observations on pedestrian pre-crash reactions during simulated … · 2014. 5. 13. · 2 soni et...

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Submission Number

Stapp Car Crash Journal, Vol. 57 (November 2013), pp. Copyright © 2013 The Stapp Association

Observations on Pedestrian Pre-Crash Reactions during Simulated Accidents

Anurag Soni1, Thomas Robert1, Frédéric Rongiéras1, 2, Philippe Beillas1 1 Université de Lyon, F-69622, Lyon, France; IFSTTAR, UMR_T9406, LBMC, Université Lyon 1

2 Chirurgie orthopédique et traumatologique, Hôpital d'instruction des armées Desgenettes, 69003 Lyon, France

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ABSTRACT – Pedestrian protection systems, both active and passive systems, are being introduced in the EU and Japan to comply with regulatory requirements. Their designs are specific and, in general, reflect an accident scenario of the pedestrian being struck on the side by a vehicle traveling at a maximum travel speed of 40 kph. The present study is an effort to quantify the effects of pedestrian reaction prior to an accident and identify characteristics that may help minimize or prevent the pedestrian to vehicle interaction. Accident situations were simulated with volunteers using a non-impacting methodology. Fifty one reactions from 23 volunteers of two age groups were observed. Most of the volunteers were found to run, step-back or stop in fright in a dangerous situation. Volunteer speed was an important parameter which could help in differentiating these reactions. Age related differences were also observed, both for reaction strategy and reaction times. While the majority of young subjects ran, elderly stopped as often as they run. Volunteers’ posture at the time of impact was found to be highly variable irrespective of the type of reactions. The exception was when a volunteer stopped/braced in apparent fright and raised their arms to form a triangle covering their face and their head. Results of the present study may be helpful when selecting or evaluating the benefit of pedestrian safety strategies by allowing the inclusion of information about types of reaction, pedestrian speed, reaction time and age differences in the scenarios. In addition, pedestrian pre-crash postures and muscle activities could be utilized for evaluating/improving the passive safety systems and active models.

KEYWORDS – Pedestrian accidents, Pedestrian Pre-Crash Reaction, Pedestrian Posture, Reaction time, Active safety systems, Passive safety systems

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INTRODUCTION

Pedestrians account for a significant proportion of road accident fatalities. Over twenty million people are injured and more than one million people are killed every year in road traffic crashes worldwide (Mohan, 2003; Nantulya and Reich, 2002). In the European Union alone, more than 5,000 pedestrians were killed in the year 2009 (CARE database, 2009). This suggests that devising countermeasures to ensure pedestrian safety on road is essential to continue reducing road fatalities.

Pedestrian protection approaches using passive and active safety systems have been developed and improved over the years. The passive systems have been devised to mitigate the extent of injuries to pedestrians by introducing various impact energy-absorbing components (such as compliant bumpers, pop-up bonnets and windscreen airbags) when an

accident cannot be prevented. Active safety systems have been devised with an intention to avoid an accident or to mitigate its consequences by reducing the impact velocity. Strategies include warning the driver, activating autonomous braking or evasive steering after predicting the possibility of collision with a pedestrian based on detection with sensors (e.g. Gavrila et al., 2003; McCarthy et al., 2004, and Gandhi and Trivedi, 2007). Both approaches have advantages and limitations and they can be difficult to compare in terms of efficiency and cost benefit (Lubbe et al., 2012).

In terms of realism, both active and passive safety approaches typically do not account for the pedestrian’s ability to react prior to an imminent accident. The existing passive safety procedures for evaluating vehicle aggressiveness against a pedestrian were mainly derived from research conducted using passive human surrogates (i.e. cadavers, dummies and numerical models which do not account for muscle activity) in a standard walking posture. The selection of this particular posture which represents only one instance (walking or running) of

Address correspondence to Philippe Beillas, LBMC, UMR_T9406, 25 Avenue François Mitterrand, Case 24, 69675 Bron Cedex FRANCE. Electronic mail: [email protected]

2 Soni et al. / Stapp Car Crash Journal 57 (November 2013)

commonly known pedestrian activities prior to accidents could be compatible with previous epidemiological studies, as for example Chidester and Isenberg (2001) suggested that 78% of the pedestrians are struck from the side. On the other hand, a design of active system controllers may be specific to simplified pre-crash scenarios such as either the pedestrian is typically assumed standing still or walking with a constant speed in front of a vehicle approaching at a certain distance (e.g. Schramm and Roth, 2009; Ebner et al., 2010; Habibovic and Davidsson, 2011; Niewöhner et al., 2011; Yuasa et al., 2013). In contrast, real world car-pedestrian accidents involve live humans having the capability of reacting and contracting their muscles. Under perceived risk or an emergency situation such as an imminent pedestrian accident, the survival instinct could result in sudden voluntary or involuntary reactions, including avoidance maneuvers, stiffening, etc. These reactions may eventually affect posture, muscle activity, kinematics and kinetics just before and during the pedestrian impact. This could have multiple consequences. For example, it could affect and sometimes reduce the potential benefits of a passive safety system due to an inadequate posture or to an underestimation of an injury risk due to the lack of additional muscle forces (Chawla et al., 2007). Reactions may lead a frightened pedestrian to run away or turn back to avoid the accident, and result in a false positive by the active system. If these reactions were known and understood, their presence or absence could help to select or refine strategies for active systems (pedestrian warning time, brake trigger, level of braking, steering angle, etc.).

At this stage, there is a relative lack of quantitative knowledge of pedestrian reactions prior to impact. Anecdotal evidence is available from accident databases (e.g. GIDAS) or field videos, but quantitative information that could be used for modeling purposes seems to be lacking primarily because the focus of the accident data collection to date has been for the understanding of the overall injury/fatality statistics. Thus, the current study focuses on developing a clinical understanding of how a pedestrian would react to an imminent accident situation.

Accident statistics highlight the importance of the elderly population in fatal accidents (CARE database, 2009). Aging is also associated with increased frailty, higher chances of functional disorders and decline in

visual, cognitive, sensory and reactive abilities. Therefore, the specific aim of the current study was to observe and quantify the performance - in terms of types of reactions, reaction time, postures and muscular activities - of elderly and young pedestrians subjected to an immediate emergency situation similar to pre-crash.

METHODS

The protocol1 of this study was first reviewed and approved by the internal ethics committee of IFSTTAR. Then, in accordance with French regulations, the protocol was submitted, reviewed and approved by legal authorities as a request for “authorization of a clinical trial for the evaluation of non-medical products” (AFSSAPS Reference B120081-40, regional ethical committee Reference CPP Lyon 2012-A00014-39).

Human Volunteer Details

After ethical approval, volunteers were recruited using flyers. Volunteer age was the only inclusion criteria: two age groups, i.e. young from 18-30 years and elderly from 60-75 years, were targeted. Volunteers with existing orthopedic, neurological, cardiac, balance, mobility, hearing or visual disorders, and volunteers who practice sport at high level (participate in competitions or practice more than 8 hours per week) were excluded from the study during the mandatory medical check-up prior to the experiments. Finally, twenty three volunteers (12 young and 11 elderly) were recruited. All participants then signed an informed written consent form according to the guidelines of the ethical committee. The average values of age, stature and weight of the recruited volunteers are listed in Table 1 and more detailed information are given in Table A1 of Appendix A.

