Jean-Marc Conrat
email: jeanmarc.conrat@orange-ftgroup.com

Patrice Pajusco

Directional Propagation Channel Estimation and Analysis in urban Environment with Panoramic Photography

This article aims to provide readers with a physical understanding of the propagation channel that is complementary to the mathematical channel modeling. It presents an analysis of the directional propagation channel based on radiophotos. Radiophotos are graphical objects where directions of arrival are superimposed on 3D panoramic photographs. The interaction between EM waves and the environment is immediately identified with these representations. This paper focuses on the direction of arrival at mobile in an urban macrocell environment. The first radiophoto collection illustrates the major propagation phenomena such as the reflection, the diffraction or the street canyoning. The second collection illustrates typical propagation channel profiles that are classified according to the delay, azimuth and elevation spread values. The paper also describes an original panoramic based method for estimating the noise level in the azimuth-elevation domain.

INTRODUCTION

Growing demand for high-speed wireless networks has led to the development of new, more sophisticated radio interfaces. In particular, multi-antenna (MIMO) technology has recently emerged to increase system performance while limiting spectral resource utilization [START_REF] Gesbert | From theory to practice: an overview of MIMO space-time coded wireless systems[END_REF][START_REF] Goldsmith | Capacity limits of MIMO channels[END_REF]. As with any other modern radio interface, development, testing and evaluation of MIMO systems involve extensive computer simulations. Accurate and reliable propagation channel models are therefore required in order to obtain coherent results from the entire simulation chain [START_REF] Almers | Survey of Channel and Radio Propagation Models for Wireless MIMO Systems[END_REF]. Modeling is always preceded by propagation channel characterization. In fact, channel models cannot be defined without the physical understanding of the measurement data. For example, [START_REF] Landmann | Polarization Behavior of Discrete Multipath and Diffuse Scattering in Urban Environments at 4.5 GHz[END_REF] and [START_REF] Laurila | Wideband 3D characterization of mobile radio channels in urban environment[END_REF] present characterisation results in urban areas, such as the identification of reflection/diffraction/diffusion by buildings. However, the measurement physical interpretation is sometimes long and laborious. Simultaneous comparison of measurement results, measurement campaign notes, building or city maps, and standard photographs is time-consuming and can be a source of error. Development of efficient and simple measurement analysis tools is therefore a concern for scientists involved in propagation channel characterisation.

Mixing measurement results with environment photographs is certainly the most convenient way to physically analyse propagation channel behaviour. In 2002, photographs taken with a standard lens onto which representative rays detected by the SAGE algorithm were superimposed, were included in [START_REF] Fleury | High-resolution channel parameter estimation for MIMO applications using the SAGE algorithm[END_REF]. More recently, a method based on panoramic images generated from 6 video streams was presented in [START_REF] Kwakkernaat | High-Resolution Angleof-Arrival Measurements on Physically-Nonstationary Mobile Radio Channels[END_REF]. Omnidirectional video data that were captured during the measurements were used in combination with the measurement results to identify and relate the received radio waves directly to the actual environment. Both analyses were limited to the campus environment with large building separation.

The purpose of this paper is to extend the previous analyses for real urban macrocell environments. The physical interpretation will be based on radiophotos which are defined as the superimposition of 3D panoramic photos, with directions of arrival at the mobile (MS-DoA). 3-D panoramic photos are images with an angle of view that reaches 180° vertically and 360° horizontally. A radiophoto example is given in Figure 1. The 0° azimuth axis represents the BS-MS (base station-mobile station) line. Each path is represented by a circle, the radius or colour of which is proportional to its power. The paper is organized as follow: the second section describes the measurement campaign and the directional propagation channel estimation method. An original method based on radiophotos is applied in order to detect and suppress the noise in the azimuth-elevation domain. The third section shows a large collection of radiophotos highlighting the main physical phenomena involved in the propagation channel in outdoor urban environment. Section 3 also provides radiophotos related to typical measurement files. The typical file selection was achieved according to the azimuth, elevation and delay spread values.

