neck kinematics in low-speed rear-end collisions

Accident Analysis and Prevention 31 (1999) 393 – 407

The influence of head restraint and occupant factors on peak head/neck kinematics in low-speed rear-end collisions Gunter P. Siegmund a,*, Bradley E. Heinrichs a, Jeffrey B. Wheeler b b

a MacInnis Engineering Associates, 11 -11151 Horseshoe Way, Richmond, BC V7A 4S5, Canada Biomechanics Research and Consulting, 840 Apollo Street, Suite 218, El Segundo, CA 90245 USA

Received 25 August 1998; received in revised form 2 December 1998

Abstract Prior two-way analyses of variance showed that the peak kinematic response of the head and neck of subjects exposed to low-speed rear-end collisions was related to speed change and gender, however potential reasons for this gender dependence were not determined. Using multiple linear regression, this study further examined these response data to determine the relative influence of specific factors, including subject anthropometry, neck strength, cervical range of motion, seated posture and head restraint position, which may have been responsible for the previously-observed gender dependence. The results of this analysis showed that vehicle speed change and relative head restraint position explained the largest proportion of the observed variation in peak occupant kinematic response. Seated posture measures also explained some of the variation in kinematic response. The current analysis prioritizes which variables to explore more thoroughly in future research and which variables should be carefully controlled in future studies. © 1999 Elsevier Science Ltd. All rights reserved. Keywords: Whiplash; Occupant kinematics; Head restraint; Gender; Anthropometry; Seated posture

1. Introduction After a low-speed rear-end automobile impact, occupants sometimes complain of symptoms commonly referred to as whiplash-associated disorders (WAD) (Spitzer et al., 1995). The exact mechanisms producing WAD remain unclear, although numerous factors that may influence the incidence or duration of WAD have been reported. These factors are generally grouped into three categories: seat factors, occupant factors and external factors (Viano and Gargan, 1995). Seat factors include seat and head restraint geometry, stiffness, strength and inclination; occupant factors include gender, anthropometry, seated posture and preparedness; and external factors include vehicle mass, vehicle stiffness, bumper design and collision severity (States and Balcerak, 1973; Foret-Bruno et al., 1991; Viano and Gargan, 1995). Many studies have attempted to draw a direct link between the production or duration of WAD and spe* Corresponding author. Tel.: +1-604-277-3040; fax: + 1-604-2773020. E-mail address: [email protected] (G.P. Siegmund)

cific seat, occupant, or external factors (see A in Fig. 1). With seat factors, for instance, a higher head restraint has been shown to reduce the incidence of neck injury (Nygren et al., 1985), and increased head restraint backset, defined as the horizontal gap between the back of the head and the front surface of the head restraint, has correlated significantly with increased neck symptom duration (Olsson et al., 1990). With occupant factors, a higher incidence of WAD has been reported in females than males (O’Neill et al., 1972; Balla, 1980; Kahane, 1982; Lo¨vsund et al., 1988; Otremski et al., 1989), and other occupant factors such as height (Carlsson et al., 1985), age (Otremski et al., 1989), preparedness and pre-impact posture (Sturzenegger et al., 1994), and seat belt use (Otremski et al., 1989; Maag et al., 1990) have also been observed to affect the incidence or duration of WAD. External factors such as collision speed change have been associated with initial measures of neck strain severity (Ryan et al., 1993). Fewer studies have addressed either the relationship between the seat, occupant, or external factors and the physical responses of occupants (see B in Fig. 1) or the relationship between the physical and clinical responses

0001-4575/99/$ - see front matter © 1999 Elsevier Science Ltd. All rights reserved. PII: S 0 0 0 1 - 4 5 7 5 ( 9 8 ) 0 0 0 7 7 - 3

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(see C in Fig. 1). Seat factors have been shown to have a larger effect on occupant kinematics than vehicle construction (an external factor) at a given collision speed change (Haland et al., 1996), and specific seat back and head restraint modifications have been shown to affect the kinematic and kinetic response of anthropomorphic test devices (ATD’s) (Svensson et al., 1996). Occupant kinematic response has also been shown to increase with collision speed change (Svensson et al., 1996; Siegmund et al., 1997). There is, however, limited information regarding the influence of occupant anthropometry, physiology and seated posture on the physical response of vehicle occupants. In this paper the relationship of some occupant and seat factors to the peak kinematic response data of 42 human subjects exposed to low-speed rear-end impacts was examined using multiple linear regression. A previous two-way analysis of variance (ANOVA) of these kinematic data found that peak amplitude and time to peak amplitude of some sagittal-plane kinematic response parameters varied significantly with gender (Siegmund et al., 1997), however, specific gender-based factors responsible for this correlation were not identified. This study examined inter-subject differences in anthropometry, neck strength and cervical range of motion to identify occupant factors that may be responsible for the previously-observed gender differences in peak kinematic response. Potentially confounding variables such as head restraint position (backset and height) and seated posture of the occupant immediately before impact were also incorporated into the analysis.

2. Methods Predictors used in the regression analyses are designated in capital letters where they are defined.

Fig. 1. Relationships between potentially influential factors and physical and clinical responses.

2.1. Subjects Ethics review and approval was obtained from the Western Institutional Review Board (Olympia, WA) regarding human subject protection policies and test procedures. Subjects were required to be between 20 and 40 years old (AGE), between the 10th and 90th percentile height for their gender and between the 10th and 90th percentile mass for their height (Najjar and Rowland, 1987). In addition, seated height was checked to ensure that a subject’s head and not their neck contacted the head restraint. This latter criterion eliminated subjects with an erect seated height of 96 cm or greater, the median seated height for a 90th percentile male (Diffrient et al., 1974). Subjects with a history of specific medical conditions and a prior or active injury claim were excluded. After obtaining informed consent, subjects underwent a magnetic resonance scan of their cervical spine. Subjects with a disc bulge \ 2 mm or degenerative findings deemed moderate or greater by a radiologist were excluded from the study. A total of 42 subjects (21 males and 21 females; GENDER) participated in the test procedure.