Table1: Volunteer details

N Sex

Age (years) mean (SD)

Stature (cm) mean (SD)

Weight (Kg) mean (SD)

Young 12 8 Males & 4 Females

25 (3.1)

170 (7.4)

65.5 (9.4)

Elderly 11 7 Males & 4 Females

65.5 (2)

165.8 (8.2)

74.1 (12.2)

1 The protocol of this study was reviewed and approved by properly constituted ethics committees and conducted in accordance with the practice of the responsible governing authority.

Soni et al. / Stapp Car Crash Journal 57 (November 2013) 3

Experimental Design

Pedestrian Walking Space: A pedestrian street crossing environment, shown in Figure 1(a), was created in the laboratory. A street-crossing with two-way traffic flow was simulated using a walking track (5 meters long and 1 meter wide) positioned between two screens (6 m wide and 2 m high) onto which generated road traffic was projected using two video projectors. A customized software application (Cavallo et al., 2006) was used for generating the traffic flow. A 7.1 audio system was also installed around the walking space to create sound localization effects corresponding to the traffic flow. A top down schematic view of the pedestrian walking space shows the arrangement of all the elements in Figure 1(b). Six motion capture Vicon cameras and three additional conventional digital video cameras were placed around the walking space. A spring loaded launcher was placed behind each screen. Thick gymnastic mattresses were placed between platform and screens to provide safety to the volunteers in case of fall.

(a)

(b)

Figure 1: (a) Pedestrian walking space created in-house and its (b) schematic arrangement

Traffic Scenario: The visual scenes displayed a two way street, 5 m wide sidewalk-to-sidewalk. The virtual traffic consisted of six different cars on one side and five different cars with two motorcycles on the opposite side. A single set of traffic flow was used and repetitively displayed in loop. The visual scenes on both screens showed the traffic approaching towards the pedestrian on one screen and leaving on the other screen after passing him. The traffic on both sides was moving with a constant speed of 40 km/h and with constant but unequal inter-vehicle distances leading to create a complex traffic flow which could be crossed only at specific instants.

Elements of Simulated Accident: A combination of visual, audio and physical perturbations was used to surprise the volunteers during a simulated accident trial:

(a) Visual: To create a visual surprise, a target vehicle (a red “emergency” truck) was inserted in the traffic scene. Its speed and start position differed from the other vehicles in the traffic: it appeared relatively farther away from the crossing but was moved with a higher velocity such that it reached the volunteer in 1.1 seconds to simulate an accident. The vehicle speed and start position and thus the time to (virtual) impact of 1.1 seconds were selected after several iterations during pre-tests in order to create a condition where the impact is apparently unavoidable while leaving sufficient time to observe reactions.

(b) Audio: Concurrently, a car crash sound of 95 db maximal intensity was played for 1 second to create an audio perturbation. An effect of rapidly approaching vehicle followed by a crash event was created by modulating the audio pulse on the speakers.

(c) Physical: A spring loaded L shape launcher outfitted with an air filled light plastic ball (40 cm in diameter) (shown in figure 2) was launched from behind the screen. An opening in the screen allowed the ball to pass through and suddenly appear on the side of the volunteer. An electromagnet was used to hold the launcher in armed position and release it at an appropriate time. The torsion spring specifications were such that the ball was moving with an average speed of 1.8 m/s towards the volunteer. The L shape frame and stopper were designed so that the ball was stopped 0.5 m away from the volunteer and did not create a shadow on the screen in the armed position.

Projection Screens

Platform

DV Camera

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Projector

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tress

es

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en

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Giganet


Figure 2: Spring loaded launcher outfitted with a ball

Measurements and Instrumentations

Anthropometry: Classic anthropometric measurements were obtained for each volunteer, including weight, stature (with and without shoes), top head-to-C7, C7-to-sacrum, shoulder-to-shoulder, upper arm, forearm, hip-to-knee, and knee-to-ankle.

Motion Capture: An optoelectronic VICON® Nexus MX T40 motion analysis system with 6 cameras, sampled at 100 Hz, was used to capture the trajectory of spherical reflective markers (14.5 mm diameter) placed on the volunteer’s body. The accuracy of this system was verified by a static and dynamic calibration. A total of 46 markers, as shown in Figure 3, were glued on the skin near anatomical bony landmarks selected by palpation. One digital video camera (50 Hz) was calibrated and synchronized with the Vicon system to be used as visual support for the motion analysis. In addition, two more digital cameras were utilized to record the entire test from different viewpoints.

Muscle Activity: Surface electromyography (EMG) electrodes were glued bilaterally to 12 key muscles of upper arms (Biceps and Triceps) and lower extremities (Quadriceps, Hamstrings, Gastrocnemius and Tibialis Anterior) to measure the muscle activity. A wireless AURION® surface EMG system was used to record the EMG signals at a frequency of 1000 Hz.

Figure 3: Position of markers (circles) and the EMG electrodes (squares)


Experimental Procedure and Timing

After the anthropometric measurements, EMG electrodes were glued and reflective markers were positioned on the volunteer. Motion captures of calibration tasks (static poses and a dynamic task) were then performed. These are further used to adjust a digital manikin to the tested volunteer, eventually used for the kinematics reconstruction of the volunteer movements.

After the calibration tasks, simulated traffic flow was displayed on the screens. Volunteers were instructed to cross the street with their preferred pace when the traffic permitted it. The EMG and Vicon marker trajectories were recorded for the normal gait during one of the street crossing task. After a training period, accident situations were triggered without their knowledge in order to create a surprise effect. Based on several iterations during pretests, it was decided to manually trigger the accident trial when the volunteer was about to take his second step from the start point (about half a meter away from the start point) so that the simulated impact occurs near the middle of the track. After the manual trigger, the timing of the surprise elements and the trigger of the Vicon and EMG recordings were controlled and synchronized by a microprocessor. Figure 4 provides a timeline of the simulated accident trial including the onset of the three surprise elements. A LED was lit at the time of trigger to help define the time zero of the event for video cameras during the post processing.

After each accident trial, the launcher was re-armed and the volunteer was instructed to resume the street

crossing task. After 5 to 10 cycles of normal street crossing, another accident trial was triggered. Because of the repetitions, it was more likely for the surprise effect to diminish with trial repetitions. The accident side (left or right) was alternated in an attempt to minimize this affect. Also, after the first two trials, the experiment was stopped if it was found, based on volunteer gesture, that the volunteer was less surprised and that particular trial was excluded from analysis. As a result, two (minimum) to four (maximum) simulated accident trials were run for each of the volunteers. Volunteers’ feedback about surprise effects was also collected at the end of the experiment.

