-Point (a) establishes the intended driving/riding position of each seat, (b) has X, Y, and Z coordinates relative to the designed vehicle structure, and (c) simulates the position of the pivot center of the human torso and thigh, along with associated angles (for more detail the interested reader is referred to the Society of Automotive Engineers (1995)). The seats were base level (i.e. cloth with manual track and recliner). Both the subjective and objective evaluations were conducted in vehicle (as opposed to in laboratory). The vehicles, each designed by a different manufacturer, were white with gray interior (1997 model year). This precaution was taken to minimize the effect of color preferences. The procedure began by obtaining anthropometric measurements (i.e. standing height and body mass) in a self-report fashion from 12 occupants. The occupants were volunteers of working age with no health problems. Demographics and anthropometry were held constant by using the same 12 occupants for all five seats (repeated measures experimental design). The demographic and anthropometric details are included in Table 1. For modeling purposes, females were assigned a zero and males were assigned a one. Based on an anthropometric data published by Gordon et al. (1989), it can be said that the sample spanned a broad range of the population (in terms of percentiles), which is important for occupant accommodation. Gender (SEX), standing height (SH), and body mass (BM) were considered model inputs. The seat-interface pressure technology included thin, flexible sensor arrays (manufactured and supplied by Tekscan). The sensors featured a grid-work of 48 columns and 44 rows based on 10 mm centers. At each of the 2112 intersection points on the grid, a sensing cell is created. An electrical resistance inversely proportional to the pressure applied relative to the cell’s surface characterizes each sensing cell. By scanning the grid and measuring the electrical resistance at each grid point, the pressure distribution on the sensor’s surface can be
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Table 1 Demographic and anthropometric characteristics of occupants evaluating five different front driver bucket seats using both subjective and objective methods Subject
Gender
Standing Percentile Body Percentile height (cm) mass (kg)
1 2 3 4 5 6 7 8 9 10 11 12 Mean STD
Female Male Male Female Male Female Female Male Female Male Female Male
176 189 198 179 189 178 153 175 154 172 152 164 173 15
90 98 99 99 98 99 5 45 10 30 5 3 57 44
55 132 105 73 82 73 61 79 64 85 73 61 78 21
20 99 98 90 65 90 50 55 60 75 90 5 66 30
determined. The scanning electronics are packaged in a handle assembly that clips onto the sensor’s interface tab and provides the electrical connection to each sensing cell. The sensor arrays (also known as mats) were calibrated prior to each seat evaluation according to the instructions outlined by Tekscan, Inc. (1996). The seat cushion and seatback were fitted with the calibrated mats. These mats were securely attached to the seat using strips of masking tape. Care was exercised to ensure that the mats were placed in a consistent location from occupant-to-occupant and seat-to-seat. Provisions included: (1) lining up the center of the mat with the mid-point of the head restraint rods; and (2) tucking the mats into the biteline, which is defined as the region where the cushion and seatback converge. Occupants were not permitted to sit in the seat (on top of the mats) until they removed their wallets and belts. This was done to avoid false seat-interface pressure readings. Each occupant was allowed to adjust the track position and the seatback angle. In this study, there were no other seat features to adjust. The preferred setting was called ‘‘occupant selected seat position’’ or ‘‘comfort position’’. Recall that seats from the same market segment were selected because they were thought to have comparable H-Point to heel point relationships (i.e. similar packages/environments). Coupled with the fact that the same 12 participants were used for all five seats, occupant preferred seat position was expected to be similar between seats. This would not be true if, for example, van seats were compared to sport car seats (these two market segments represent vastly different packages/environments/H-Point to heel point relationships (van seats are typically much higher with more upright seatback angles)). Once set, the pressure distribution was recorded. The system software then
ARTICLE IN PRESS Too much
Too firm
& & & & & & & & & Uncomfortable Uncomfortable Uncomfortable Cushion H. Ischial/buttocks comfort I. Thigh comfort J. Cushion lateral comfort
& & &
& &
&
Too much & &
&
Too much & &
&
Too much & & &
& & & & & & & & & & & & & & & & & & & & & Too little Uncomfortable Too little Uncomfortable Too little Uncomfortable Too soft Seatback A. Amount of lumbar support B. Lumbar comfort C. Amount of mid-back support D. Mid-back comfort E. Amount of back lateral support F. Back lateral support G. Seat back feel/firmness
& & & & & & &
3 2 3 Item
2
1
Just right
1
& &
The mats were removed and the occupant was asked to re-enter the seat in order to complete the survey without interference from the mats (the survey, which was adopted from Kolich (1999), is shown in Table 2). It should, at this point, be stated that some occupants completed the appearance-rating item (a five-point scale—5 is best) prior to sitting in the seat while others completed the item after exiting the seat. There was no standard procedure outlined for when occupants were to complete the appearance-rating item. The reason should be obvious—it is difficult for occupants to rate the appearance of the seat if they are sitting in it. The risk associated with failing to control this experimental detail was judged to be minimal. The remainder of the survey was designed so that occupants who were satisfied with the comfort or support being assessed by a particular item would mark the ‘‘just right’’ box, which, in the ensuing analysis, corresponded to a score of zero. Most of the items could be rated from 3 to +3. To obtain a single score from the survey, the absolute deviation of each item from just right was summed. Therefore, the closer the score was to zero, the more comfortable the occupant. The worst-case score was 30. This score was considered an OCI. In terms of model development, the appearance rating (AR) was considered an input while the OCI was considered an output. The entire procedure took approximately 30 min to complete (per occupant). Each seat evaluation was completed within 1 day. There was a 1-month delay between seat evaluations owing to the fact that the seat evaluation was only one part of a much larger investigative protocol applied to each vehicle. Although the process was not truly randomized, occupants were not tested in any particular order (i.e. the order was definitely not consistent from seat-to-seat). Descriptive statistics, together with a one-way ANOVA (degree of rigor was set to 0.05), were used to determine which of the metrics (subjective and objective) could be used to distinguish between seats. For the one-way ANOVA, the independent variable was seat (5 levels) and the dependent variables were (1) OCI, (2) AR, and (3) eight seat-interface pressure measures. This was necessary in order to demonstrate that the differences between the selected seats could be quantified through the adopted protocol.
&
*
&
*
1
*
Overall seat appearance
*
Good, slight improvements needed 4
*
Fair, minor improvements needed 3
*
Poor, major improvements needed 2
*
cushion contact area (cm2)—CCA; cushion total force (N)—CTF; cushion load at the center of force (N/cm2)—CCF; cushion peak pressure (N/cm2)—CPP; seatback contact area (cm2)—BCA; seatback total force (N)—BTF; seatback load at the center of force (N/cm2)—BCF seatback peak pressure (N/cm2)—BPP.
Table 2 Reliable and valid survey used to collect subjective data—adopted from Kolich (1999)
*
&
produced the following objective measures, which served as model inputs:
5
World class seat
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Stop! start over
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Next, 45 of 60 (12 occupants 5 seats) examples were randomly selected (i.e. 75% of the total data set). This sample, referred to as the training set, was used to develop two types of models. The first was based on a stepwise, multiple, linear regression approach. The stepwise selection criteria used for the model development were: (1) probability-of-F-to-enter=0.05 and (2) probability-of-F-to-remove=0.10. These were default criteria in SPSS, Inc.’s (2000) statistical software package. The second was a neural network developed with the help of NeuroShells Predictor (commercial computer software available through Ward Systems Group, Inc. (1997)). As part of this process, input neurons were created for every input variable and an output neuron was created for the output variable (i.e. OCI). In the context of traditional statistical modeling, this is synonymous with identifying independent and dependent variables. When the value of an input variable is fed into an input neuron, the neuron is activated, along with its links to other neurons. All of the input neurons are first directly linked to the output neuron (linear relationship). Weight values are assigned to the links, which indicate the strength of the connection. After the preliminary relationships are found, neurons are added to the hidden layer so that nonlinear relationships can be found. Input values in the first layer are multiplied by the weights and passed to the second (hidden) layer. Neurons in the hidden layer produce outputs that are based upon the sum of weighted values passed to them. The hidden layer passes values to the output layer in the same fashion, and the output layer produces the desired results (predictions). The neural network ‘learns’ by adjusting the interconnection weights between layers. The neural network’s predictions are repeatedly compared with the correct answers, and each time the connecting weights are adjusted slightly in the direction of the correct answers. Additional hidden neurons are added as necessary to capture features in the data set. Eventually, if the problem can be learned, a stable set of weights evolves and will produce good answers for all of the sample decisions or predictions. The real power of neural networks is evident when the trained network is able to produce good results for data that the network has not previously encountered. The specific architecture of the neural network, in terms of the number of hidden neurons, must be determined. Too many hidden neurons can hinder the neural network’s ability to generalize to data not encountered during training, which is referred to as over-fitting. Too few can cripple the neural network’s ability to learn the relationships at hand. Both models were assessed using standard statistics. The primary goal was to maximize the r2 value and minimize the average error for the training set, while avoiding over-fitting (which would compromise validity
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as indicated by a poor cross-validated r). The remaining 25% of the total data, referred to as the test set, was used for validation. For the sake of completeness, average errors for the test set were also computed.