2.2. Instrumentation Head acceleration was measured using a nine accelerometer array (Kistler 8302B20S1; 920 g, Amherst, NY) arranged in a 3-2-2-2 configuration (Padgaokar et al., 1975). Torso acceleration was measured using a tri-axial accelerometer (Summit 34103A; 9 7.5 g, Akron, OH) and an angular rate sensor (ATASensors DynaCube; 9 100 rad/s, Albuquerque, NM). Both torso transducers were fastened to an aluminum plate, which was applied in the mid-sagittal plane to the chest immediately below the manubrium. Transducer data were acquired at 10 kHz and each data channel conformed to SAE J211, Channel Class 1000 (SAE, 1989). Signal conditioners onboard the vehicle were tethered to 12 bit, simultaneous-sample-and-hold data acquisition cards (Win30 DAQ cards, United Electronics, Watertown, MA). Digital video of sagittal plane motion was captured using an OmniSpeed HS motion capture system (Speed Vision Technologies, Solana Beach, CA) and highspeed camera (JCLabs 250; 512× 216 lines resolution, Mountain View, CA). Video data were recorded at 250 frames/s using a shutter speed of 1/1000 s. Video data were digitized using OmniSpeed AutoTracker software with a combined experimental setup and video system accuracy of 9 2 mm at the vertical plane containing the seat centerline. Vehicle speed change (SPEED) was measured with a 5th wheel (MacInnis Engineering Associates, Richmond, BC) attached to each vehicle. Fifth wheel data were recorded simultaneously for both vehicles at 128 Hz. All data acquisition systems were synchronized using a bumper contact switch.

G.P. Siegmund et al. / Accident Analysis and Pre6ention 31 (1999) 393–407

2.3. Test procedure Immediately after the informed consent interview, subject anthropometry and neck strength were measured. Standing height (HEIGHT) and mass (MASS) were measured using a standard scale (Health-O-Meter, Bedford Heights, OH). Head circumference (HEADCIR) was measured at the level of the glabella (most anterior protrusion of forehead) and opisthocranion (most posterior protrusion of back of head). Neck circumference (NECK-CIR) was measured at the midpoint of the neck, perpendicular to the long axis. Maximum isometric cervical flexion (MMT-FLEX) and extension (MMT-EXT) force were measured manually by a physical therapist using a MicroFET handheld dynamometer (Empi, St. Paul, MN). Maximum isometric cervical flexion force was measured with the subject supine on an examination table. The head and neck were in a neutral position and C7 was placed at the edge of the table so the head and neck projected over the edge. The dynamometer was centered on the glabella and the subject was instructed to maintain the head-neutral position while an increasing downward force was applied to the forehead through the dynamometer until the head-neutral position was broken. Maximum isometric cervical extension force was measured with the subject prone, positioned with the sternoclavicular joints at the edge of the table, the head and neck over the edge, and the dynamometer centered on the opisthocranion. Gravity-assisted positions ensured that all subjects’ maximum cervical force could be overcome by the physical therapist. Isometric cervical testing was conducted at least 48 h prior to the first impact test to ensure that potential transient symptoms produced by the maximal contractions did not confound symptoms produced by the impact. Cervical flexion, extension, right and left lateral flexion, right and left rotation, protraction, and retraction ranges of motion were measured with the cervical range of motion device (CROM, Performance Attainment Associates, St. Paul, MN). Angular motion in each plane was summed to produce one variable for flexion and extension (FLEX-EXT), left and right lateral flexion (LAT-FLEX) and left and right lateral rotation (LAT-ROT). Horizontal translation measurements of protraction (PRO) and retraction (RET) were treated separately. Range of motion measurements were made before each impact. Prior to each impact test, reflective targets for subsequent motion analysis were applied to the head over the glabella, left temporomandibular joint, left lateral aspect of the cranium and to the left side of the head accelerometer assembly. Torso targets were applied in the mid-sagittal plane to the chest accelerometry and over the spinous process of the seventh cervical vertebrae (C7). With a subject’s head stabilized in an op-

395

tometrist’s forehead rest, the positions of the head video targets, head accelerometry, and the Frankfort plane (defined by the external acoustic meati and lower rims of the orbits) were measured using a 3D digitizer (FaroArm B-08, Faro Technologies, Mary Lake, FL). Subjects were then seated in the front passenger seat of the test vehicle (the target vehicle) and restrained by a lap and shoulder seat belt. Subjects were instructed to sit normally in the seat, face forward with their head level, place their hands on their lap and to relax prior to impact. After being seated in the vehicle, the torso and head video targets and the torso accelerometry were digitized relative to specific vehicle and seat landmarks. An aligned collision between a rolling bullet vehicle and a stationary target vehicle was used for this study. Both vehicles were in neutral and their engines were not running. The bullet vehicle rolled down a ramp and its front bumper squarely struck the rear bumper of the unbraked target vehicle. After impact, the target vehicle rolled into gravel located 3 m ahead of the vehicle and stopped. Each subject was to be exposed to two impacts: one which produced a 4 km/h speed change on the target vehicle and another which produced an 8 km/h speed change. The order of impact presentation to each subject was randomized. In all cases, the two impacts were separated by at least seven symptom-free days. Because of the potential effect of pre-impact neck muscle contraction on kinematics, special attention was devoted to depriving the subjects of visual and aural cues of the impending impact and to ensuring subjects were relaxed before the impact. A black felt curtain separated the target vehicle from the bullet vehicle and other equipment to eliminate visual cues. Foam ear plugs and music were used to defeat aural cues. No test personnel were visible to the subject in the minutes preceding the impact. The relaxed state of the occupant immediately prior to impact was confirmed visually with a live feed from a video camera mounted on the A-pillar and by ensuring quiescent electromyographic (EMG) activity from the sternocleidomastoid and cervical para-spinal muscles bilaterally for at least 1 min prior to impact.

2.4. Vehicle specifications The bullet vehicle was a 1983 Volvo 240DL station wagon (mass 1618 kg) and the target vehicle was a 1990 Honda Accord LX 4-door sedan (mass 1414 kg). The bullet vehicle was in its stock condition. Although the target vehicle was altered to accommodate instrumentation and filming, the effect of these modifications on its response to impacts of the magnitude used here was negligible. The fore/aft adjustment of the right front seat was locked in the full-rear position, the seat back

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Fig. 2. Definition of postural angles and head restraint measures. The open circles with cross-hairs correspond to the estimated centre of mass of the head (upper) and C7–T1 joint axis (lower).

was angled rearward about 27° from the vertical, and the head restraint was adjusted and locked in the full-up position for all subjects. Detailed information regarding seat back modifications made to accommodate head restraint instrumentation has been presented elsewhere (Lawrence et al., 1997).