II. MEASUREMENT DATA A) Measurement campaign description

The measurement campaign was conducted in the city centre of Mulhouse, France, where the average height of buildings is about 25 meters. The propagation channel complex impulse responses (CIR) were collected with the wideband channel sounder of Orange Labs (AMERICC) [START_REF] Conrat | A Multibands Wideband Propagation Channel Sounder from 2 to 60 GHz[END_REF] at a carrier frequency of 2.2 GHz and a measurement bandwidth of 62.5 MHz. The Tx antenna was placed on the rooftop of a building at about 30 meters above ground level. The Tx antenna was a 12 dBi gain sectorial antenna with a half power elevation beamwidth of 16 °. The transmitted power was 43 dBm. Figure 3 and Figure 4 show the urban environment viewed from the base station. At the receiver side, a static reference antenna and a Virtual Uniform Planar Array (VUPA) of 21×21 omni-directional dipole antennas were located on the roof of a car (Figure 2). The signal coming from the reference antenna was used for time and phase synchronisation. The synchronisation procedure is detailed in [START_REF] Dunand | Dual-Polarized Spatio-temporal Characterization in urban macrocells at 2 GHz[END_REF][START_REF] Conrat | Analysis of the spacetime Propagation Channel Behavior in Outdoor-to-Indoor Environment[END_REF]. The Tx and Rx antennas were dual-polarized, however, this paper deals only with the results in vertical polarization. A polarization statistical analysis based on this measurement campaign is presented in [START_REF] Dunand | Polarization behaviour in urban macrocell environments at 2.2 GHz[END_REF]. Rx1 to Rx10 in Figure 3 and Figure 4 indicate some Rx locations, the measurement results of which will be discussed in the following next sections.

Measurement antenna Measurement antenna

B) Beamforming processing

A Bartlett beamforming algorithm is used to compute the various directions of arrival at the receiver. The general principle is to synthesize a narrow-beam antenna and scan the environment in all directions. The beamforming algorithm is applied at each delay and the final result is the directional impulse response h(θ,φ,τ), where θ, φ, τ are the elevation, the azimuth and the delay respectively. h(θ,φ,τ) is computed by applying the following formula: The beamforming algorithm is limited to a delay interval that includes CIR non-noisy parts. The noise level is estimated with the minimum of the maximum (MinMax) method illustrated in Figure 5. The CIR is divided in N segments. i max P is the maximum power over segment i.

The noise level P

noise is equal to: Margin compensates the possible difference between the MinMax value and the maximum of noisy segments. The segment number and the margin are manually evaluated from a few CIRs and then applied to all CIRs. The MinMax method is simple and efficient but assumes that a non-neglictible part of the impulse response is noise. This assumption is usually checked as channel sounder excitation sequences are selected much longer than the expected maximal CIR length. A MinMax method adaptation in the azimuth-elevation domain is presented in paragraph II.C).

  min i noise max i 1,N P P margin    (2)
The VUPA horizontal planar symmetry introduces an up/down ambiguity in the elevation estimation. From a strictly theoretical point of view, two rays with the same azimuth but with supplementary elevation angles cannot be resolved. Practically, the metallic plate, on which the XY-rail is set, shadows paths coming from the bottom. We can therefore assume that the measurement system is able to estimate without ambiguity elevations between 0° and 90°. Elevation 0° is the vertical direction.

In order to visualize h(θ,φ,τ) clearly, different power profiles are defined as mentioned in Table 1. Examples of power profiles are shown in Figure 6.

    2 ,, DPP h        Azimuth Elevation Power Profile (AEPP)     2 , , , AEPP h         Azimuth Delay Power Profile (ADPP)     2 , , , ADPP h         Elevation Delay Power Profile (EDPP)     2 , , , EDPP h         Azimuth Power Profile (APP)     2 ,, APP h        Elevation Power Profile (EPP)     2 ,, EPP h       

C) Channel discretization

The discretization process consists in finding the local maxima of |h(θ,φ,τ)| 2 [START_REF] Conrat | Clusterization of the MIMO Propagation Channel in urban macrocells at 2 GHz[END_REF]. h(θ,φ,τ) is converted in a set of ray defined by matrice Ray.

, , ,

i i iMS iMS Ray P       (3)
,, i i i P  and i  are respectively, the power, the delay, the elevation and the azimuth of the i th ray. The method is illustrated in Figure 7. This new representation has several advantages. It dramatically reduces the size of data structures used for saving the results, the computational complexity of successive operations and therefore the computational time. On the other hand, retaining only the peak values from the continuous profiles intrinsically ignores the width of the beamformer main lobe (along the angular dimension) and the measurement equipment rise time (along the temporal dimension).

The noise level determination is a crucial step in the channel estimation method described above. The noise includes all undesirable signals due to the thermal noise, beamforming sidelobes, measurement equipment limits or channel non-stationarity. Regarding the path estimation process described in the previous paragraphs, the noise level defines a threshold above which local maxima detected on power profiles are not considered as physical paths. Its minimal value is theoretically bounded by the estimator space-time sidelobes. The directional impulse response was computed using a Hanning function. Therefore all maxima, the power of which is lower than the maximal power minus 30 dB, are withdrawn from the estimation. Its maximal value depends on the measurement conditions and is calculated for each impulse response delay by the MinMax method extended in the azimuth-elevation domain.