3. Results Prior to modeling, it was necessary to determine whether the adopted protocol could be used to distinguish between seats. If, given the selected metrics, all the seats were the same, then the creation of models would be unnecessary. Along these lines, descriptive statistics (Table 3), together with the one-way ANOVA (Table 4), were used to demonstrate that a few of the identified measures could be used to distinguish between seats. Specifically, OCI differences were statistically significant. The post hoc results (Table 5) revealed that all comparison were different with the exception of Seat B (mean=10.3) and Seat D (mean=8.6). Seat C (mean=2.3) was the most comfortable. Similarly, AR differences were statistically significant (Table 4). The post hoc results (Table 5) suggested that Seat B (mean=2.8) was among the least aesthetically pleasing, while Seat C (mean=4.4) was among the most. In terms of seat-interface pressure measures, the results revealed that only CPP and BCF could be used to quantitatively distinguish between seats (i.e. the differences were statistically significant—Table 4). Considering the post hoc results for CPP (Table 5), Seat C (mean =1.5 N/ cm2) was different than Seat E (mean=0.8 N/cm2), whereas, the post hoc results for BCF (Table 5) suggested that Seat B (mean=0.2 N/cm2) and Seat C (mean=0.2 N/cm2) were different than Seat E (mean=0.4 N/cm2). The relationship between each of the 12 input variables (i.e. SEX, SH, BM, AR, CCA, CTF, CCF, CPP, BCA, BTF, BCF, and BPP) and the OCI (output variable representing subjective perceptions of comfort) was examined using Pearson product moment correlation coefficients (Table 6). Only three of the variables (AR, BCF, and CPP) were statistically related to the OCI at the 0.05 level. The fact that three input variables were linearly related to the OCI suggested that a viable linear model could be developed. Not only were the majority of relationships nonlinear, they were non-quadratic. This became apparent when the input variables were plotted against the OCI (scatter plots) in a separate exercise. This was a surprising result, particularly with respect to the seatinterface pressure measures. One would expect an optimal amount of CCA, for example, with too much or too little CCA being comparably detrimental. Since the one-to-one relationships were not as straightforward as one would hope, the data were assumed to be beset with several important interactions and/or complicated
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Table 3 Descriptive statistics for overall comfort index, appearance rating, and seat interface pressure measures Seat
OCI
AR
CCA (cm2)
CTF (N)
CCF (N/cm2)
CPP (N/cm2)
BCA (cm2)
BTF (N)
BCF (N/cm2)
BPP (N/cm2)
A
Mean STD Min Max
6.0 2.2 2 11
3.8 0.7 2.5 4.5
1717 113 1585 1967
598 160 377 1010
0.3 0.2 0.0 0.7
1.1 0.3 0.7 1.6
1318 191 1086 1653
273 74 192 422
0.3 0.2 0.0 0.5
0.7 0.3 0.5 1.3
B
Mean STD Min Max
10.3 1.9 7 13
2.8 0.6 2.0 4.0
1699 122 1494 1964
588 194 367 1066
0.3 0.1 0.0 0.4
1.2 0.5 0.6 2.4
1338 248 990 1896
240 74 137 363
0.2 0.1 0.0 0.4
0.7 0.2 0.4 1.0
C
Mean STD Min Max
2.3 1.1 1 4
4.4 0.6 3.0 5.0
1746 112 1623 2002
697 172 537 1186
0.2 0.2 0.0 0.5
1.5 0.7 0.6 3.4
1342 281 850 1908
277 108 140 518
0.2 0.1 0.0 0.4
1.1 0.9 0.4 2.9
D
Mean STD Min Max
8.6 1.3 6 10
3.8 1 2.5 5.0
1630 119 1494 1917
564 155 359 958
0.3 0.2 0.1 0.5
1.1 0.3 0.6 1.7
1219 183 1005 1711
250 88 126 451
0.3 0.1 0.1 0.5
0.7 0.2 0.5 1.0
E
Mean STD Min Max
12.8 1.4 10 15
3.2 0.7 2.0 4.5
1725 117 1494 1948
579 149 424 970
0.2 0.1 0.0 0.4
0.9 0.3 0.5 1.6
1358 254 953 1978
322 116 154 557
0.4 0.1 0.1 0.6
0.7 0.2 0.5 1.1
Total
Mean STD Min Max
8 4 1 15
3.6 0.9 2.0 5.0
1703 120 1494 2002
605 168 359 1186
0.3 0.1 0.0 0.7
1.2 0.5 0.5 3.4
1315 232 850 1978
272 95 126 557
0.3 0.1 0.0 0.6
0.8 0.5 0.4 2.9
levels of non-linearity. This triggered the application of neural network technology. As mentioned, 75% of the total sample was randomly selected and used to develop the stepwise, multiple, linear regression model and the neural network. The final linear model can be expressed as follows: OCI ¼ 13:749 2:038 AR þ 6:32314 BCF 0:99796 CPP þ 0:01046 BTF 0:0204 CTF þ 0:133 BM: The model explained 71.3% of the variance in OCI with an average error of 1.8 (Table 7). This can be contrasted with the final neural network, which contained 31 hidden neurons. This number produced the maximum r2 values and the minimum average error values for both the training and test sets. The neural network explained 83.2% of the variance in OCI with an average error of 1.2 (Table 7). The remainder of the total sample (i.e. 25%) was used for validation purposes. In this regard, both models resulted in a significant relationship between the actual and predicted OCI ratings (as demonstrated through the high crossvalidated r-values and low average error values—Table 7). Both models were considered valid.
In addition to predicting the output for the test set, the models were used to determine which variables were most effective at estimating the OCI. This information provided a relative measure of the significance of each input variable (in terms of its ability to predict the output). Weights could range from zero to one. Higher values were associated with more important variables (inputs). Since the sum of all importance values was approximately one, the importance values were thought of as the percent contribution to the model (Table 8).
4. Discussion As stated, the automobile seat design process proceeds by comparison with existing models (i.e. targets). From a comfort perspective, seats evolve only because the design objective is to exceed the performance of the target. Even marginal improvement is considered success. Rarely is statistical significance considered. It should also be noted that, often times, the process fails. For these reasons, automobile seat comfort has not improved/evolved substantially over the last decade. The prevailing thought is that prediction models may
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Table 4 One-way ANOVA for seat-interface pressure measure differences between seats Sum of squares
df
Mean square
F
Sig.