2.5. Data analysis A detailed explanation of the data treatment used to determine occupant kinematics has been given elsewhere (Siegmund et al., 1997). Briefly, the analysis was limited to sagittal plane kinematics. Acceleration and velocity were computed from the transducer data and position data were computed from the video record. All kinematic parameters were resolved into the global reference frame to one of two origins (Fig. 2) using assumptions of rigid body kinematics. The origin for the head kinematics was the estimated location of the center of mass for each subject (Clauser et al., 1969). The origin for the torso kinematics was the estimated location of the C7–T1 joint axis (Queisser et al., 1994). Nine kinematic parameters were calculated for both the head and upper torso origins: linear acceleration (ax, az ), velocity (6x, 6z ) and position (sx, sz ); and angular acceleration (ay ), velocity (vy ) and displacement (uy ). The relative kinematics of the head with respect to C7–T1, i.e. ((head response) — (C7 – T1 response)), were also calculated, bringing the total num-

ber of kinematic responses considered here to 27. The kinematic responses for all subjects were then examined for peaks common to all subjects. Peak amplitude (PA) and time-to-peak amplitude (TPA) were then determined for each peak within each subject’s data. Only peaks which occurred within 300 ms of impact were considered. Various measures of pre-impact occupant posture were determined from a combined analysis of the 3D digitizer data and the sagittal-plane position data drawn from the video frame immediately preceding impact (Fig. 2). The angle of the Frankfort plane relative to the horizontal was used to define the initial head angle (F-PLANE). The angle of the line joining the reflective targets of the manubrium and the C7 vertebrae relative to the horizontal was used to approximate the initial orientation of the base of the cervical spine (MC7). The distance from the C7–T1 joint axis to the head center of mass was used to estimate the length of the neck (NECK-LEN), and the angle of the line joining the C7–T1 joint axis and the head center of mass while seated in the vehicle defined the initial angle of the neck link (NECK-ANG). Although the seat back angle remained constant across all tests, each subject’s seated posture varied. The angle of the line joining the acromioclavicular joint and the greater trochanter relative to the vertical was used to define an occupant’s initial torso recline angle (RECLINE). The minimum horizontal distance from the front surface of the head restraint to the back of the head defined the initial head restraint backset (BACKSET), and the vertical distance from the top of the head restraint to the external acoustic meatus defined the initial head restraint height (HR-HEIGHT). In addition to the predictors defined above, one transformed predictor was also included. It has been hypothesized that the different injury reporting rates between genders may be related to a proportionally larger head mass to neck area ratio for females than males (States and Balcerak, 1973) and significantly weaker flexion and extension neck strength for females than males (Snyder et al., 1975). To evaluate this potential gender effect, an estimate of the head mass to neck cross-sectional area (HN-RATIO= (head circumference)3/(neck circumference)2) was also included as a potential predictor.

2.6. Statistical analysis The effect of specific occupant, head restraint position, and seated-posture factors (predictors Xi ) on peak kinematic response was examined using separate linear regressions for each PA and TPA kinematic response (Yj ). No interaction terms were examined. The best subset of predictors (p) for each response was chosen by examining all possible regressions and minimizing

G.P. Siegmund et al. / Accident Analysis and Pre6ention 31 (1999) 393–407

the mean square error between the actual and fitted values. The particular formulation used here was Cpminimization (Weisberg, 1995) using the method of Furnival and Wilson (1974). All potential predictors Xi except gender were examined for collinearities, and both predictors Xi and responses Yj were standardized prior to regression. Only the best Cp model for each kinematic parameter was used. The best Cp model for each kinematic parameter was determined to be significant if its r 2j was greater than a critical R 2j . The value of R 2j for each kinematic parameter was determined by exercising the model with actual predictors and 107 responses generated randomly from distributions with the same mean and covariance structure as the actual responses, ensuring no actual correlation between the predictors and the responses, and then examining the distribution of r 2 values for these random responses to determine the R 2j for a significance level of P =0.05. To ensure the probability of a false positive was less than 0.05 across all kinematic parameters, a Bonferonni adjustment was used (Devore, 1982). The adjusted P-value was also used to examine the significance of each predictor within a given model. The assumption of normality in the random measurement errors of each model was tested using the Shapiro–Wilk’s W-test (Shapiro and Wilks, 1965) as implemented by the Statistica v5.1 software (Statsoft, Tulsa, OK). The residuals for each regression were used as estimates of the random errors of that model. The same significance level (P B 0.05) and Bonferonni adjustment were used. 3. Results Data from 21 male and 21 female subjects were analyzed. Three subjects (1M, 2F) withdrew between their 4 and 8 km/h tests, yielding data from 81 collisions. High speed video data (position and angle data) were successfully acquired from all tests, whereas occasional sensor problems yielded 75 complete data sets of acceleration and velocity data. A total of 31 response peaks common to all subjects were observed (Fig. 3). This produced 31 responses for peak amplitude (PA) and 31 responses for time to peak-amplitude (TPA), resulting in a total of 62 separate linear regressions. A Bonferonni adjusted P-value of 0.0008 (0.05/62) was thus used to determine the R 2j to achieve model significance. The cross-correlation of potential predictors (Table 1) showed acceptable orthogonality between predictors for a regression analysis (Weisberg, 1995). Based on these cross correlations, six potential surrogates for GENDER were identified: MMT-EXT, NECK-CIR, MMT-FLEX, HEIGHT, HN-RATIO and HRHEIGHT, listed here in order of decreasing r 2.