Data Analysis

The raw marker trajectories for the simulated accident trials were processed in the Vicon Nexus 1.4.116 software package. Processed trajectories obtained for the 46 markers were utilized for motion reconstruction using a custom inverse kinematic solver (Wang et al., 2005). This routine computes the joint angles from the captured marker trajectories by minimizing the distance between the captured and model-determined marker positions of all the body segments at each frame. In the present study, the kinematic model used in the solver was the one of the RAMSIS digital manikin (Meulen et al., 2007), made of total 27 rigid body elements connected by 65 degrees of freedom, scaled to the volunteers’ anthropometry. The estimated joint angles were then used to quantify the volunteer postures at the time of impact.

Figure 4: Timeline of a typical simulated accident trial. The drawings show a top view of the walking track with the volunteer (pointed by an arrow). Onsets of visual (T2: target car appears), audio (T3) and physical (T4) surprise elements are shown next to the drawing

T1 T2 = 0 sec T3 = 0.6 sec T4 = 1.1 sec

6

In an upright standing position, the anatomical position of center of gravity (CG) in human body is located in front of the sacrum bone, at about the second sacral level (Winter, 1990). Therefore, a marker placed near the sacrum, (named LPIS in Figure 3), was used to approximate the volunteer CG. Its position was also used to characterize the volunteer’s position and speed. The estimated CG speeds along with the descriptive analyses of the recorded digital videos were utilized to classify the reaction types.

The moment of the first reaction was estimated as the first noticeable change in the movement detected on the recorded videos of 20 ms resolution (50 Hz frequency). Since the moment when the volunteer perceives the surprise element could not be estimated, the reaction time was calculated using the time of triggering as the reference.

The EMG activity recorded for the normal gait and surprise trials were analyzed by following a standard processing approach (Winter, 1990). Muscle activity for a subject in a surprise trial was then calculated by normalizing it by its corresponding peak value during the normal gait.

Non parametrical statistical tests (Mann Whitney, Kruskal Wallis and Chi-squared) were used to analyse the results.

RESULTS

A total of 70 trials (37 for young and 33 for elderly) were performed. For all volunteers, the first two trials were included in the analysis except when data was missing due to technical issues. Subsequent trials were only included if the volunteer was found to be surprised. Finally, results from 51 trials were analyzed in the present paper (26 for young and 25 for elderly), out of which 22 were from the first trial, 20 were from second and 9 were from third trial. Details of these trials and recorded volunteer reactions are provided in Appendix B.

Types of Reactions

Volunteers were found to be surprised and reacted in 45 out of 51 trials. They did not react and kept walking normally in 6 trials. Out of these 6 no reaction trials, 3 were from elderly who seemed to be surprised on the video but seemed unable to react and continued walking. This contrasts with other three young volunteers (especially Y03), which were looking elsewhere instead of on the screens and also continued walking.

Based on visual analysis of the recorded videos and volunteer speed time histories, three additional types of reactions/strategies were identified from the 45 trials in which volunteers reacted. In summary, the reactions were referred to as follows:

1) Accelerating: when the volunteer started running, jumping, or sprinting (N = 25)

2) Freezing: when the volunteer stopped or decelerated (N = 15)

3) Backing-out: when the volunteer stepped back to avoid the simulated accident (N = 5)

4) Walking: when the volunteer did not react (N = 6)

Age related differences in the types of reactions were visible between young and elderly populations (Figure 5). Young subjects accelerated (58% of the cases) more than they froze (18%) or stepped back (12%). Elderly volunteers accelerated (40%) as often as they froze (40%) and stepped back in 8% of the cases. Both young and elderly did not react and kept on walking in 12% of the cases. A Chi-square test indicated that there were no significant differences in the proportions of reactions between Young and Elderly (χ² (3, N = 51) = 2.85, p>0.1) populations. The type of reaction was also found to be largely unaffected by the trial repetition (χ² (6, N = 51) = 6.18, p>0.1), except that some of the volunteers were seen to step back only during second (3 cases, trial 10, 19 and 40) and third trials (2 cases, trial 17 and 34).

58%

18%12% 12%

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uenc

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Accelerated Froze Backed- out

Walking

Figure 5: Distribution of types of reactions for young and elderly volunteers


Volunteer CG Speed

Volunteer CG speed was grouped according to the reactions. Speed corridors (mean +/- standard deviation) were generated for young (Figure 6a) and elderly (Figure 6b). For both, differences in speed among the three strategies were visible. During the simulated accident, CG speed increased when the volunteer accelerated, whereas it decreased to reach zero when the volunteer froze and it became negative when the volunteer backed-out. Volunteer CG speeds for all the trials are plotted in Figure C1 in Appendix C. A Kruskal-Wallis non parametric test was used to compare the CG speed between the four strategies at different times after the trigger. It showed that volunteer CG speeds at the time of

trigger were significantly different among all the types of reactions (H (3) = 21.2, p<0.01). More specifically, the CG speed at trigger was significantly lower for the trial where the volunteer froze compared to those where volunteers accelerated or did not react (U = 42.5, p<0.01 and U = 2.0, p<0.01, respectively). The differences in CG speed among four types of reactions then increased sufficiently that 300 ms after the trigger (or 800 ms before impact) “Accelerating” and “Walking” reactions could be differentiated from “Backing out” and “Freezing” reactions for both young and elderly populations (Figure 7). Furthermore, no difference was found in the CG speeds at the time of impact between the young and elderly groups (U = 206, p>0.1)

Figure 6: Speed corridors for each type of reactions for (a) young and (b) elderly volunteers (Note: (1) zero corresponds to the trigger time and (2) sudden jumps in the average speed curves corresponds to the end of data for some trials

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CG

spee

d at

300

ms (

m/s

). * *

**

Figure 7: Differences in volunteer CG speed at 300 ms after the trigger (* represents the cases having significant differences)

Time to First Reaction and Reaction Time

Time of the first reaction for all the trials (expect for 6 trials of the walking strategy) was plotted on the simulated accident timeline in Figure 8. While the elderly reacted after the onset of the sound stimuli in 68% of the trials (15 out of 22), the young also reacted before the sound onset time in 60% of the trials (14 out of 23).

Mann-Whitney test showed that reaction time was significantly shorter for young (mean 470 ms, median 440 ms) than for elderly (mean 630 ms, median 680 ms) (U = 132, p<0.05). Moreover, a Kruskal-Wallis test indicated that the repetition of surprise did not influence the reaction time (H (2) = 3.28, p>0.1). It also indicated that differences in the time to first reaction were not significant (H (2) = 2.26, p>0.1) among the types of reactions.

Tria

ls

Young Elderly

Trigger Sound Impact0ms 600ms 1100ms

Figure 8: Time of first reaction for young and elderly plotted on the event time line

Volunteer CG Location on the Platform at the Time of Trigger and Impact

The CG location at the time of trigger and at the time of impact for young and elderly are plotted in Figure 9(a) and Figure 9(b), respectively. Irrespective to the type of reaction, most of the volunteers (88% of younger adults and 91% of elderly) were in the danger zone of accident (within 1 meter of the platform centre) at the time of impact. Few of the volunteers could escape due to early anticipation or running speed.