Overall Comfort Index
Between seats Within seats Total
780.267 143.667 923.933
4 55 59
19.067 2.612
74.677
0.000
Appearance Rating
Between seats Within seats Total
18.400 29.146 47.546
4 55 59
4.600 0.530
8.680
0.000
CCA
Between seats Within seats Total
94,346.798 749,382.925 843,729.723
4 55 59
23586.699 13625.144
1.731
0.156
CTF
Between seats Within seats Total
133,067.292 1,531,350.961 1,664,418.253
4 55 59
33266.823 27842.745
1.195
0.323
CCF
Between seats Within seats Total
554.233 12,613.500 13,167.733
4 55 59
138.558 229.336
0.604
0.661
CPP
Between seats Within seats Total
30,635.136 115,966.372 146,601.507
4 55 59
7658.784 2108.479
3.632
0.011
BCA
Between seats Within seats Total
147,976.804 3,020,261.389 3,168,238.193
4 55 59
36994.201 54913.843
0.674
0.613
BTF
Between seats Within seats Total
47,893.236 480,967.674 528,860.910
4 55 59
11973.309 8744.867
1.369
0.257
BCF
Between seats Within seats Total
3436.433 9584.500 13,020.933
4 55 59
859.108 174.264
4.930
0.002
BPP
Between seats Within seats Total
18,360.978 110,814.024 129,175.002
4 55 59
4590.244 2014.800
2.278
0.072
ameliorate this situation by helping seat system design teams understand, earlier in the development process, how to exceed the comfort level offered by the target. The new found efficiency may (1) create shorter product life cycles or (2) provide design teams with more time to develop and integrate innovative ideas/solutions for comfort enhancement. In either case, seat comfort will advance at a faster rate. This research found that both an artificial neural network and a stepwise, multiple linear regression model could be used to adequately predict subjective perceptions of comfort. The decision regarding which model to advocate was, first and foremost, based on r2 values and error estimates derived from the training set. The secondary consideration was to avoid over-fitting, which would compromise validity as indicated by a poor crossvalidated r (derived from the test set). The neural
network was deemed superior to the regression model because, while still validating (i.e. generalizing well), it explained more of the variance in OCI with lower average error. The neural network’s ability to deal with interaction effects is offered as the principle reason for its superior performance. The neural network, which considers a larger number of inputs, is also more useful to seat system design teams because constraints (which are an inevitable part of the design process) are less limiting. That is, the more options design teams have for how to improve comfort, the better. Note also that the regression model categorizes occupants according to only body mass. The neural network considers body mass, standing height, and gender. For this reason the neural network is capable of determining how a particular set of inputs will affect a more focused subset of a target population.
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Table 5 Post-hoc (Tukey’s honestly significant difference test) in support of one-way ANOVA (I)Seat
(J) Seat
Overall comfort index A B C D E
Mean difference (IJ)
Sig.
4.25 3.75 2.58 6.75
0.00 0.00 0.00 0.00
(I)Seat CPP A
(J) Seat
Mean difference (IJ)
Sig.
B C D E
12.44 46.02 2.08 22.49
0.96 0.12 1.00 0.75
B
A C D E
4.25 8.00 1.67 2.50
0.00 0.00 0.10 0.00
B
A C D E
12.44 33.58 14.53 34.93
0.96 0.39 0.94 0.35
C
A B D E
3.75 8.00 6.33 10.50
0.00 0.00 0.00 0.00
C
A B D E
46.02 33.58 48.10 68.51
0.12 0.39 0.09 0.01
D
A B C E
2.58 1.67 6.63 4.17
0.00 0.10 0.00 0.00
D
A B C E
2.08 14.53 48.10 20.41
1.00 0.94 0.09 0.81
E
A B C D
6.75 2.50 10.50 4.17
0.00 0.00 0.00 0.00
E
A B C D
22.49 34.93 68.51 20.41
0.75 0.35 0.01 0.81
Appearance rating A B C D E
0.96 0.63 0.04 0.63
0.02 0.23 1.00 0.23
B C D E
7.92 9.08 3.17 11.50
0.59 0.45 0.98 0.22
BCF A
B
A C D E
0.96 1.58 1.00 0.33
0.02 0.00 0.01 0.79
B
A C D E
7.92 1.17 11.08 19.42
0.59 1.00 0.25 0.01
C
A B D E
0.63 1.58 0.58 1.25
0.23 0.00 0.30 0.00
C
A B D E
9.08 1.17 12.25 20.58
0.45 1.00 0.17 0.00
D
A B C E
0.04 1.00 0.58 0.67
1.00 0.01 0.30 0.18
D
A B C E
3.17 11.08 12.25 8.33
0.98 0.25 0.17 0.54
E
A B C D
0.63 0.33 1.25 0.67
0.23 0.79 0.00 0.18
E
A B C D
11.50 19.42 20.58 8.33
0.22 0.01 0.00 0.54
Based on both models, CTF was the most important predictor of OCI. This raises an interesting discussion topic in that CTF was not one of the pressure measures found to distinguish between seats (i.e. all five seats had similar CTFs). The explanation lies in the fact that occupants, within specific seats, tended to be more comfortable when there was high CTF. Design teams
interested in improving comfort should, therefore, focus on this variable. It is somewhat intuitive to suggest that CTF can be increased through the provision of a more upright recline angle. As the recline angle becomes more upright, more of the occupant’s mass is taken up by the cushion and less by the seatback. In practice, this type of design solution would require a change in H-Point
ARTICLE IN PRESS M. Kolich et al. / Applied Ergonomics 35 (2004) 275–284 Table 6 Relationship between predictor variables and overall comfort index (n ¼ 60) Predictor variable
r
p
Gender—SEX Standing height (cm)—HT Body mass (kg)—BM Appearance rating—AR Cushion contact area (cm2)—CCA Cushion total force (N)—CTF Cushion load at the center of force (N/cm2)—CCF Cushion peak pressure (N/cm2)—CPP Seatback contact area (cm2)—BCA Seatback total force(N)-BTF Seatback load at the center of force (N/cm2)—BCF Seatback peak pressure (N/cm2)—BPP
0.076 0.163 0.031 0.645 0.181 0.219 0.032 0.381 0.062 0.203 0.505 0.201
0.562 0.213 0.817 0.000 0.166 0.092 0.808 0.003 0.635 0.119 0.000 0.124
Table 7 Comparison of model performance Performance statistics
Stepwise, linear regression
Artificial neural network
Model development r2 Average error
0.713 1.8
0.832 1.2
Model validation Cross-validated-r Average error
r (15)=0.952, p ¼ 0 0.5
r (15)=0.847, p ¼ 0 0.7
(recall that the H-Point establishes the intended driving/ riding position of each seat (i.e. design position)—this includes the torso angle). It was also interesting to note that AR was related to OCI. Although correlation does not imply causality, automobile seat design studios would, almost definitely, be interested in knowing that perceptions of seat appearance are related to perceptions of seat comfort. This finding substantiates the claim originally made by Branton (1969). Having established that a neural network can be used to predict automobile seat comfort, future research should focus on the derivation of an optimized set of inputs (i.e. a set of inputs that will result in maximum comfort). There are many optimization algorithms available for this purpose. Also, future investigations should broaden the scope of the models to include seats from other market segments (i.e. in addition to compact car) and other seating positions (i.e. in addition to front driver). The assumption is that expectations of automobile seat comfort vary by market segment (compare the compact car consumer to the luxury car consumer) and seating position (compare passengers to drivers, which are much more constrained). Once completed, the findings could be published in the form of seat comfort design guidelines/standards.