397

The multiple linear regression analyses produced models that achieved significance (r 2j = R 2j ) for all PA models (Table 2) and all but one TPA model (Table 3). PA models contained a median of p= 8 predictors and TPA models contained a median of p= 7 predictors. The hypothesis of normally-distributed residuals (Wtest) was rejected for five models. Normal probability plots of the residuals for these five models revealed one or two outliers in each residual distribution. Removal of these outliers resulted in the hypothesis of normallydistributed residuals to be accepted. No one subject or group of subjects was responsible for these outliers. Collision speed change (SPEED) was present in all 61 models that reached significance, however its b-coefficient was only significantly different from zero in 56 of these models. A total of 55 of these models were the same as those found previously using a two-way ANOVA containing only SPEED and GENDER (Ta– T1 bles 2 and 3). The exception (a C7 TPA model) x achieved significance by a narrow margin, whereas it was rejected by a similarly small margin in the previous ANOVA. The effect of increasing SPEED was to increase peak magnitude and decrease the time between impact and peak magnitude across all models with – T1 significant SPEED b-coefficients (except a C7 x amplitude). GENDER was present in 21 models that reached significance, however its b-coefficient was only significantly different from zero in four PA models. None of the models previously identified as GENDER-dependent in the two-way ANOVA analysis contained significant GENDER coefficients (Tables 2 and 3). In the eight PA models previously identified as GENDER-dependent, HEIGHT and/or BACKSET were consistently present with significant b-coefficients, and GENDER was absent. MASS, HEAD-CIR, MMTEXT, RECLINE, MC7, and HR-HEIGHT were also present with significant b-coefficients in these models, but each appeared in only one or two of the eight PA models. In the eight TPA models which were previously identified as GENDER-dependent in the two-way ANOVA analysis, BACKSET was significant in seven, HR-HEIGHT was significant in four, and GENDER was absent. Of the remaining predictors, MMT-EXT appeared in one and RECLINE appeared in three of these eight models, together with BACKSET. In both PA and TPA models (Figure 4a and b), the most frequently present predictors, and most frequently present predictors with significant b-coefficients, were SPEED and BACKSET. Peak amplitude increased as BACKSET increased for all kinematic responses where BACKSET was significant, except peak horizontal head velocity (6 Head ). The time between impact and peak x kinematic response increased with BACKSET in each model where it was present.

398 G.P. Siegmund et al. / Accident Analysis and Pre6ention 31 (1999) 393–407 Fig. 3. Sample kinematic response data of a single subject undergoing a speed change of 8 km/h. Shown are the absolute sagittal-plane kinematics of the head and C7–T1, and the relative kinematics of the head relative to C7–T1. The 31 common response peaks are identified in this data set with open circles and labeled as follows: linear acceleration (a), linear velocity (6), linear displacements (s), angular acceleration (a), angular velocity (v), and angular displacement (u). Subscripts x, y, and z refer to the components along each of the orthogonal global coordinate axes. Trace H corresponds to head restraint contact and trace B corresponds to bumper contact. Impact occurred at time zero. Zero for each trace occurs at time zero. The + and − signs listed in Tables 2 and 3 refer to peaks above ( +) and below (− ) each trace’s zero.

BACKSET

HRHEIGHT

SPEED

HR-HEIGHT SPEED GENDER AGE HEIGHT MASS HEAD-CIR NECK-CIR NECK-LEN HN-RATIO MMT-FLEX MMT-EXT FLEX-EXT LAT-FLEX LAT-ROT PRO RET RECLINE MC7 NECK-ANG F-PLANE

0.02 −0.01 0.01 0.00 0.02 0.02 0.02 0.01 −0.02 0.00 0.00 0.00 −0.05 0.01 −0.03 0.00 0.01 −0.13 0.15 −0.28 0.00

0.00 0.45 0.06 0.72 0.29 0.11 0.37 0.32 −0.35 0.38 0.43 0.00 −0.02 −0.01 0.11 0.07 −0.02 0.02 0.00 0.01

0.0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.01 −0.02 0.03 −0.01 0.01 0.00

Mean S.D. Units

4.1 1.8 cm

−9.4 2.9 cm

5.9 2.1 km/h

GENDER

−0.01 0.54 0.32 0.28 0.63 0.07 −0.48 0.61 0.75 0.01 0.00 −0.02 0.16 0.01 0.00 −0.01 0.00 0.03 0.51 0.50 0-F l-M

AGE

HEIGHT

MASS

HEADCIR

NECKCIR

NECKLEN

HNRATIO

MMTFLEX

MMTEXT

FLEXEXT

LATFLEX

LATROT

0.00 0.01 0.00 0.01 0.02 −0.01 0.00 0.00 −0.05 −0.04 0.00 0.00 −0.01 0.00 0.07 0.01 0.05

0.33 0.11 0.38 0.27 −0.35 0.47 0.47 0.01 0.00 0.00 0.07 0.04 0.00 0.00 0.00 0.02

26.8 4.7 years

170 7.8 cm

0.48 0.65 0.00 −0.31 0.53 0.43 −0.11 −0.03 −0.08 0.02 0.01 0.00 −0.08 0.00 0.06

0.48 −0.07 −0.03 0.33 0.35 −0.08 0.00 −0.05 0.04 0.02 0.00 −0.07 −0.02 0.05

0.01 −0.68 0.80 0.72 −0.02 −0.02 −0.08 0.06 0.00 0.00 −0.02 −0.01 0.07

−0.14 0.02 0.07 0.11 0.00 0.00 0.04 0.07 0.01 0.02 0.04 −0.02

−0.55 −0.50 0.00 0.02 0.05 −0.04 0.03 0.00 0.00 0.00 −0.02

0.69 0.00 −0.01 −0.01 0.04 0.00 0.00 −0.03 0.00 0.03

0.00 0.00 −0.02 0.13 0.03 0.00 −0.01 0.00 0.03

0.16 0.19 0.04 0.01 0.01 0.00 0.03 −0.02

0.10 0.00 0.00 −0.01 0.00 0.00 −0.01

68.6 10.9 kg

56.6 2.2 cm

34.7 3.5 cm

19.6 1.2 cm

154 22 cm

98 45 N

212 64 N

131 10 °

90 12 °

PRO

RET

0.00 0.03 −0.02 0.00 0.05 −0.01

0.00 −0.03 −0.01 0.03 0.00

0.00 0.00 0.00 0.00

137 12 °

3.8 1.1 cm

3.1 1.0 cm

RECLINE

MC7

NECKANG

−0.10 0.01 0.00

−0.17 0.03

−0.05

24.4 2.6

22.0 5.0

86.1 4.0

°

°

°

FPLANE

8.1 7.0 °

G.P. Siegmund et al. / Accident Analysis and Pre6ention 31 (1999) 393–407

Table 1 Cross-correlation (r 2), mean and standard deviation (S.D.) of potential predictors