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Froze_@Trigger

Walk_@Trigger

Acc_@Impact

Back-out_@Impact

Froze_@Impact

Walk_@Impact

(a) Young population (b) Elderly population

Figure 9: CG location at the time of trigger (empty polygons) and impact (filled polygons) for (a) young and (b) elderly (Note: zero represents the center of the platform and the shaded area illustrates an accident zone equivalent to a 2-meter wide car)


Variations of the volunteer positions at trigger time – which are due to the manual triggering – can also be observed in Figure 9. The statistical analysis was performed to determine if the volunteer CG position at the time of trigger affected the types of reaction (Figure 10 (a)) or volunteer CG position at impact (Figure 10 (b)). A Kruskal-Wallis non parametric test showed that CG location at time of trigger differed among the different types of reactions (H (3) = 4.7, p>0.1). Mann Whitney post hoc tests showed that the differences were significant only between the “Accelerating” (mean 1297 mm, median 1399 mm) and “Freezing” (mean 1646 mm, median 1750 mm) reactions (U = 76, p<0.01). Moreover, CG location at time of trigger was closer to the platform center for Young (mean = 1273 mm, median = 1303 mm) than for Elderly (mean = 1653 mm, median = 1750 mm) (U = 132, p<0.01). CG position at impact strongly depended on the type of reaction (H (3) = 30,

p<0.01). Mann Whitney paired comparison showed that impact location was farther for “Accelerating” reaction (mean = -446 mm, median = -492 mm), followed by “walking” (mean = -23 mm, median = 61 mm), “freezing” (mean = 541 mm, median = 520 mm) and “backing-out” (mean = 1222 mm, median = 987 mm), respectively. Impact locations between walking and accelerating and walking and freezing were not statistically different (U = 43, p>0.1 and U = 20, p = 0.052, respectively). Volunteer CG location at impact was also found to be correlated with volunteer location at the time of trigger (R2 = 0.455, p<0.001). This could explain the differences between young and elderly in their distribution of impact location w.r.t. a vehicle (Figure 11) where in 60% cases, younger adults were between the center and far-side the vehicle and 60% of elderly were in the middle of the vehicle.

(a) (b)

Figure 10 Effects of volunteer position at the time of trigger on (a) types of reactions and (b) its position at impact

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Nearside -500 1000

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YoungElderly

Near -1000 -500 Center 500 1000 FarSide Side

Figure 11: Comparison of distribution of impact location with respect to the simulated vehicle between young and elderly

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10

Posture at the Time of Impact

Examples of volunteer postures at the time of trigger and time of impact for different reactions are shown in Figure 12 (see Appendix D for all the trials). No obvious differences in terms of posture were found between the different types of reactions except that the volunteers raised their arms to cover their head when they froze (Figure 13), with shoulder angles up to 125 degrees. Values of angles for the 7 joints in the torso region and the 8 joints in the upper and lower extremities are listed in Table E1 and Table E2 of Appendix E, respectively. Lower extremity patterns were analyzed and classified with further details hereafter.

For young volunteers (Figure 13 (a)), three predominant postural stages were identified for both swing leg and stance leg: forward-swing, mid-swing

and backward-swing. Corresponding to these stages, average knee angles were 153, 94 and 81 degrees, and the average hip angles were 41, 25 and -28 degrees for the swing leg. For the stance leg, average knee angle were 137, 141 and 165 degrees, and average hip angle were 40, 8 and -18 degrees, respectively.

Patterns differed for the elder subjects (Figure 13 (b)). Only two postural stages (i.e. only forward and mid stages) could be identified for both swing leg and stance leg. Corresponding to these stages, the swing leg had average knee angles of 153 and 140 degrees, and average hip angles of 22 and 10 degrees, respectively. For the stance leg, the average knee angles were 135 and 113 degrees, and the average hip angles were 35 and 14 degrees, respectively.

Figure 12: Examples of volunteer posture at the time of trigger (T1) and time of impact (T2) for different reactions.

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Figure 13: Mean (+/- SD) values of knee and hip angles for swing and stance legs for (a) young and (b) elderly volunteers


Muscle Activity

A sudden increase in the muscle activity was observed (as illustrated in Figure 14) during the accident trials. Muscle co-contraction (“bracing”) was also found in some cases, mainly in the upper extremity muscles (e.g., R_Bcp and L_Bcp muscles reach their peak nearly at the same time as shown in Figure 14). Peak normalized muscle activity (i.e. peak value of the ratio of activity in a given muscle during surprise trial to the peak activity in that muscle during normal gait) was above 1 for nearly all the muscles (Figure 15), indicating a higher

activity during surprise trials as compared to the normal gait. The normalized EMG values were relatively higher in the upper extremity muscles (maximum 34.7 in R_Bcp for young in Figure 15(a)) than the lower extremity muscles (maximum 4.2 in R_Ham for elderly in Figure 15 (b)) for both the populations independent to the type of reaction. Moreover, the normalized EMG values in the upper extremity muscles were highest when younger adults froze in apparent fright whereas; it was nearly the same during all types of reactions for elderly. Normalized EMG values at the time of impact for all the trials are given in Appendix F.

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L-Quad L-Gas

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Figure 15: Average values of the peak normalized muscle activity in (a) young and (b) elderly

14

DISCUSSION

The aim of the present study was to observe and quantify pedestrian reactions just prior to a simulated imminent crash with a vehicle that was designed to be unavoidable. Experiments with human volunteers were performed using a custom methodology designed to induce reactions in the volunteers. Based on the volunteers’ feedback, the methodology was successful in creating an immersive virtual pedestrian street crossing scenario. The combination of visual, audio and physical perturbations was intense enough and sufficiently well synchronized to trigger a surprise effect and a reaction in the volunteers that are believed to be realistic. The surprise trials were repeated, however, no significant differences were observed for the types of reaction or reaction time. This suggests that volunteers were not habituated with the repetition of surprise accident events and a reaction was always achieved. These experiments were helpful to observe a possible chain of events during a pedestrian pre crash phase. In the simulated pedestrian environment, most test volunteers perceived the perturbation and seemed to interpret it in terms of danger, triggering a first reaction and a burst of activities after a delay.

Volunteers did not react at all and kept walking normally only in a very few trials. Other reactions included volunteers that ran/sprinted/jumped (in a majority of trials), froze in apparent fright or stepped back. Volunteer speed is one of the parameters which could help in differentiating these activities around 800 ms before the time of impact (i.e. around 300 ms after the trigger), volunteer CG speed corridors were separated for all types of reactions and for both young and elderly populations (Figure 6). This could have interesting implications towards current active safety procedures: it suggests that perception and avoidance strategies of pedestrians confronted by an accident situation may be detectable using speed profiles which could be computed using on board cameras. This may help improve the robustness of active safety algorithms, reducing the number of false positive (for pedestrian accelerating or backing out for example) and help select a strategy for steering maneuvers (select the left or right of the pedestrian) as early as possible.