283
Table 8 Comparison of relative importance of input variables Stepwise linear regression SEX HT BM AR CCA CTF CCF CPP BCA BTF BCF BPP
0.261 0.161 0.335 0.050 0.100 0.093
Artificial neural network 0.009 0.203 0.018 0.024 0.002 0.658 0.011 0.012 0.034 0.010 0.008 0.011
Perceptions of comfort are constantly changing. A comfortable automobile seat from 1970, for example, would probably not be considered comfortable today. This suggests that prediction models, in order to remain useful, will need to be periodically updated. Consumer researchers, at both the vehicle manufacturer and seat supplier levels, will have a vital role to play in specifying the frequency of these updates. In all applications, comfort degrades with time on task (Zhang et al., 1996). A recent article by Helander (2003) found that all seats, even those that conform to some basic set of ergonomic criteria, decrease comfort. This was attributed to the physiological aspects of sitting for extended durations rather than the seat design. So while comfort will decrease over time, the rank order of preference among a set of seats will not change over time. Said another way, occupants can reliably assess comfort immediately. It can be argued that this immediate evaluation (sometimes referred to as static or showroom comfort) is what sells automobiles. The present investigation was focused on this type of comfort. Having said this, it would still be valuable, as part of future research, to understand the time dependency associated with seat-interface pressure measures. Unfortunately, with the current state of technology, this will not be easy. Today’s pressure sensors are obtrusive (i.e. they may, themselves, cause the occupant to modify his/her posture or to influence comfort directly), impractical to use when driving, adversely affected by the seated occupant over a long drive, and cumbersome from a data management perspective (Gyi et al., 1998). There is, in short, a technological divide, which represents a product opportunity. This technological divide may be why Porter et al. (2003), who considered driving comfort a dynamic phenomenon, were unsuccessful in relating subjective perceptions of comfort to seat-interface pressure characteristics.
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References Akerblom, B., 1948. Standing and sitting posture with special reference to the construction of chairs. Unpublished Doctoral Dissertation, Karolinska Institute, A.B. Nordiska Bokhandeln, Stockholm, Sweden. Andersson, B.J.G., Ortengren, R., Nachemson, A., Elfstrom, G., 1974. Lumbar disc pressure and myoelectric back muscle activity during sitting. IV. Studies on a car driver’s seat. Scand. J. Rehabil. Med. 6, 128–133. Bader, D.L., Barnhill, R.L., Ryan, T.J., 1986. Effect of externally applied skin surface forces on tissue vascularization. Arch. Phys. Med. Rehabil. 67 (11), 807–811. Branton, P., 1969. Behavior, body mechanics and discomfort. Ergonomics 12, 316–327. Bush, T.R., Mills, F.T., Thakurta, K., Hubbard, R.P., Vorro, J., 1995. The use of electromyography for seat assessment and comfort evaluation. Technical Paper No. 950143, Society of Automotive Engineers, Inc., Warrendale, PA, USA. Caudill, M., Butler, C., 1990. Naturally Intelligent Systems. The MIT Press, Cambridge, MA, USA. Chen, C.H., 1996. Fuzzy Logic and Neural Network Handbook. McGraw-Hill, New York. Chow, W.W., Odell, E.I., 1978. Deformations and stresses in soft body tissues of a sitting person. J. Biomech. Eng. 100, 79–87. Diebschlag, W., Heidinger, F., Kuurz, B., Heiberger, R., 1988. Recommendation for ergonomic and climatic physiological vehicle seat design. Technical Paper No. 880055, Society of Automotive Engineers, Inc., Warrendale, PA, USA. Ebe, K., Griffin, M.J., 2000. Qualitative models of seat discomfort including static and dynamic factors. Ergonomics 43 (6), 771–790. Gately, E., 1996. Neural Networks for Financial Forecasting. Wiley, New York. Goldberg, D., 1989. Genetic Algorithms in Search, Optimization & Machine Learning. Addison-Wesley, Reading, MA, USA. Gordon, C.C., Churchill, T., Clauser, C.E., Bradtmiller, B., McConville, J.T., Tebbetts, I., Walker, R.A., 1989. 1988 Anthropometric survey of US army personnel: methods and summary statistics. Technical Report NATICK/TR-89/044, Anthropology Research Project, Yellow Springs, OH, USA. Gyi, D.E., Porter, J.M., Robertson, N.K.B., 1998. Seat pressure measurement technologies: consideration for their evaluation. Appl. Ergon. 27 (2), 85–91. Helander, M.G., 2003. Forget about ergonomics in chair design? Focus on aesthetics and comfort!. Ergonomics 46, 1306–1319. Hertzberg, H.T.E., 1972. The human buttocks in sitting: pressures, patterns, and palliatives. Technical Paper No. 72005, Society of Automotive Engineers, Inc., New York.
J.D. Power & Associates, 1997. 1997 Seat Quality Report. J.D. Power & Associates, Agoura Hills, CA. Kamijo, K., Tsujimura, H., Obara, H., Katsumat, M., 1982. Evaluation of seating comfort. Technical Paper No. 820761, Society of Automotive Engineers, Inc., Warrendale, PA. Kohara, J., Sugi, T., 1972. Development of biomechanical manikins for measuring seat comfort. Technical Paper No. 720006, Society of Automotive Engineers Inc., New York, NY. Kolich, M., 1999. Reliability and validity of an automobile seat comfort survey. Technical Paper No. 990132, Society of Automotive Engineers Inc., Warrendale, PA, USA. Lee, J., Ferraiuolo, P., 1993. Seat comfort. Technical Paper No. 931005, Society of Automotive Engineers Inc., Warrendale, PA, USA. Manenica, I., Corlett, E.N., 1973. Model of vehicle comfort and a model for its assessment. Ergonomics 11 (6), 849–854. Park, S.J., Kim, C.B., 1997. The evaluation of seating comfort by the objective measures. Technical Paper No. 970595, Society of Automotive Engineers, Inc., Warrendale, PA, USA. Porter, J.M., Gyi, D.E., Tait, H.A., 2003. Interface pressure data and the prediction of driver discomfort in road trials. Appl. Ergon. 34, 207–214. Reed, M.P., Saito, M., Kakishima, Y., Lee, N.S., Schneider, L.W., 1991. An investigation of driver discomfort and related seat design factors in extended-duration driving. SAE Technical Paper No. 910117, Society of Automotive Engineers, Inc., Warrendale, PA, USA. Shen, W., Parsons, K.C., 1997. Validity and reliability of rating scales for seated pressure distribution. Int. J. Ind. Ergon. 20, 441–461. Sheridan, T.B., Meyer, J.E., Roy, S.H., Decker, K.S., Yanagishima, T., Kishi, Y., 1991. Physiological and psychological evaluations of driver fatigue during long term driving. SAE Technical Paper No. 910116, Society of Automotive Engineers, Inc., Warrendale, PA, USA. Society of Automotive Engineers, 1995. Surface vehicle standard. Devices for use in defining and measuring vehicle seating accommodation, SAE J826. Issued 1962-11 Revised 1995-07. Society of Automotive Engineers, Inc., Warrendale, Palestinians, USA. SPSS, Inc., 2000. SPSS for Windows (release 10.1.0). Computer software. Tekscan, Inc., 1996. Tekscan body pressure distribution measurement system (Version 3.843). Computer software. Ward Systems Group, Inc., 1997. NeuroShells Predictor (Release 2.0). Computer software. Zhang, L., Helander, M.G., Drury, C.G., 1996. Identifying factors of comfort and discomfort in sitting. Hum. Factors 38 (3), 377–389.