399

400

Table 2 Standardized b-coefficients produced by the multiple linear regression analysis for PA modelsa r2

p

Head ax + az − + 6x + 6z − ay + − vy + − uy +

0.93* 0.64* 0.70* 0.99* 0.65* 0.84* 0.80* 0.84* 0.70* 0.78*

5 9 5 8 9 9 7 8 8 11

C7–T1 ax 6x 6z ay vy uy

+ + − + − + +

0.86*† 4 0.98*† 9 0.69* 6 0.58*† 4 0.64* 4 0.70* 4 0.88* 10

Head wrt C7–T1 ax − 0.86* + 0.78* az + 0.70* 6x − 0.84* + 0.67* 6z + 0.71* sx − 0.82* sz − 0.67* ay + 0.66* − 0.68* vy + 0.72* − 0.55* uy − 0.73* + 0.60*

10 5 7 10 5 3 7 10 5 7 8 10 11 11

BACK- HRSPEED GENSET HEIGHT DER

0.65* 0.41 0.29* −0.07* 0.71* 0.32 0.36* 0.10 0.28* −0.13 0.27* −0.40 0.13 −0.07 0.16

0.43*

0.19 −0.48* 0.30* −0.29* 0.10 0.17 0.26 0.32*

0.96* 0.23 0.76* 0.99* 0.14 0.87* 0.86* 0.82* 0.79* 0.42 0.90* 0.99* 0.75* 0.71* 0.70* 0.81* 0.85*

0.88* 0.87* 0.78* 0.74* 0.74* 0.15 0.21 0.82* 0.41* −0.40* 0.62* 0.33* −0.41* 0.44* 0.34* −0.18 0.73* 0.18 0.74* 0.24 0.73* 0.50* 0.31* 0.72* −0.15

AGE

HEIGHTMASS

−0.16* 0.43 −0.02 0.42* 0.28 0.37*

HEAD- NECK- NECK- HN-RA- MMTCIR CIR LEN TIO FLEX

0.38*

−0.06* 0.42 −0.24

−0.57* −0.29*

−0.06

−0.15*

−0.13

−0.18 −0.46* −0.53*

0.25

−0.28 0.12

−0.53* −0.28 −0.25 −0.33 −0.41

0.17

−0.34

0.23

−0.30

−0.14

PRO

−0.14 −0.05

RET

REMC7 CLINE

NECK- FSpeed ANG PLANE

0.13*

−0.06 0.19 0.13 −0.04 0.17 0.13

−0.15 −0.13 −0.09

0.16

0.14

0.18

−0.06* 0.23

0.17

0.06

0.25* 0.16 0.22 0.15

0.22 −0.12

−0.04

*

*

−0.13

* *

* *

−0.14 −0.23* −0.23* −0.22 −0.15

* * * * *

*

−0.08

* * * * * * *

−0.10

* * * * * * * * * * * * *

−0.23

−0.19 −0.31

0.54*

0.28 0.19

−0.08

0.15 0.18

−0.13

−0.20 0.25* 0.18

−0.28 −0.27

0.25

0.27 0.16 0.23

−0.31* −1.8*

0.30 0.41

1.3 0.21

3.3*

2.7*

−2.4 −0.69

−1.7 −0.27

−0.36

−0.37 −0.18 −0.22 −0.31

−0.15 −0.21 −0.22 −0.15

−0.23

0.12 −0.14

0.14

0.13 −0.15

−0.13 −0.22 0.14

−0.18

−0.23

0.17

−0.34 −0.55 0.19 −0.23

−0.24 0.18 0.25 0.32

−0.18

−0.14 0.14

−0.18

0.17

−0.16 0.20

0.18 0.17

Gender

−0.9

−0.07

−0.14

−0.11

0.57*

LATROT

0.16 −0.19

0.31 0.53* 0.63 −0.43* −0.20 0.52 0.17

LATFLEX

−0.11

0.12* 0.29

−0.19*

0.25

FLEXEXT

0.04

−0.05* 0.33

0.24 0.38

MMTEXT

−0.21 −0.24 −0.30* −0.21 −0.16

* *

*

*

a The + or − indicates whether the peak has a positive or negative magnitude, respectively. The correlation coefficient (r 2j ) and number of predictors (p) are shown for each model. The final two columns indicate which response peaks were previously identified as dependent on speed change and gender using a two-way ANOVA (Siegmund et al., 1997). * Significant at P=0.05/62= 0.0008. †

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Response

Table 3 Standardized b-coefficients produced by the multiple linear regression analysis for TPA modelsa Response

r2

a

BACK- HRSPEED GENSET HEIGHT DER

AGE

9 6 7 6 6 6 4 12 8 7 5

0.32* 0.31* 0.35* 0.52* 0.57* 0.39* 0.43* 0.35* 0.49* 0.21 0.40*

−0.08

4 8 3 8 5 5 5 6 7 4 8 7 7 6 9 13 10 12 6 8 7 7 6

−0.31* −0.67* −0.80* −0.78*

−0.34

0.35* −0.21

0.34* 0.26* 0.30 0.42* 0.37* 0.39* 0.45* 0.50* 0.45* 0.35* 0.40* 0.30 0.44* 0.44* 0.39* 0.45*

−0.55 −0.38

−0.55* −0.58* −0.50* −0.60*

0.34 −0.49* −0.32* −0.62* −0.46* −0.66* −0.31 −0.59* −0.51* −0.32* −0.58* −0.33 −0.47* −0.56* −0.34* −0.58* −0.44* −0.43* −0.79* −0.27 −0.34* −0.60* −0.51* −0.32 −0.58*

0.13 0.14 0.12

0.17

−0.24 −0.14

−0.54* −0.29 −0.18 0.54*

−0.12 0.45*

MMTEXT

FLEXEXT

LATFLEX

0.18

LATROT

PRO

RET

−0.07

−0.29 −0.45 0.21

−0.13

0.12

−0.14 −0.13

−0.09 −0.15 −0.14 −0.13 −0.22 −0.12

0.84

0.19

0.12 −0.29

0.36

−0.28

0.14

0.33

−0.54

−0.42

0.14 −0.14

−0.24

0.21

0.25

0.20

0.28 0.56*

0.22

0.21

0.23 0.51*

0.11

0.30 0.14 0.17

−0.15 −0.20 −0.13 −0.15

0.41*

−0.26 −0.42

−0.30 −0.37

−0.26

−0.25 −0.15 −0.13

0.95* 0.39* 0.75*

0.20 0.14 0.30

0.47* 0.41

0.25

−0.19 −0.14

−0.35

−0.17 −0.24

−0.39

0.43

−0.60

−0.14 −0.14

−0.12 0.45

−0.27 0.11

The + or − indicates whether the peak has a positive or negative magnitude, respectively. The correlation coefficient identified as dependent on speed change and gender using a two-way ANOVA (Siegmund et al., 1997). * Significant at P=0.05/62= 0.0008.