Differences between the young adult and elderly were also clearly visible in the experimental results. While a majority of young subjects accelerated during the simulated accidents, the elderly were equally divided between froze and accelerated. Also, the elderly were slower than younger subjects in average, with reaction times of 630 ms and 470 ms, respectively. This could be explained by the decline

in reactive abilities, motor performance and physical strength in elderly with aging: several studies suggest that although elderly are more vigilant, they are less active in taking an evasive action against a dangerous situation (Carthy et al., 1995; Overstall et al., 1977 and Verillo & Verillo, 1985). They often take more time in perceiving and reacting to the traffic light signals (Sparrow et al., 2002 and Kausler, 1991). In addition, behavioral studies also suggest that the elderly often expect the driver to brake or to alter their course to avoid the accident (Carthy et al., 1995). This suggests that aging needs to be considered while devising protection strategies.

Volunteers’ posture at the time of impact was found to be highly variable irrespective of the type of reactions except when frozen in fright. In that case, volunteers raised their arms in a triangle to cover their face and head. This is in line with literature which suggests that in fear provoking situations, human tends to acquire the “foetus posture”, with the arms going up in the shape of a triangle to cover the head while the thorax and knee flex (Hochheim et al., 2002).

Muscle activity during the surprise trials, normalized using the peak activity recorded during normal gait, helped illustrate the muscle activity during different reactions. An increase of muscle activity was related to the sudden voluntary or involuntary reactions during the surprised accident trials. This was especially true for the upper extremity muscle activity which increased drastically, especially when the volunteer froze in fright. These results may be useful for the development/improvement/validation of active safety models of pedestrians taking into account muscle activities.

From the passive safety point of view, this study provides information about 51 different pedestrian pre-crash conditions including postures, speed, and muscle activity prior to and at the time of possible impact. In a recent preliminary simulation study, Soni et al. (2013) indicated that the crash avoidance strategies observed in the present paper may affect vehicle-to-pedestrian interaction during the impact. For example, higher speed achieved by a pedestrian who started running after the detection of the oncoming vehicle may affect the pedestrian sliding over the bonnet during the impact and his head may impact farther across the windscreen as compared to the cases where the pedestrian either froze in apparent fright or stepped back. Thus, a more detailed study on investigating the effects of these pre-crash reactions could be useful to study the robustness of existing passive safety designs and methods.


LIMITATIONS

A simulated unavoidable accident situation of 1.1 second was created. The selection of this duration was arbitrary and it could have affected the results of this study. For example, a longer time to impact may not create a sense of danger in a pedestrian. Hence, the results presented here were specific to the test configuration and the event duration and extrapolations to other configurations should be done with caution.

Also, while there were significant efforts to create a situation that generated a sense of danger and triggered a reaction, the realism of the simulator could be improved. The visual scene shown on the screens had a single static vanishing point corresponding to the viewpoint of a pedestrian standing on a sidewalk. Updating the vanishing point on the visual scene relative to the pedestrian position on the virtual street would help achieve a better immersion. Also, fully 3D virtual reality could be useful to improve the realism. However, past studies reported that virtual reality had unpleasant effects such as nausea or loss of balance (Sharples et al., 2008) which could be problematic especially for elderly volunteers. This should be further investigated within the current context.

Elderly volunteers who participated in the experiment were aged between 60 to 75 years however, because of the medical screening before the experiment, they were relatively fit and they were not impaired. Therefore, they may not be truly representative of this age group.

A single traffic pattern was displayed and repeated in loop. It is possible that a volunteer became habituated with the scenario, reducing the realism of the interaction. Moreover, the setup used in this study represented a simplified version of a street crossing, and more complex scenarios could be implemented in future studies to increase the realism.

More generally, the degree of realism of the volunteer reactions is unknown. While volunteers seemed genuinely affected by the stimuli, evaluating the realism of their reaction would require having a real world reference point which seems to be missing. Comparisons with limited data from surveillance cameras at intersections and detailed accident investigations could be attempted in the future.

The triggering of the surprise event (car, launcher and sound) was manual. This caused variability in the volunteer position at the time of trigger. This could have affected the type of reactions and the position of

the volunteer at the time of impact. An automatic triggering based on the real time position of volunteer on the platform may be used in the future to reduce this variability.

CONCLUSION

The reactions of pedestrians subjected to a simulated accident situation were observed. Pedestrians were found likely to accelerate, stop or step back to avoid the collision instead of walking at a constant speed, which seems to be the dominant scenario considered for active safety system evaluation. Pedestrian pre-crash kinematics and muscle activity were documented. These observations may help to design of active safety systems and evaluate their performance. They may also contribute to the validation/improvement of active human models for pedestrian impact.

ACKNOWLEDGMENTS

This research was funded by the European Commission under the Seventh Framework Programme FP7/2007-2013 –International Incoming Fellowship (grant agreement No [252605]-[PedPcReact]). Authors would like to acknowledge the help provided by the LEPSIS research unit from IFSTTAR, and especially to Mr. Daniel Ndiaye and Mr. Fabrice Vienne for their help in customizing the software generating the simulated road traffic. Also, the authors would like to thank to Dr. Laurence Havé for performing medical check-ups and screening the volunteers for the present study and the master student Mr. Bilel Guiga for providing his help during the experiments.

REFERENCES

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Cavallo, V., Lobjois, R.7 and Vienne, F., (2006) The interest of an interactive road crossing simulation for the study of adaptive road crossing behaviour.


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17

APPENDIX A: Volunteer Details

Table A1 Volunteers characteristics and anthropometric measurements

Subject ID

Age (Years)

Sex Weight (Kg)

Stature A1 A2 A3 A4 A5 A6 A7 A8 (All dimensions are in cm)

Y01 24 M 71 176 24 49 47 38 32 82 52 49 Y02 24 M 73.7 177 28 49 49 34 31 84 52 48 Y03 27 M 61.6 171 26 45 47 30 27 77 47 42 Y04 28 M 70.9 165 24 44 47 33 24 83 45 42 Y05 29 M 71.7 168 26 46 44 33 25 85 45 42 Y06 24 M 64.8 169 27 41 46 32 26 85 46 40 Y07 24 M 72.3 184 26 43 53 36 28 76 48 46 Y08 28 F 61.1 167 25 34 42 31 25 76 48 44 Y09 18 F 45.7 163 24 34 41 32 28 75 45 45 Y10 28 M 77.4 180 26 42 49 33 27 82 44 45 Y11 23 F 51.6 160 25 38 46 30 25 72 42 42 Y12 23 F 64.1 164 26 38 48 31 24 87 43 37 E01 66 M 75.5 165 24 45 51 33 26 94 41 39 E02 68 F 58.2 158 24 38 41 30 29 82 41 43 E03 63 M 82 173 24 47 44 32 27 102 42 41 E04 65 M 99 173.5 24 39 48 34 28 105 40 43 E05 65 F 55.4 160 23 36 43 29.5 24 89 39 36 E06 64 M 75.1 169 25 41 47 33 28 90 45 42 E07 64 M 80.2 176 29 41 54 33 26 93 43 44 E08 66 M 74 170 26 39 50 32 28 98 41 41 E09 70 F 64.1 151 30 38 42 31 24 83 40 38 E10 65 F 69.7 157 27 37 45 30 24 94 44 39 E11 64 M 82 172 28 41 50 31 29 94 42 41