Applied Ergonomics 35 (2004) 275–284
Automobile seat comfort prediction: statistical model vs. artificial neural network M. Kolich*, N. Seal, S. Taboun Department of Industrial & Manufacturing Systems Engineering, University of Windsor, Windsor, Ont., Canada N9B-3P4 Received 20 January 2003; accepted 28 January 2004
Abstract The current automobile seat comfort development process, which is executed in a trial and error fashion, is expensive and outdated. The prevailing thought is that process improvements are contingent upon the implementation of empirical/prediction models. In this context, seat-interface pressure measures, anthropometric characteristics, demographic information, and perceptions of seat appearance were related to an overall comfort index (which was a single score derived from a previously published 10-item survey with demonstrated levels of reliability and validity) using two distinct modeling approaches—stepwise, linear regression and artificial neural network. The purpose of this paper was to compare and contrast the resulting models. While both models could be used to adequately predict subjective perceptions of comfort, the neural network was deemed superior because it produced higher r2 values (0.832 vs. 0.713) and lower average error values (1.192 vs. 1.779). r 2004 Elsevier Ltd. All rights reserved. Keywords: Automobile seat; Comfort; Neural network
1. Introduction The typical approach to automobile seat comfort development is to first select a target from the appropriate vehicle segment. The target, which is usually a competitive vehicle, is selected through the joint efforts of engineering, marketing, and program management. One of the primary considerations is positive consumer comfort ratings. Next, the target seat is obtained and benchmarked. As part of this exercise a subjective evaluation is performed. This involves an extended duration ride and drive with a highly structured survey, which directs occupants to assign feelings of discomfort to specific regions of the seat. The nature of the subjective evaluation methodology makes it necessary to investigate the opinions of large groups of occupants in order to determine the impact of various design characteristics on perceived seating comfort (Manenica and Corlett, 1973). Nevertheless, this feedback, in terms of likes and dislikes *Corresponding author. Johnson Controls, Inc., Automotive Systems Group, 49200 Halyard Drive, Plymouth, MI 48170, USA. Tel.: +1-734-254-5911; fax: +1-734-254-6277. E-mail address: [email protected] (M. Kolich). 0003-6870/$ - see front matter r 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.apergo.2004.01.007
attributable to the seat design, is used to drive comfort development for the remainder of the program. That is, prototypes are built and evaluated using the same subjective evaluation approach. The subjective ratings obtained from the prototype seat are compared to the subjective ratings obtained from the target seat. Design decisions are taken to address the shortcomings of the prototype seat. This process continues until the prototype seat exceeds the comfort level offered by the target seat. Any improvement, no matter how slight, is considered success. The purported strength of this process lies in the A to B comparison of seats. Since a typical seat program takes 3–4 years to execute (it is not uncommon for a program to require 15 separate subjective evaluations), by the time the product is launched, it is slightly more comfortable than the best seat in the market 3–4 years ago (assuming that the design team was successful). From the perspective of providing design direction, seat system design teams struggle with subjective evaluations because, while offering credible evaluations in terms of face validity, the output is poor in terms of experimental rigor. This creates a scenario in which prototypes built to address specific comfort issues (i.e. those arising from one set of subjective evaluations) fail
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to produce the expected results in future subjective evaluations. That is, design decisions appear suspect/ faulty even though they were appropriate given the available data. The current trial and error approach to seat comfort development is inefficient and outdated. It is extremely time consuming (the excessively long development time hinders advances in comfort), expensive, and prone to measurement error (associated with reliability and validity (described in the next paragraph). These limitations could in some ways be justified if the process could guarantee a comfortable seat. This is, unfortunately, not the case. Since good seats are an exception and not a rule, the seat comfort development process needs to be augmented with more efficient and modern evaluation techniques. Measurement error associated with subjective evaluations can, at least, partly be addressed through survey design. Given the automotive seating industry’s propensity to use subjective evaluations, it is surprising that so little attention has been paid to the quantitative aspects of survey design and analysis. This lack of attention is best demonstrated through the shortage of published literature pertaining to this topic (noteworthy exceptions include Reed et al., 1991; Shen and Parsons, 1997; Kolich, 1999). A survey should not be used as the basis for design decisions unless it: (1) provides consistent ratings and is relatively free of random error (reliable); and (2) reflects exactly what the seat system design team intends to measure (valid). To satisfy these criteria, the survey must be designed with special emphasis on the wording of the survey items, the type and number of rating scale categories, the verbal tags associated with the categories, the method of quantification, and the interest and motivation of the respondent (as a function of survey length). Kolich (1999) has published such a survey to be used for automobile seat comfort development. It represents a significant improvement over the surveys included as part of traditional seat comfort development processes because it has proven levels of test-retest reliability, internal consistency, criterion-related validity, and constructrelated validity (refer to Kolich (1999) for details). The survey can also be reduced to a single, overall comfort index (OCI), which minimizes the bias (e.g. vehicle nameplate, type of trim material (cloth vs. leather), etc.) thought to plague seat comfort ratings (Kolich, 1999). Recent advances in seat comfort evaluation technologies can reduce the time and expense associated with subjective evaluations, which stems from both scheduling/organizing the subjective evaluation and building the required prototypes, if, and only if, the output from the advance (i.e. objective measure) can be linked to perceptions of comfort. Examples of objective measures include electromyography (Bush et al., 1995; Lee and Ferraiuolo, 1993; Sheridan et al., 1991), disc pressure
measurement (Andersson et al., 1974), vibration transmissibility (Ebe and Griffin, 2000), and microclimate at the occupant–seat interface (Diebschlag et al., 1988). One of the better-developed approaches is based on pressure measurement at the occupant–seat interface (Hertzberg, 1972; Kohara and Sugi, 1972; Chow and Odell, 1978; Kamijo et al., 1982; Bader et al. 1986; Diebschlag et al., 1988). Some basic research regarding the interpretation of pressure distribution profiles has already been conducted (beginning with Akerblom (1948) and extending to Reed et al. (1991) and Park and Kim (1997)). The implication is that pressure distribution characteristics correspond to perceptions of comfort. The ideal solution, from an automobile seat development viewpoint, is to formalize the implication through a model that can be used to predict subjective perceptions of comfort from quantitative measures. In this regard, the previously described OCI makes for an ideal model output. There are several modeling approaches available. In practice, stepwise, multiple, linear regression is the most common. Artificial neural networks are another option. They have not been applied as extensively by ergonomists and human factors professionals. Neural networks take previously solved examples and look for patterns, learn these patterns, and develop the ability to correctly classify new patterns (i.e. provide forecasts/predictions). The basic building block of neural network technology is the simulated neuron. Independent neurons are of little use, however, unless they are interconnected in a network of neurons. The network processes a number of inputs from the outside world to produce an output (i.e. the network’s predictions). The neurons are connected by weights and grouped into layers by their association to the outside world. For example, if a neuron receives data from outside of the network, it is considered to be in the input layer. If a neuron contains the network’s predictions, it is in the output layer. Neurons in between the input and output layers are in the hidden layer, which serve to: (1) add non-linearity to the system; and (2) address interactions between input variables. There can be many hidden layers (i.e. many levels of nonlinearity and many interactions). It should, at this point, be stated that the addition of hidden layers is only one of many available neural network based approaches. The interested reader is referred to Chen (1996), Gately (1996), Caudill and Butler (1990), and Goldberg (1989) for additional detail. The current automobile seat comfort development process is replete with shortcomings. The common belief is that seat system design teams need more efficient and modern evaluation techniques. One alternative may take the form of prediction models relating comfort ratings to seat-interface pressure measures, anthropometric characteristics, demographic information, and perceptions of seat appearance (the belief is that occupants are
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more likely to provide a positive seat comfort rating if the seat is aesthetically pleasing (Branton, 1969)). The purpose of this paper is to compare and contrast two distinct types of modeling approaches; namely stepwise, multiple, linear regression and artificial neural network.