−0.06 (r 2j )

−0.14 0.16 0.11 0.20 0.21

−0.11 −0.20 −0.15

0.12

−0.20*

0.11

−0.06 −0.18

−0.10 0.15

0.45

−0.27 0.11 0.16 −0.32* −0.29 −0.15 −0.17 −0.19

*

* * *

−0.29 0.24

0.25

−0.43

−0.42

0.18

Gender

−0.25

0.64* −0.21 −0.69* −0.48

* * * * * * * * * * *

−0.13*

0.12 1.0

0.17

−0.22

NECK- FSpeed ANG PLANE

0.11

0.15

−0.37

REMC7 CLINE

0.12

0.22

−0.15 −0.15

0.32 0.28 0.18 0.33*†

HEAD- NECK- NECK- HN-RA- MMTCIR CIR LEN TIO FLEX

−0.31* −0.18

−0.52* −0.74* −0.54* −0.24 −0.62* −0.30* −0.54* −0.34 −0.55* −0.22* −0.68*

HEIGHTMASS

−0.20 −0.20 −0.18

0.12 −0.21

0.14 0.14 0.17

0.23 −0.12

* * * * * * * * * * * * * * * *

*

* *

*

−0.20 −0.15 −0.19 −0.14

0.11 −0.21

* * *

G.P. Siegmund et al. / Accident Analysis and Pre6ention 31 (1999) 393–407

Head ax + 0.94* az − 0.83* + 0.82* 6x + 0.72* 6x + 0.72* 6z − 0.85* ay + 0.69* − 0.87* vy + 0.83* − 0.65* uy + 0.90* C7–T1 ax + 0.32* 6x + 0.40* 6z − 0.15 ay + 0.56* − 0.54* vy + 0.51* + 0.71* uy Head-wrt C7–T1 ax − 0.66* + 0.83* az + 0.47* 6x − 0.85* + 0.71* 6z + 0.52* sx − 0.90* sz − 0.67* ay + 0.77* − 0.79* vy + 0.71* − 0.50* uy − 0.50* + 0.84* 6z + 0.52* sx − 0.90*

p

*

and number of predictors (p) are shown for each model. The final two columns indicate which response peaks were previously

†

401

402

G.P. Siegmund et al. / Accident Analysis and Pre6ention 31 (1999) 393–407

Of the remaining predictors, head restraint height (HR-HEIGHT) and HEIGHT appeared most frequently with significant coefficients in PA models (Fig. 4a). A higher head position relative to the head restraint (decreased HR-HEIGHT) resulted in both larger angular velocity and displacement of C7–T1 and greater horizontal and vertical displacement of the head relative to C7 – T1. HR-HEIGHT did not produce significant b-coefficients for any of the peak head kinematic responses. When significant, increased HEIGHT was related to lower absolute horizontal and angular head and C7 – T1 kinematics, but larger relative neck extensions. In TPA models, HRHEIGHT was present and significant more frequently than the other predictors (except SPEED and BACKSET). When significant, increased HR-HEIGHT con-

sistently resulted in a longer delay between impact and peak response. Despite having few or no significant b-coefficients, MMT-EXT, FLEX-EXT and F-PLANE were present in about half of the PA models (Fig. 4a), and RECLINE was present in more than half of the TPA models (Fig. 4b). The effect of increasing F-PLANE angles was to consistently decrease the absolute peak amplitude of responses in which it appeared. An increasing RECLINE angle decreased the time to peak amplitude in all but two model in which it appeared. MMT-EXT and FLEX-EXT had a more variable effect, though increased MMT-EXT consistently decreased absolute head kinematics. Other predictors appeared either too infrequently with significant coefficients or with inconsistent effect to identify patterns.

Fig. 4. Total (outlined) and significant (solid) occurrences of predictors in all PA and TPA models.

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4. Discussion The relative influence of 22 selected occupant, seat and external factors on 31 kinematic response peaks obtained from 42 human subjects exposed to 81 lowspeed rear-end automobile collisions has been assessed using multiple linear regression. The purpose of this analysis was to potentially identify the occupant-related factors responsible for the previously observed genderdependency of peak kinematic responses using a twoway ANOVA (Siegmund et al., 1997). Overall, the previous correlation between SPEED and peak kinematic response was unaffected by the inclusion of the 19 additional occupant- and seat-related factors, whereas the previous correlation between GENDER and peak response did not recur in a more comprehensive analysis. In the presence of the additional occupant- and seat-related factors, the models of responses which were previously identified as GENDER-dependent most commonly contained head restraint backset (BACKSET) and occupant height (HEIGHT) rather than GENDER. Less frequently, occupant mass (MASS), head circumference (HEAD-CIR), neck strength in extension (MMT-EXT), torso angle (RECLINE), C7 angle (MC7) and head restraint height (HR-HEIGHT) were also present. These predictors, some of which were correlated to GENDER (Table 1), appeared to be responsible for the earlier observation that gender was related to peak kinematic response (both magnitude and time to peak magnitude). With these surrogate predictors available in the regression, GENDER was never significant for those eight PA and eight TPA models previously identified as gender-dependent. The cross-correlation between height (HEIGHT) and head restraint height (HR-HEIGHT) was relatively strong (r 2 =0.72) and the potential for one to act as a surrogate for the other exists. Absolute head restraint height in these tests was fixed, and therefore HRHEIGHT was related to seated height, which itself is related, though not rigidly, to standing height. Both occupant height and head restraint height have been previously related to neck injury incidence (Carlsson et al., 1985; Nygren et al., 1985), however given their inter-relationship, it was not possible to determine whether height or head restraint height most affected occupant response. Intuitively, head restraint height would seem best because it is a relative measure between the occupant and support and is therefore potentially portable across differing seats and head restraints. A differentiation between height and relative head restraint height was not possible with the current data set because seat geometry remained constant throughout the tests. Of all potential predictors, neck strength in extension (MMT-EXT) cross-correlated most strongly with