A1: Top head to C7

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A3: C7 to Sacrum middle bony tip

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APPENDIX B: Details of simulated accident trials and volunteer reactions

Table B1 Trial details and volunteer reactions (A: Accelerated, F: Froze, B: Backed-out, and W: Walking)

Trial_ID Subject_ID Trial Order Type of Time to First Volunteer Volunteer 1 Y01 1st F 620 1032 -148 2 2nd F 620 1585 440 3 Y02 1st A 280 1399 -683 4 2nd F 360 1863 823 5 Y03 1st W --- 1129 -795 6 2nd W --- 2193 766 7 Y04 1st A 200 566 -1424 8 2nd W --- 1148 -585 9 Y05 1st A 640 1650 -206

10 2nd B 240 1333 616 11 Y06 1st A 160 624 -588 12

Y07 1st A 760 1483 -690

13 2nd A 440 1274 -710 14 3rd A 620 1480 -1265 15

Y08 1st A 720 1480 196

16 2nd A 400 534 -589 17 3rd B 240 1748 1673 18

Y09 1st F 480 1014 183

19 2nd B 400 1110 878 20 3rd A 240 976 -658 21 Y10 1st A 620 761 -617 22 2nd A 640 1235 -416 23 Y11 1st A 800 1565 -325 24 2nd A 400 963 -361 25 Y12 1st F 580 1480 -397 26 2nd A 360 1470 -541 27 E01 1st W --- 1768 350 28 2nd W --- 1760 213 29

E02 1st F 680 1750 520

30 2nd F 740 1898 1461 31 3rd F 360 1802 620 32

E03 1st A 680 1356 -32

33 2nd A 960 1480 -112 34 3rd B 520 1824 987 35 E05 1st W --- 1276 -91 36

E06 1st F 620 1995 969

37 2nd A 620 1840 251 38 3rd A 440 1550 -5 39

E07 1st A 680 1503 -492

40 2nd B 680 1994 1957 41 3rd A 720 1370 -857 42 E08 1st A 660 1754 -50 43 2nd F 680 1645 501 44

E09 1st F 780 1717 328

45 2nd A 440 1710 -249 46 3rd A 720 1246 -514 47 E10 1st A 920 1166 -216 48 2nd F 740 1295 99 49

E11 1st F 520 1869 932

50 2nd F 440 1986 887 51 3rd F 240 1763 896


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2000

x

E09_Surprise_Right

y

z

-5000

500-2000

-1000

0

1000

2000

0

500

1000

1500

2000

x

E09_Surprise_Right2

y

z








-5000

500-2000

-1000

0

1000

2000

0

500

1000

1500

2000

x

E09_Surprise_Left2

y

z

-5000

500-2000

-1000

0

1000

2000

0

500

1000

1500

2000

x

E10_Surprise_Right

y

z

-5000

500-2000

-1000

0

1000

2000

0

500

1000

1500

2000

x

E10_Surprise_Left

y

z

-5000

500-2000

-1000

0

1000

2000

0

500

1000

1500

2000

x

E11_Surprise_Left

y

z

-5000

500-2000

-1000

0

1000

2000

0

500

1000

1500

2000

x

E11_Surprise_Right

y

z

-5000

500-2000

-1000

0

1000

2000

0

500

1000

1500

2000

x

E11_Surprise_Right2

y

z


APPENDIX E: Joint angles defining volunteer posture at the time of impact for all the surprised trials

Figure E1: Definition of joint angles for torso region (in left) and upper and lower extremities (in right)