2. Method Five different front driver bucket seats, representing a range of good and bad seats (based on seat comfort ratings provided by J.D. Power & Associates (1997)), were obtained from local rental agencies. Only seats from the compact car segment were selected. This decision was based on the assumption that seats from the same segment have comparable H-Point to heel point relationships (i.e. similar packages/environments). The H-Point (a) establishes the intended driving/riding position of each seat, (b) has X, Y, and Z coordinates relative to the designed vehicle structure, and (c) simulates the position of the pivot center of the human torso and thigh, along with associated angles (for more detail the interested reader is referred to the Society of Automotive Engineers (1995)). The seats were base level (i.e. cloth with manual track and recliner). Both the subjective and objective evaluations were conducted in vehicle (as opposed to in laboratory). The vehicles, each designed by a different manufacturer, were white with gray interior (1997 model year). This precaution was taken to minimize the effect of color preferences. The procedure began by obtaining anthropometric measurements (i.e. standing height and body mass) in a self-report fashion from 12 occupants. The occupants were volunteers of working age with no health problems. Demographics and anthropometry were held constant by using the same 12 occupants for all five seats (repeated measures experimental design). The demographic and anthropometric details are included in Table 1. For modeling purposes, females were assigned a zero and males were assigned a one. Based on an anthropometric data published by Gordon et al. (1989), it can be said that the sample spanned a broad range of the population (in terms of percentiles), which is important for occupant accommodation. Gender (SEX), standing height (SH), and body mass (BM) were considered model inputs. The seat-interface pressure technology included thin, flexible sensor arrays (manufactured and supplied by Tekscan). The sensors featured a grid-work of 48 columns and 44 rows based on 10 mm centers. At each of the 2112 intersection points on the grid, a sensing cell is created. An electrical resistance inversely proportional to the pressure applied relative to the cell’s surface characterizes each sensing cell. By scanning the grid and measuring the electrical resistance at each grid point, the pressure distribution on the sensor’s surface can be
277
Table 1 Demographic and anthropometric characteristics of occupants evaluating five different front driver bucket seats using both subjective and objective methods Subject
Gender
Standing Percentile Body Percentile height (cm) mass (kg)
1 2 3 4 5 6 7 8 9 10 11 12 Mean STD
Female Male Male Female Male Female Female Male Female Male Female Male
176 189 198 179 189 178 153 175 154 172 152 164 173 15
90 98 99 99 98 99 5 45 10 30 5 3 57 44
55 132 105 73 82 73 61 79 64 85 73 61 78 21
20 99 98 90 65 90 50 55 60 75 90 5 66 30
determined. The scanning electronics are packaged in a handle assembly that clips onto the sensor’s interface tab and provides the electrical connection to each sensing cell. The sensor arrays (also known as mats) were calibrated prior to each seat evaluation according to the instructions outlined by Tekscan, Inc. (1996). The seat cushion and seatback were fitted with the calibrated mats. These mats were securely attached to the seat using strips of masking tape. Care was exercised to ensure that the mats were placed in a consistent location from occupant-to-occupant and seat-to-seat. Provisions included: (1) lining up the center of the mat with the mid-point of the head restraint rods; and (2) tucking the mats into the biteline, which is defined as the region where the cushion and seatback converge. Occupants were not permitted to sit in the seat (on top of the mats) until they removed their wallets and belts. This was done to avoid false seat-interface pressure readings. Each occupant was allowed to adjust the track position and the seatback angle. In this study, there were no other seat features to adjust. The preferred setting was called ‘‘occupant selected seat position’’ or ‘‘comfort position’’. Recall that seats from the same market segment were selected because they were thought to have comparable H-Point to heel point relationships (i.e. similar packages/environments). Coupled with the fact that the same 12 participants were used for all five seats, occupant preferred seat position was expected to be similar between seats. This would not be true if, for example, van seats were compared to sport car seats (these two market segments represent vastly different packages/environments/H-Point to heel point relationships (van seats are typically much higher with more upright seatback angles)). Once set, the pressure distribution was recorded. The system software then
ARTICLE IN PRESS Too much
Too firm
& & & & & & & & & Uncomfortable Uncomfortable Uncomfortable Cushion H. Ischial/buttocks comfort I. Thigh comfort J. Cushion lateral comfort
& & &
& &
&
Too much & &
&
Too much & &
&
Too much & & &
& & & & & & & & & & & & & & & & & & & & & Too little Uncomfortable Too little Uncomfortable Too little Uncomfortable Too soft Seatback A. Amount of lumbar support B. Lumbar comfort C. Amount of mid-back support D. Mid-back comfort E. Amount of back lateral support F. Back lateral support G. Seat back feel/firmness
& & & & & & &
3 2 3 Item
2
1
Just right
1
& &
The mats were removed and the occupant was asked to re-enter the seat in order to complete the survey without interference from the mats (the survey, which was adopted from Kolich (1999), is shown in Table 2). It should, at this point, be stated that some occupants completed the appearance-rating item (a five-point scale—5 is best) prior to sitting in the seat while others completed the item after exiting the seat. There was no standard procedure outlined for when occupants were to complete the appearance-rating item. The reason should be obvious—it is difficult for occupants to rate the appearance of the seat if they are sitting in it. The risk associated with failing to control this experimental detail was judged to be minimal. The remainder of the survey was designed so that occupants who were satisfied with the comfort or support being assessed by a particular item would mark the ‘‘just right’’ box, which, in the ensuing analysis, corresponded to a score of zero. Most of the items could be rated from 3 to +3. To obtain a single score from the survey, the absolute deviation of each item from just right was summed. Therefore, the closer the score was to zero, the more comfortable the occupant. The worst-case score was 30. This score was considered an OCI. In terms of model development, the appearance rating (AR) was considered an input while the OCI was considered an output. The entire procedure took approximately 30 min to complete (per occupant). Each seat evaluation was completed within 1 day. There was a 1-month delay between seat evaluations owing to the fact that the seat evaluation was only one part of a much larger investigative protocol applied to each vehicle. Although the process was not truly randomized, occupants were not tested in any particular order (i.e. the order was definitely not consistent from seat-to-seat). Descriptive statistics, together with a one-way ANOVA (degree of rigor was set to 0.05), were used to determine which of the metrics (subjective and objective) could be used to distinguish between seats. For the one-way ANOVA, the independent variable was seat (5 levels) and the dependent variables were (1) OCI, (2) AR, and (3) eight seat-interface pressure measures. This was necessary in order to demonstrate that the differences between the selected seats could be quantified through the adopted protocol.
&
*
&
*
1
*
Overall seat appearance
*
Good, slight improvements needed 4
*
Fair, minor improvements needed 3
*
Poor, major improvements needed 2
*
cushion contact area (cm2)—CCA; cushion total force (N)—CTF; cushion load at the center of force (N/cm2)—CCF; cushion peak pressure (N/cm2)—CPP; seatback contact area (cm2)—BCA; seatback total force (N)—BTF; seatback load at the center of force (N/cm2)—BCF seatback peak pressure (N/cm2)—BPP.
Table 2 Reliable and valid survey used to collect subjective data—adopted from Kolich (1999)
*
&
produced the following objective measures, which served as model inputs:
5
World class seat
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Stop! start over
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Next, 45 of 60 (12 occupants 5 seats) examples were randomly selected (i.e. 75% of the total data set). This sample, referred to as the training set, was used to develop two types of models. The first was based on a stepwise, multiple, linear regression approach. The stepwise selection criteria used for the model development were: (1) probability-of-F-to-enter=0.05 and (2) probability-of-F-to-remove=0.10. These were default criteria in SPSS, Inc.’s (2000) statistical software package. The second was a neural network developed with the help of NeuroShells Predictor (commercial computer software available through Ward Systems Group, Inc. (1997)). As part of this process, input neurons were created for every input variable and an output neuron was created for the output variable (i.e. OCI). In the context of traditional statistical modeling, this is synonymous with identifying independent and dependent variables. When the value of an input variable is fed into an input neuron, the neuron is activated, along with its links to other neurons. All of the input neurons are first directly linked to the output neuron (linear relationship). Weight values are assigned to the links, which indicate the strength of the connection. After the preliminary relationships are found, neurons are added to the hidden layer so that nonlinear relationships can be found. Input values in the first layer are multiplied by the weights and passed to the second (hidden) layer. Neurons in the hidden layer produce outputs that are based upon the sum of weighted values passed to them. The hidden layer passes values to the output layer in the same fashion, and the output layer produces the desired results (predictions). The neural network ‘learns’ by adjusting the interconnection weights between layers. The neural network’s predictions are repeatedly compared with the correct answers, and each time the connecting weights are adjusted slightly in the direction of the correct answers. Additional hidden neurons are added as necessary to capture features in the data set. Eventually, if the problem can be learned, a stable set of weights evolves and will produce good answers for all of the sample decisions or predictions. The real power of neural networks is evident when the trained network is able to produce good results for data that the network has not previously encountered. The specific architecture of the neural network, in terms of the number of hidden neurons, must be determined. Too many hidden neurons can hinder the neural network’s ability to generalize to data not encountered during training, which is referred to as over-fitting. Too few can cripple the neural network’s ability to learn the relationships at hand. Both models were assessed using standard statistics. The primary goal was to maximize the r2 value and minimize the average error for the training set, while avoiding over-fitting (which would compromise validity
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as indicated by a poor cross-validated r). The remaining 25% of the total data, referred to as the test set, was used for validation. For the sake of completeness, average errors for the test set were also computed.