403

GENDER (r 2 = 0.75). Despite this relatively strong correlation, it was significant in only two of the 16 PA and TPA models previously identified as GENDER-dependent. BACKSET on the other hand was effectively independent of GENDER, HEIGHT, HR-HEIGHT and MMT-EXT, yet it appeared in four of eight PA models and seven of eight TPA models previously identified as gender-dependent. The large number of models in which BACKSET appeared, combined with the absence of a correlation between GENDER and BACKSET, indicated that BACKSET was not likely a surrogate for GENDER, but rather a prominent confounding variable in the overall analysis. The appearance of GENDER as a significant predictor in three PA models (Table 2) for which it was not significant in the two-way ANOVA was puzzling. Based on these three models, males appeared to have a larger peak angular acceleration of the head in extension, a larger peak angular velocity of the head relative to C7–T1, and a smaller initial flexion of the head relative to C7–T1. The inclusion of other predictors appeared to strengthen the relationship between GENDER and these responses. That is, GENDER was not significant in models comprising a subset of half of the predictors from the best Cp model. For the kinematic response peaks previously attributed to gender (Siegmund et al., 1997), acceleration and velocity amplitudes were greater for females, displacement amplitudes were greater for males, and gender-dependent peaks occurred later for males. None of the potential gender surrogates identified in this study mimicked this pattern, suggesting that the previously observed gender-dependency was not likely caused by only one of the surrogates. More generally, the results of these regression analyses indicated that vehicle speed change (SPEED) and relative head-restraint position (BACKSET and HRHEIGHT) affect the magnitude and timing of peak kinematic responses more than the occupant-related factors considered here. These three factors combined accounted for 70% of all predictors with significant b-coefficients in the regression analyses. While the inclusion of these three factors in many of the models was not unexpected, the degree to which they were present compared to the 18 other factors was larger than expected. SPEED was the single most dominant predictor of peak kinematic response. In all responses for which SPEED’s b-coefficients was significant, increased SPEED was associated with larger peak kinematic response magnitudes. Larger peak kinematic and kinetic responses with increasing speed change have been observed using an anthropomorphic tests device (ATD) fitted with a modified RID neck at speed changes of 5 and 12.5 km/h (Svensson et al., 1996) and human subjects at speed changes of 2–4 km/h (Ono and

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Kanno, 1993). The current analysis extends these findings in two ways: first by statistically demonstrating this phenomenon with a larger sample, and second, by demonstrating the dominance of SPEED in relation to other occupant-related factors. In both PA and TPA models, BACKSET was present and significant most often in response models of absolute head kinematics and least often in response models of absolute C7 – T1 kinematics. This finding was consistent with our general observation that the torso interacted exclusively with the seat back and the head interacted exclusively with the head restraint during the tests. As a result, most models which contained BACKSET were for kinematic peaks generated by head restraint contact. Larger backsets appeared to allow a larger degree of differential motion between the head and head restraint to develop before head restraint contact occurred. A decrement in head restraint backset from 10 to 4 cm has previously been shown to reduce some peak kinematic and kinetic response parameters using ATD’s (Svensson et al., 1996). The current analysis has shown that this observation extends to human subjects with a more continuous range of head restraint backsets. Unlike BACKSET, relative head restraint height (HR-HEIGHT) was not present and significant most often in PA models of absolute head kinematics. Instead, it appeared in models of absolute angular C7–T1 kinematics and relative linear displacement kinematics. In contrast, relative head restraint height was significant in TPA models of absolute head kinematics but absent in models of absolute C7 – T1 kinematics. The reasons underlying this relationship to C7 – T1 kinematics are not known and warrant additional investigation. Two predictors with a consistent effect on peak response appeared in a relatively large number of models without significant b-coefficients. Increased FPLANE angle was consistently associated with decreased peak amplitude and increased RECLINE angle was consistently associated with a reduced interval between impact and peak response. The absolute range of both predictors was smaller than would be expected in the general population because both were essentially controlled variables. Subjects were instructed to hold their heads horizontal and the seat back angle was fixed; therefore variations in both F-PLANE and RECLINE were the result of how occupants chose to seat themselves within the constraints of the experiment. If initial head or seat back angle was varied more, either of these two predictors may have reached significance in more of the models. Seat back angle has been shown to affect peak kinetic parameters using human subjects at speed changes of 2 – 4 km/h (Ono and Kanno, 1993), although the effect of an initially flexed or extended position on peak head kinematics has not been systematically explored.

The occupant-related factors could be loosely grouped into anthropometry factors, physiology factors, and seated posture factors (Fig. 4a and b). When so grouped, it was observed that anthropometry factors yielded predictors with significant b-coefficients for more responses than did either physiology or seated posture factors. Notably, coefficients for neck length and all six measures of neck range of motion did not once reach significance for either PA or TPA models. The present study draws some links between peak kinematic response and occupant, seat, and external factors (see B in Fig. 1). The relationship between occupant kinematics/kinetics and the production, severity and duration of WAD (see C in Fig. 1) has not yet been determined. Generally however, impact-related injury is likely the result of excessive stress or strain in a particular tissue, and is induced by either direct external application of force, indirect forces set up by relative motion, internal forces set up by muscle contraction, or some combination of the three. The current experiments have shown that all three factors are present during low-speed rear-end collisions: there is direct contact between the occupant and the seat, there is differential motion between the head and torso set up by unsynchronized seat and head restraint contact, and although not addressed here, the muscles are active early in the occupant dynamics (Brault et al., 1998). Minimizing occupant kinematics could potentially minimize two of these contributors to tissue stress and possibly reduce the potential for injury. With this assumed relationship between kinematics and injury in mind, the primary dependence of peak kinematic response on vehicle speed change and relative head restraint position has important safety implications for vehicle manufacturers. Unlike occupant factors, manufacturers have some degree of control over both collision severity and head restraint position and can therefore optimize vehicle and head restraint designs to minimize occupant kinematic response for a given collision. For example, vehicle speed change for a given collision closing speed can be minimized with bumpers that effectively dissipate energy and minimize collision restitution (Siegmund and King, 1997). In addition, head restraint designs which minimize backset and extend sufficiently high to control head motion exist (Wiklund and Larsson, 1998) and could be incorporated into all vehicles. Although the number of potentially influential variables examined in this study was relatively large, numerous potentially important variables were omitted. The collision direction, vehicle, seat, and seat adjustment remained constant across all tests and all subjects sat upright, faced forward, held their head horizontal, and had initially relaxed neck musculature. Out of position occupants and level of preparedness in particular have been previously reported to affect clinical