Table E1: Values of joint angles for torso region for all the trials

Trial ID GKH GHH GHB GBB GBL GLL GLK

x y z x y z x y z x y z x y z x y z y z 1 -4 3 5 -2 -6 8 -1 0 -3 -1 0 8 0 0 0 0 0 8 0 1 2 1 -2 11 1 -4 11 2 -1 -2 2 -1 6 1 -1 1 1 -1 9 -1 4 3 6 -2 11 4 3 15 -2 -1 -9 -2 -1 9 -1 -1 0 -1 -2 5 -1 -4 4 -20 19 5 -15 -4 8 -2 0 -8 -2 -1 9 -1 0 0 -1 -1 7 0 -2 5 -4 5 12 -3 3 6 -1 -1 3 -1 -2 4 -1 -1 0 0 -2 6 -1 3 6 2 -1 1 0 6 8 -4 2 -13 -4 2 14 -1 2 0 -1 3 7 2 -9 7 -31 23 9 -19 -18 14 -8 -1 -8 -8 -1 11 -3 -1 0 -2 -2 7 -1 -7 8 39 -24 -14 24 16 19 4 -1 -15 4 -1 18 2 -1 1 1 -2 11 -1 -9 9 -15 14 -10 -3 -18 -4 1 -2 8 1 -3 -8 0 -2 -2 0 -3 -14 -2 11 10 0 2 -5 1 -2 12 -5 -1 -2 -5 -1 12 -2 -1 0 -1 -1 10 -1 -4 11 -5 -1 4 -4 -4 0 0 -1 -7 0 -1 10 0 -1 0 0 -1 8 -1 -1 12 -8 8 9 -7 3 8 -3 0 -9 -3 -1 11 -1 0 0 -1 -1 6 -1 -6 13 -5 2 10 -6 5 17 3 -2 -6 3 -2 12 1 -2 0 1 -2 9 -2 -3 14 -3 -1 12 -2 -3 18 2 0 1 2 0 6 1 0 1 1 0 10 0 5 15 -7 15 -8 1 -12 21 3 2 -4 3 3 11 1 2 0 1 3 8 3 -6 16 1 14 3 3 5 35 -3 -2 -2 -3 -2 12 -1 -1 0 -1 -2 9 -2 -6 17 -35 33 -28 -11 -27 17 1 0 -2 1 1 8 0 0 0 0 1 6 0 -5 18 -24 12 15 -21 -5 -3 -2 -1 -17 -2 -2 14 -1 -1 0 0 -2 7 -2 -13 19 10 -9 -2 5 3 42 1 2 -18 1 2 26 0 2 8 0 2 22 2 -1 20 -3 4 5 -2 2 14 5 -2 -17 5 -3 20 2 -2 1 1 -3 13 -3 -7 21 -27 24 7 -17 -21 6 -6 -1 -8 -6 -1 9 -2 -1 0 -2 -1 5 -1 -6 22 -2 0 9 -1 -5 0 1 -1 -8 1 -1 11 1 -1 0 0 -1 7 -1 -5 23 -18 25 3 -3 -33 -12 -4 1 12 -4 2 -7 -2 1 0 -1 2 -8 1 12 24 -28 49 -32 -4 -35 19 6 1 10 6 2 -13 2 1 0 2 2 -20 1 29 25 -1 -1 11 -2 1 9 3 -2 -11 3 -2 17 1 -1 0 1 -2 8 -2 -11 26 5 -2 4 4 4 -1 2 0 5 2 0 1 1 0 0 1 0 6 0 7 27 -6 4 0 -5 1 3 2 0 -12 2 0 14 1 0 0 1 0 7 0 -9 28 -31 28 5 -21 -23 6 0 0 -22 0 0 27 0 0 0 0 -1 9 0 -15 29 -20 19 -2 -14 -12 11 -2 0 -6 -2 0 8 -1 0 0 -1 0 4 0 -3 30 -24 22 3 -13 -24 -7 1 -1 -18 1 -2 22 0 -1 0 0 -2 7 -1 -15 31 -26 20 -3 -13 -21 11 -2 0 -14 -2 0 15 -1 0 0 -1 0 8 0 -10 32 -20 5 4 -15 -10 5 1 0 -7 1 0 14 1 0 0 0 0 9 0 -8 33 -10 -2 5 -12 5 3 -2 -2 -6 -2 -3 12 -1 -2 0 -1 -3 13 -3 -4 34 -20 -1 4 -21 -1 -3 0 -2 -2 0 -2 5 0 -2 0 0 -2 8 -2 0 35 2 -1 1 0 6 8 -4 2 -13 -4 2 14 -1 2 0 -1 3 7 2 -9 36 14 -5 1 11 3 5 0 1 3 0 1 -3 0 1 0 0 1 -2 1 6 37 -1 21 -2 21 -27 -14 5 2 8 5 2 -7 2 2 0 1 3 -4 2 17 38 -10 17 -1 3 -20 10 1 1 -19 1 2 21 0 1 0 0 2 9 1 -12 39 -3 1 5 -3 0 17 -2 2 -12 -2 2 16 -1 2 0 -1 3 7 2 -11 40 -29 18 -11 -17 -17 5 0 0 3 0 0 -3 0 0 0 0 0 -4 0 3 41 0 -1 12 0 0 3 1 2 -5 1 2 8 0 1 0 0 2 4 2 -4 42 -4 1 6 -4 1 -3 -1 -1 -8 -1 -1 7 0 -1 0 0 -1 5 -1 -2 43 -21 8 0 -17 -4 8 -3 0 -11 -3 -1 11 -1 0 0 -1 -1 7 0 -6 44 -26 20 -3 -13 -21 11 -2 0 -14 -2 0 15 -1 0 0 -1 0 8 0 -10 45 0 0 5 -1 5 3 -2 -1 -3 -2 -1 5 -1 -1 0 0 -1 5 -1 1 46 -5 3 -1 -4 -2 6 -1 -1 -7 -1 -1 11 -1 -1 0 0 -1 10 -1 -5 47 -15 6 -6 -10 0 18 5 0 -18 5 0 14 2 0 0 1 0 9 0 -8 48 -9 8 0 -3 -9 -5 -1 2 9 -1 2 -8 -1 2 0 0 3 0 2 22 49 -11 12 -3 -7 -9 9 -1 1 -5 -1 1 8 0 1 0 0 1 8 1 1 50 -20 19 -2 -14 -12 11 -2 0 -6 -2 0 8 -1 0 0 -1 0 4 0 -3 51 -23 21 -8 -16 -13 10 -3 0 -11 -3 0 13 -1 0 0 -1 0 8 0 -7


Table E2: Values of joint angles for lower and upper extremity region for all the trials

Trial ID

GHUR GHUL GKNR GNKL x y z x y z x y x y

1 -2 10 24 3 1 11 17 63 -7 18 2 -12 2 10 -3 -8 38 18 26 -6 83 3 12 7 26 0 4 22 -4 44 -10 88 4 0 9 23 6 4 30 3 8 9 37 5 -5 -9 24 12 -6 -9 24 10 -6 29 6 -14 10 33 -3 4 -6 38 28 -4 47 7 6 3 19 -9 0 -20 9 12 1 30 8 2 8 22 -4 4 5 15 16 -13 46 9 -6 -7 58 -9 -11 47 15 99 5 62 10 22 -8 38 -1 2 4 -1 34 -9 53 11 20 -4 32 1 -2 -5 13 36 -16 24 12 5 -8 65 -3 -3 16 23 103 -13 52 13 6 -7 -1 -8 -16 38 7 77 -15 45 14 -3 -9 20 -6 -6 44 18 110 -18 45 15 -6 9 -8 -6 0 39 15 30 -10 34 16 14 -6 38 9 -3 -5 17 31 -17 41 17 -8 -2 -4 4 1 34 16 51 -13 25 18 -4 1 32 10 -4 -7 32 18 -5 2 19 -10 -1 36 17 -8 27 9 36 17 80 20 0 -10 21 4 -21 32 38 84 0 44 21 2 -3 46 -6 -6 -12 15 22 3 8 22 -3 -4 10 -9 -8 30 18 65 3 41 23 3 1 28 -6 -4 -12 30 31 -18 37 24 9 -6 -9 -3 -1 60 28 18 -24 79 25 4 -8 26 0 -12 11 12 63 -18 33 26 12 6 26 6 -3 41 7 52 -18 68 27 -5 8 3 -15 14 33 25 16 -8 53 28 1 4 -9 -10 6 35 16 8 -3 6 29 7 2 8 -20 -6 2 10 5 -7 0 30 -9 3 -4 -12 -7 20 40 29 -14 5 31 1 -5 -14 -15 3 23 5 31 -12 21 32 -1 -4 -3 -12 1 20 27 62 -19 17 33 -4 -6 38 -8 -12 24 20 97 -15 48 34 11 -2 21 -10 0 31 15 105 -8 50 35 -14 10 33 -3 4 -6 38 28 -4 47 36 -18 4 13 -35 1 4 9 10 -11 47 37 -10 0 5 -16 6 34 23 54 -7 23 38 -2 -3 -8 -27 -1 3 20 56 -5 29 39 7 9 15 -1 8 15 14 28 -16 82 40 1 -3 8 -14 2 40 8 22 -3 50 41 5 8 12 4 6 48 11 42 -11 66 42 5 8 27 0 -1 34 16 41 -27 83 43 -11 -10 20 0 -1 27 29 55 -24 52 44 1 -5 -14 -15 3 23 5 31 -12 21 45 1 -3 35 -9 -7 -3 22 53 -9 36 46 -1 -10 26 -4 -6 -8 29 8 -3 34 47 17 -4 0 9 -17 27 10 52 -28 21 48 6 -14 41 2 -15 11 25 53 -10 24 49 12 1 13 -17 -1 11 10 24 -13 52 50 7 2 8 -20 -6 2 10 5 -7 0 51 3 2 16 -23 -4 7 10 4 -4 3