3. Results Prior to modeling, it was necessary to determine whether the adopted protocol could be used to distinguish between seats. If, given the selected metrics, all the seats were the same, then the creation of models would be unnecessary. Along these lines, descriptive statistics (Table 3), together with the one-way ANOVA (Table 4), were used to demonstrate that a few of the identified measures could be used to distinguish between seats. Specifically, OCI differences were statistically significant. The post hoc results (Table 5) revealed that all comparison were different with the exception of Seat B (mean=10.3) and Seat D (mean=8.6). Seat C (mean=2.3) was the most comfortable. Similarly, AR differences were statistically significant (Table 4). The post hoc results (Table 5) suggested that Seat B (mean=2.8) was among the least aesthetically pleasing, while Seat C (mean=4.4) was among the most. In terms of seat-interface pressure measures, the results revealed that only CPP and BCF could be used to quantitatively distinguish between seats (i.e. the differences were statistically significant—Table 4). Considering the post hoc results for CPP (Table 5), Seat C (mean =1.5 N/ cm2) was different than Seat E (mean=0.8 N/cm2), whereas, the post hoc results for BCF (Table 5) suggested that Seat B (mean=0.2 N/cm2) and Seat C (mean=0.2 N/cm2) were different than Seat E (mean=0.4 N/cm2). The relationship between each of the 12 input variables (i.e. SEX, SH, BM, AR, CCA, CTF, CCF, CPP, BCA, BTF, BCF, and BPP) and the OCI (output variable representing subjective perceptions of comfort) was examined using Pearson product moment correlation coefficients (Table 6). Only three of the variables (AR, BCF, and CPP) were statistically related to the OCI at the 0.05 level. The fact that three input variables were linearly related to the OCI suggested that a viable linear model could be developed. Not only were the majority of relationships nonlinear, they were non-quadratic. This became apparent when the input variables were plotted against the OCI (scatter plots) in a separate exercise. This was a surprising result, particularly with respect to the seatinterface pressure measures. One would expect an optimal amount of CCA, for example, with too much or too little CCA being comparably detrimental. Since the one-to-one relationships were not as straightforward as one would hope, the data were assumed to be beset with several important interactions and/or complicated
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Table 3 Descriptive statistics for overall comfort index, appearance rating, and seat interface pressure measures Seat
OCI
AR
CCA (cm2)
CTF (N)
CCF (N/cm2)
CPP (N/cm2)
BCA (cm2)
BTF (N)
BCF (N/cm2)
BPP (N/cm2)
A
Mean STD Min Max
6.0 2.2 2 11
3.8 0.7 2.5 4.5
1717 113 1585 1967
598 160 377 1010
0.3 0.2 0.0 0.7
1.1 0.3 0.7 1.6
1318 191 1086 1653
273 74 192 422
0.3 0.2 0.0 0.5
0.7 0.3 0.5 1.3
B
Mean STD Min Max
10.3 1.9 7 13
2.8 0.6 2.0 4.0
1699 122 1494 1964
588 194 367 1066
0.3 0.1 0.0 0.4
1.2 0.5 0.6 2.4
1338 248 990 1896
240 74 137 363
0.2 0.1 0.0 0.4
0.7 0.2 0.4 1.0
C
Mean STD Min Max
2.3 1.1 1 4
4.4 0.6 3.0 5.0
1746 112 1623 2002
697 172 537 1186
0.2 0.2 0.0 0.5
1.5 0.7 0.6 3.4
1342 281 850 1908
277 108 140 518
0.2 0.1 0.0 0.4
1.1 0.9 0.4 2.9
D
Mean STD Min Max
8.6 1.3 6 10
3.8 1 2.5 5.0
1630 119 1494 1917
564 155 359 958
0.3 0.2 0.1 0.5
1.1 0.3 0.6 1.7
1219 183 1005 1711
250 88 126 451
0.3 0.1 0.1 0.5
0.7 0.2 0.5 1.0
E
Mean STD Min Max
12.8 1.4 10 15
3.2 0.7 2.0 4.5
1725 117 1494 1948
579 149 424 970
0.2 0.1 0.0 0.4
0.9 0.3 0.5 1.6
1358 254 953 1978
322 116 154 557
0.4 0.1 0.1 0.6
0.7 0.2 0.5 1.1
Total
Mean STD Min Max
8 4 1 15
3.6 0.9 2.0 5.0
1703 120 1494 2002
605 168 359 1186
0.3 0.1 0.0 0.7
1.2 0.5 0.5 3.4
1315 232 850 1978
272 95 126 557
0.3 0.1 0.0 0.6
0.8 0.5 0.4 2.9
levels of non-linearity. This triggered the application of neural network technology. As mentioned, 75% of the total sample was randomly selected and used to develop the stepwise, multiple, linear regression model and the neural network. The final linear model can be expressed as follows: OCI ¼ 13:749 2:038 AR þ 6:32314 BCF 0:99796 CPP þ 0:01046 BTF 0:0204 CTF þ 0:133 BM: The model explained 71.3% of the variance in OCI with an average error of 1.8 (Table 7). This can be contrasted with the final neural network, which contained 31 hidden neurons. This number produced the maximum r2 values and the minimum average error values for both the training and test sets. The neural network explained 83.2% of the variance in OCI with an average error of 1.2 (Table 7). The remainder of the total sample (i.e. 25%) was used for validation purposes. In this regard, both models resulted in a significant relationship between the actual and predicted OCI ratings (as demonstrated through the high crossvalidated r-values and low average error values—Table 7). Both models were considered valid.
In addition to predicting the output for the test set, the models were used to determine which variables were most effective at estimating the OCI. This information provided a relative measure of the significance of each input variable (in terms of its ability to predict the output). Weights could range from zero to one. Higher values were associated with more important variables (inputs). Since the sum of all importance values was approximately one, the importance values were thought of as the percent contribution to the model (Table 8).
4. Discussion As stated, the automobile seat design process proceeds by comparison with existing models (i.e. targets). From a comfort perspective, seats evolve only because the design objective is to exceed the performance of the target. Even marginal improvement is considered success. Rarely is statistical significance considered. It should also be noted that, often times, the process fails. For these reasons, automobile seat comfort has not improved/evolved substantially over the last decade. The prevailing thought is that prediction models may
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Table 4 One-way ANOVA for seat-interface pressure measure differences between seats Sum of squares
df
Mean square
F
Sig.
Overall Comfort Index
Between seats Within seats Total
780.267 143.667 923.933
4 55 59
19.067 2.612
74.677
0.000
Appearance Rating
Between seats Within seats Total
18.400 29.146 47.546
4 55 59
4.600 0.530
8.680
0.000
CCA
Between seats Within seats Total
94,346.798 749,382.925 843,729.723
4 55 59
23586.699 13625.144
1.731
0.156
CTF
Between seats Within seats Total
133,067.292 1,531,350.961 1,664,418.253
4 55 59
33266.823 27842.745
1.195
0.323
CCF
Between seats Within seats Total
554.233 12,613.500 13,167.733
4 55 59
138.558 229.336
0.604
0.661
CPP
Between seats Within seats Total
30,635.136 115,966.372 146,601.507
4 55 59
7658.784 2108.479
3.632
0.011
BCA
Between seats Within seats Total
147,976.804 3,020,261.389 3,168,238.193
4 55 59
36994.201 54913.843
0.674
0.613
BTF
Between seats Within seats Total
47,893.236 480,967.674 528,860.910
4 55 59
11973.309 8744.867
1.369
0.257
BCF
Between seats Within seats Total
3436.433 9584.500 13,020.933
4 55 59
859.108 174.264
4.930
0.002
BPP
Between seats Within seats Total
18,360.978 110,814.024 129,175.002
4 55 59
4590.244 2014.800
2.278
0.072
ameliorate this situation by helping seat system design teams understand, earlier in the development process, how to exceed the comfort level offered by the target. The new found efficiency may (1) create shorter product life cycles or (2) provide design teams with more time to develop and integrate innovative ideas/solutions for comfort enhancement. In either case, seat comfort will advance at a faster rate. This research found that both an artificial neural network and a stepwise, multiple linear regression model could be used to adequately predict subjective perceptions of comfort. The decision regarding which model to advocate was, first and foremost, based on r2 values and error estimates derived from the training set. The secondary consideration was to avoid over-fitting, which would compromise validity as indicated by a poor crossvalidated r (derived from the test set). The neural
network was deemed superior to the regression model because, while still validating (i.e. generalizing well), it explained more of the variance in OCI with lower average error. The neural network’s ability to deal with interaction effects is offered as the principle reason for its superior performance. The neural network, which considers a larger number of inputs, is also more useful to seat system design teams because constraints (which are an inevitable part of the design process) are less limiting. That is, the more options design teams have for how to improve comfort, the better. Note also that the regression model categorizes occupants according to only body mass. The neural network considers body mass, standing height, and gender. For this reason the neural network is capable of determining how a particular set of inputs will affect a more focused subset of a target population.