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outcome (Sturzenegger et al., 1994). These parameters were controlled in order to produce a meaningful data set on which to perform statistical analysis, however the exclusion of these and other potentially relevant variables necessarily renders the current analysis incomplete. Given the as-yet unquantified effect of these variables, the current results do not support a contention that speed change and head restraint position are the only two important factors which affect occupant kinematic response. Within the limited number of potentially relevant variables examined in this study, the current analysis helps to prioritize which variables to explore more thoroughly. Vehicle speed change, head restraint position and possibly seated posture all affected peak kinematic response. Future research into the effect of broader ranges of head restraint and seat back positions may provide additional insight into how to minimize occupant kinematics. Moreover, the current analysis has shown which variables need to be carefully controlled in future experiments of as-yet untested variables. It must be noted that the results of this analysis apply to aligned rear-end collisions with subjects normally seated, facing forward, and relaxed prior to impact. Compared to the general population, head restraint backset in this study was smaller than 85% of the motoring public (Viano and Gargan, 1995). As a result, the data and results presented here cannot be unconditionally extrapolated to alternative impact directions, head restraint positions, or seated postures.

4.1. Regression analysis issues Linear regression presumes that predictors are linearly related to responses. If relationships are known to be non-linear, then predictors can be first transformed to ensure linearity. The current system — a multi-articulated spine with active muscle input responding to a transient loading condition — is extremely complex and the relationships between some predictors and the kinematic response were not known. Some transformed predictors based on simple analytical models of the head and neck were initially considered, however they produced high co-linearities between predictors and were therefore abandoned in favour of the simpler and directly measured potential predictors used in this analysis. Given the relatively narrow range of kinematic responses elicited by the two collision severities, a linear relationship was chosen as a first approximation for this analysis. The more complex Cp minimization procedure was used instead of the more common stepwise regression procedure for two reasons: First, not all possible models are examined using the stepwise process. As a result of not examining all models, the resulting ‘best’ model

405

can be an artifact of variable ordering and need not be the best model. And second, the resulting coefficient of determination (r 2) may overestimate the significance of the relationship (Weisberg, 1995). Because of these shortcomings, two regression criteria which examine all possible models were initially evaluated: Cp minimization and an adjusted r 2 maximization (Weisberg, 1995). Both criteria included a penalty for adding predictors to the model, however Cp minimization was ultimately favored because of its larger penalty. The best Cp models, on average, contained about half as many predictors as did the best adjusted r 2 models. Although the models presented here produced the lowest Cp value, there were often numerous models wherein some predictors could be added, deleted, or swapped without greatly changing Cp. The difference between the best ten models for a given response was often small and many of the top models for a given response produced r 2j \ R 2j . This was particularly true for most models containing SPEED and BACKSET, which together accounted for much of the observed response variation. Predictors with significant coefficients were present in all of the top models examined, and for this reason, emphasis has been given to those predictors within a ‘best’ model that had coefficients significantly different from zero at the Bonferroni-adjusted significance level. The variability in predictor membership in each model was compounded because the chosen set of predictors can itself affect the model chosen using the Cp method. The Cp value for each model is calculated using the standard error for the full model (using the entire set of candidate predictors), and therefore changing the makeup of the candidate set of predictors changes Cp. During the analysis, it was observed that adding another predictor to the set of candidates sometimes resulted in the selection of a different optimal model, even though the new predictor did not appear in that model. This property of Cp was never observed to affect predictors with coefficients significantly different from zero. Because of SPEED’s dominant role as a predictor of peak kinematic response and its bimodal distribution (SPEED was controlled to be  4 or 8 km/h), the resulting r 2j ’s produced by the analysis were likely artificially large (Weisberg, 1995). To evaluate this phenomenon, the best model from the combined data set was evaluated separately on the data from the two SPEED levels. As a result of this process, the median r 2j values decreased by 0.11 for PA models and 0.07 for TPA models. The critical R 2j values increased by 0.26 and 0.25 for PA and TPA models, respectively. Overall, this process reduced the number of models that reached significance (PB 0.0008 with at least one of the two data sets) from 31 to 22 for PA models and from 30 to 25 for TPA models. Despite the reduction in the num-

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ber of significant models, the predictors with significant b-coefficients within models remained similar and the general observation that head restraint position had a larger effect on peak occupant kinematics than did other factors was unaffected. The standardized regression coefficients given in Tables 2 and 3 should be used cautiously to infer the relative influence of each parameter. The magnitude of the standardized coefficients are related to the ratio of the predictor’s standard deviation to the response’s standard deviation. If a specific predictor has a small standard deviation because of the test methodology (e.g. RECLINE and F-PLANE), then the standardized coefficient may be smaller than would be present had this predictor been allowed to vary over a larger range. In addition, using the linear models and coefficients to predict peak response outside the range of the predictor and response values is unjustified because the models are only linear approximations of more complicated relationships. In summary, the present analysis has shown that vehicle speed change is strongly related to the magnitude and timing of the peak kinematic response of occupants in low-speed rear-end automobile collisions. Relative head restraint position (both horizontal and vertical) and, to a lesser extent, seated posture have a more consistent and broad effect on peak kinematic response than do the occupant anthropometry and physiology factors addressed here. The previously observed relationship between some peak kinematic responses and gender appeared to be the result of gender-based difference in height, and to a lesser extent occupant mass, head circumference, neck strength in extension and initial angle of the torso.

Acknowledgements Partial funding for this project was received under the Technology BC program administered by the Science Council of British Columbia.

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