Table E3: Values of joint angles for upper extremity region for all the trials

Trial ID GSR GSL GELR GELL x y z x y z x y x y

1 52 24 65 -33 -33 70 -45 -131 46 -138 2 57 33 61 -45 -33 70 -21 -138 15 -135 3 -17 0 5 -27 -32 35 -20 -112 27 -120 4 4 22 28 -25 -20 62 -32 -139 37 -144 5 34 25 22 -199 -28 29 75 -35 -70 73 6 60 39 47 -66 -36 58 270 -133 -277 -125 7 -2 31 -18 -14 -14 25 -14 -26 39 -82 8 29 23 27 -7 -21 22 -42 -55 19 -96 9 13 1 17 -12 -3 35 -22 -97 -3 -56 10 -8 5 -4 16 8 54 159 -61 15 -56 11 7 54 -10 -14 -30 30 4 -30 0 -60 12 112 2 -110 -115 -42 -63 -182 112 -111 44 13 78 27 -98 -112 -26 -71 161 92 209 81 14 77 35 -112 -95 -28 -43 -205 78 242 41 15 145 41 100 -20 -43 12 179 -150 32 -95 16 -12 61 -89 -31 -19 98 32 -150 17 -113 17 49 25 62 -46 -45 38 -27 -31 15 -34 18 -25 53 -35 21 -68 -45 -28 -23 24 -26 19 68 25 91 82 -116 -83 34 -145 -31 -148 20 56 14 88 31 -65 -31 -32 -150 -19 -85 21 -22 35 -39 -39 -45 19 -7 -47 3 -85 22 -7 48 -4 -8 -53 -18 -9 -78 13 -119 23 42 21 30 -56 -21 66 -36 -45 20 -27 24 45 -18 72 -13 -45 -16 -29 -78 13 -19 25 34 14 77 2 -9 73 -24 -16 67 -133 26 74 145 51 -58 -16 -127 -41 -117 -125 132 27 27 8 -2 -138 6 -77 221 -84 -60 99 28 5 19 12 -1 -47 -7 217 -45 -231 -27 29 59 159 48 0 -29 47 -19 -73 21 -63 30 11 26 14 -21 -20 28 214 -41 -250 -35 31 56 29 27 -6 -27 -5 16 -95 -29 -79 32 23 20 47 -28 -18 13 262 -35 -272 -63 33 27 21 40 -40 -21 36 265 -29 -252 -59 34 20 25 31 -26 -26 22 251 -48 -256 -46 35 60 39 47 -66 -36 58 270 -133 -277 -125 36 68 52 -90 59 -55 -46 -165 64 41 -98 37 240 141 63 57 -50 -49 -178 69 20 -50 38 52 41 -129 70 -61 -62 -174 36 21 -52 39 -51 17 -48 55 -16 -47 -25 -55 3 -49 40 -58 39 -46 50 -28 -77 -25 -68 -5 -83 41 -70 29 -71 88 -29 -80 -11 -65 8 -41 42 -7 33 -5 14 -31 5 10 -57 -23 -56 43 -15 20 -9 15 -34 -1 7 -72 -39 -58 44 56 29 27 -6 -27 -5 16 -95 -29 -79 45 19 28 7 -16 -26 28 62 -12 -4 -74 46 18 28 15 -90 -24 -85 43 -28 174 80 47 84 34 29 -244 -14 19 75 -134 90 95 48 2 58 -18 -8 -29 43 60 -73 -70 -60 49 -109 21 -152 131 -43 -147 -7 -68 18 -76 50 59 159 48 0 -29 47 -19 -73 21 -63 51 -78 25 -103 95 -49 -90 -19 -46 39 -56


APPENDIX F: Muscle activity

Table F1: Peak normalized EMG as a ratio of peak activity during normal gait for 12 during all the trials Trial ID

R-Bcp

L-Bcp

R-Tcp

L-Tcp R-Quad

L-Quad

R-Gas

L-Gas R-Ant L-Ant R-Ham

L-Ham

1 26 7 8 5 2 1 2 1 2 1 3 2 2 38 9 18 33 2 2 3 1 2 1 3 3 3 5 14 22 14 3 11 2 4 3 2 2 9 4 46 72 17 3 4 4 2 4 2 3 3 7 5 1 1 1 1 2 1 1 1 2 0 1 1 6 1 1 1 1 2 1 1 1 2 0 1 1 7 1 1 1 1 1 1 0 0 1 1 2 3 8 17 16 4 7 9 7 2 1 2 1 1 3 9 3 6 3 1 4 1 3 0 1 1 1 2

10 5 2 2 1 1 1 1 1 1 1 1 2 11 10 5 7 9 2 1 1 1 1 1 1 1 12 5 5 3 5 1 1 2 1 1 2 1 1 13 4 4 5 5 2 1 3 1 1 1 3 1 14 7 9 2 1 2 2 1 1 1 1 2 2 15 6 11 4 3 2 2 2 1 1 1 2 2 16 1 1 1 1 1 0 1 0 1 1 1 1 17 58 25 5 1 1 1 1 3 1 2 0 2 18 54 21 5 1 1 1 2 2 3 1 1 2 19 11 9 12 1 2 1 2 2 3 1 1 2 20 4 8 8 4 4 6 1 2 2 2 3 3 21 4 5 4 1 3 3 2 1 2 3 3 1 22 8 6 7 8 3 2 1 1 3 1 3 4 23 6 7 6 7 5 3 3 2 3 1 4 3 24 5 6 7 8 3 2 3 2 3 1 4 6 25 6 2 10 9 3 5 2 1 1 1 2 3 26 8 3 4 6 6 3 3 1 1 1 2 3 27 1 3 2 1 1 1 1 1 1 2 1 1 28 0 1 2 0 1 1 1 1 1 2 3 1 29 4 3 4 1 2 2 2 1 2 1 2 1 30 23 3 2 1 2 4 1 2 1 1 1 2 31 7 3 3 1 2 1 1 1 2 1 1 1 32 7 3 3 1 2 1 1 1 2 1 1 1 33 7 3 3 1 2 1 1 1 2 1 1 1 34 7 3 3 1 2 1 1 1 2 1 1 1 35 6 3 3 1 2 1 1 1 2 1 1 1 36 4 7 2 1 1 1 2 1 4 1 1 3 37 12 4 4 2 1 1 2 2 1 1 2 5 38 2 2 1 1 0 1 1 2 1 1 2 3 39 42 37 15 12 3 1 4 2 1 1 4 4 40 15 21 14 17 3 2 6 1 2 1 7 3 41 22 22 11 5 4 2 3 2 1 1 6 4 42 14 23 4 9 6 5 2 2 2 2 2 4 43 32 12 8 13 10 4 3 2 2 2 3 5 44 8 10 6 10 2 1 2 1 1 2 1 3 45 11 5 7 4 1 1 2 1 1 2 0 3 46 3 7 3 3 2 2 2 2 1 2 1 2 47 11 21 12 12 3 4 2 1 2 3 2 2 48 10 24 12 9 3 4 1 5 2 2 3 2 49 4 19 15 12 3 1 1 2 1 2 6 2 50 6 8 22 11 5 2 2 5 3 2 5 6 51 4 14 10 17 4 1 2 1 2 2 5 3

observations on pedestrian pre-crash reactions during simulated … · 2014. 5. 13. · 2 soni et...

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