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Table 5 Post-hoc (Tukey’s honestly significant difference test) in support of one-way ANOVA (I)Seat
(J) Seat
Overall comfort index A B C D E
Mean difference (IJ)
Sig.
4.25 3.75 2.58 6.75
0.00 0.00 0.00 0.00
(I)Seat CPP A
(J) Seat
Mean difference (IJ)
Sig.
B C D E
12.44 46.02 2.08 22.49
0.96 0.12 1.00 0.75
B
A C D E
4.25 8.00 1.67 2.50
0.00 0.00 0.10 0.00
B
A C D E
12.44 33.58 14.53 34.93
0.96 0.39 0.94 0.35
C
A B D E
3.75 8.00 6.33 10.50
0.00 0.00 0.00 0.00
C
A B D E
46.02 33.58 48.10 68.51
0.12 0.39 0.09 0.01
D
A B C E
2.58 1.67 6.63 4.17
0.00 0.10 0.00 0.00
D
A B C E
2.08 14.53 48.10 20.41
1.00 0.94 0.09 0.81
E
A B C D
6.75 2.50 10.50 4.17
0.00 0.00 0.00 0.00
E
A B C D
22.49 34.93 68.51 20.41
0.75 0.35 0.01 0.81
Appearance rating A B C D E
0.96 0.63 0.04 0.63
0.02 0.23 1.00 0.23
B C D E
7.92 9.08 3.17 11.50
0.59 0.45 0.98 0.22
BCF A
B
A C D E
0.96 1.58 1.00 0.33
0.02 0.00 0.01 0.79
B
A C D E
7.92 1.17 11.08 19.42
0.59 1.00 0.25 0.01
C
A B D E
0.63 1.58 0.58 1.25
0.23 0.00 0.30 0.00
C
A B D E
9.08 1.17 12.25 20.58
0.45 1.00 0.17 0.00
D
A B C E
0.04 1.00 0.58 0.67
1.00 0.01 0.30 0.18
D
A B C E
3.17 11.08 12.25 8.33
0.98 0.25 0.17 0.54
E
A B C D
0.63 0.33 1.25 0.67
0.23 0.79 0.00 0.18
E
A B C D
11.50 19.42 20.58 8.33
0.22 0.01 0.00 0.54
Based on both models, CTF was the most important predictor of OCI. This raises an interesting discussion topic in that CTF was not one of the pressure measures found to distinguish between seats (i.e. all five seats had similar CTFs). The explanation lies in the fact that occupants, within specific seats, tended to be more comfortable when there was high CTF. Design teams
interested in improving comfort should, therefore, focus on this variable. It is somewhat intuitive to suggest that CTF can be increased through the provision of a more upright recline angle. As the recline angle becomes more upright, more of the occupant’s mass is taken up by the cushion and less by the seatback. In practice, this type of design solution would require a change in H-Point
ARTICLE IN PRESS M. Kolich et al. / Applied Ergonomics 35 (2004) 275–284 Table 6 Relationship between predictor variables and overall comfort index (n ¼ 60) Predictor variable
r
p
Gender—SEX Standing height (cm)—HT Body mass (kg)—BM Appearance rating—AR Cushion contact area (cm2)—CCA Cushion total force (N)—CTF Cushion load at the center of force (N/cm2)—CCF Cushion peak pressure (N/cm2)—CPP Seatback contact area (cm2)—BCA Seatback total force(N)-BTF Seatback load at the center of force (N/cm2)—BCF Seatback peak pressure (N/cm2)—BPP
0.076 0.163 0.031 0.645 0.181 0.219 0.032 0.381 0.062 0.203 0.505 0.201
0.562 0.213 0.817 0.000 0.166 0.092 0.808 0.003 0.635 0.119 0.000 0.124
Table 7 Comparison of model performance Performance statistics
Stepwise, linear regression
Artificial neural network
Model development r2 Average error
0.713 1.8
0.832 1.2
Model validation Cross-validated-r Average error
r (15)=0.952, p ¼ 0 0.5
r (15)=0.847, p ¼ 0 0.7
(recall that the H-Point establishes the intended driving/ riding position of each seat (i.e. design position)—this includes the torso angle). It was also interesting to note that AR was related to OCI. Although correlation does not imply causality, automobile seat design studios would, almost definitely, be interested in knowing that perceptions of seat appearance are related to perceptions of seat comfort. This finding substantiates the claim originally made by Branton (1969). Having established that a neural network can be used to predict automobile seat comfort, future research should focus on the derivation of an optimized set of inputs (i.e. a set of inputs that will result in maximum comfort). There are many optimization algorithms available for this purpose. Also, future investigations should broaden the scope of the models to include seats from other market segments (i.e. in addition to compact car) and other seating positions (i.e. in addition to front driver). The assumption is that expectations of automobile seat comfort vary by market segment (compare the compact car consumer to the luxury car consumer) and seating position (compare passengers to drivers, which are much more constrained). Once completed, the findings could be published in the form of seat comfort design guidelines/standards.
283
Table 8 Comparison of relative importance of input variables Stepwise linear regression SEX HT BM AR CCA CTF CCF CPP BCA BTF BCF BPP
0.261 0.161 0.335 0.050 0.100 0.093
Artificial neural network 0.009 0.203 0.018 0.024 0.002 0.658 0.011 0.012 0.034 0.010 0.008 0.011
Perceptions of comfort are constantly changing. A comfortable automobile seat from 1970, for example, would probably not be considered comfortable today. This suggests that prediction models, in order to remain useful, will need to be periodically updated. Consumer researchers, at both the vehicle manufacturer and seat supplier levels, will have a vital role to play in specifying the frequency of these updates. In all applications, comfort degrades with time on task (Zhang et al., 1996). A recent article by Helander (2003) found that all seats, even those that conform to some basic set of ergonomic criteria, decrease comfort. This was attributed to the physiological aspects of sitting for extended durations rather than the seat design. So while comfort will decrease over time, the rank order of preference among a set of seats will not change over time. Said another way, occupants can reliably assess comfort immediately. It can be argued that this immediate evaluation (sometimes referred to as static or showroom comfort) is what sells automobiles. The present investigation was focused on this type of comfort. Having said this, it would still be valuable, as part of future research, to understand the time dependency associated with seat-interface pressure measures. Unfortunately, with the current state of technology, this will not be easy. Today’s pressure sensors are obtrusive (i.e. they may, themselves, cause the occupant to modify his/her posture or to influence comfort directly), impractical to use when driving, adversely affected by the seated occupant over a long drive, and cumbersome from a data management perspective (Gyi et al., 1998). There is, in short, a technological divide, which represents a product opportunity. This technological divide may be why Porter et al. (2003), who considered driving comfort a dynamic phenomenon, were unsuccessful in relating subjective perceptions of comfort to seat-interface pressure characteristics.
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