For each delay, h(θ,φ,τ) is divided into N elev ×N azi tules as shown in Figure 8. The noise level P noise_minmax (τ) is processed according equation ( 4). As pointed out in paragraph II.B), the MinMax method is efficient if N elev , N azi and margin are correctly calibrated from a few measurement samples. The calibration consists in comparing P noise_minmax (τ) with a noise reference value called P noise_ref (τ) and calculated thanks the radiophoto. The method is based on two basic assumptions: Firstly, receiving power from the sky is physically impossible. There can be obstacles above the vehicle momentarily, but the obstacle shadowing elevation is practically always lower than the building shadowing elevation as shown in Figure 9. Secondly, the noise is statistically not dependant on the direction of arrival. A sky area is defined inside a panoramic photo where no signal can be received. The maximum power within the sky area gives P noise_ref (τ).

Sky area

Figure 9: Sky area example

The optimal values for N elev , N azi are those that minimize the peak-to-peak value of P noise_minmax (τ)-P noise_ref (τ). Typical N elev , N azi and margin values are respectively 4, 4 and 10 dB. Performance of noise estimation methods are illustrated in Figure 10. The curve denoted Max is equal to the maximum of |h(θ,φ,τ)| 2 at delay τ. The curve denoted MinMax is P noise_minmax (τ) minus margin. The directional impulse response depicted in Figure 8 was computer at delay 0.28 µs. The accuracy of the path estimation method was evaluated by selecting measurement points in Line-of-Sight condition. The main path is defined as the path with the lowest attenuation. As there is no obstacle between Tx and Rx, the main path is the direct path between Tx and Rx. The main path direction was compared with the direction computed from the Rx and Tx coordinates and the main path attenuation was compared to the free space loss attenuation. The angular difference is less than 5° and the attenuation difference is less than 4 dB. The angular difference is not significant as it depends on the Tx and Rx coordinate accuracy as well. The attenuation difference is explained by the addition of small errors on the Tx power, antenna gains or cable losses but it is impossible to characterize all sources of error separately. Nevertheless, even though the path estimation may be locally biased, the measurement data and estimation process are enough accurate and reliable for understanding propagation channel physical phenomena.

D) Radiophoto generation

Panoramic photography is a style of photography that aims to create images with wide fields of view. When the angle of view reaches 180° vertically and 360° horizontally, panoramic photos are called 3-D panoramic photos. The easiest way to obtain such photos is the stitching method. The principle is to take a set of photographs that overlap slightly and to stitch them with specific software. The main drawback is the relatively long acquisition time, about ten minutes, which prohibits all real-time applications. However, consumer products can be used.

Therefore, the stitching method is the most widely used and a lot of information related to the equipment or software is available on the web. The equipment is composed of a conventional digital camera, a fish-eye lens, a tripod and a panoramic head. The fish-eye lens is a very wide-angle lens. The panoramic head is mounted on a tripod (Figure 11) and has two functions: to set the axis of rotation precisely and to rotate the camera in 3-D. Panoramic photography is performed in three steps:

-Panoramic head setting:

The panoramic head should be set in order to match up the entrance pupil of the lens (optical focalisation point) with the rotation centre of the panoramic head. An accurate setting avoids parallax errors and makes image stitching easier. The entrance pupil is located on the lens rotation axis but its exact location is unknown, as lens manufacturers refuse to disclose this data, deeming it confidential. Fortunately, there are very simple methods to adjust the panoramic head.

-Photo acquisition:

First, the number of photographs needed to cover the selected area is calculated. This number depends on the lens angular aperture and on the overlapping rate. The overlapping rate indicates the percentage of common field of view between two consecutive pictures. It is generally considered that an overlapping rate of about 20-25% makes correct stitching possible. As a next step, photographs are shot. In real environments, the light level changes greatly and photographs taken into the sun tend to be over-exposed. In this case, increasing the overlapping rate helps the stitching software to generate a panoramic photo with uniform brightness.

-Picture stitching and panorama export:

The quality of this step depends heavily on the software. We used Stitcher Unlimited software that performs all operations automatically and efficiently. The most time-consuming parts of the processing are the image stitching between neighbouring pictures and the brightness smoothing. Once these operations are completed, the panoramic photography is generated according to a user-defined projection system. We used spherical projection, where the X axis is proportional to the azimuth and the Y axis proportional to the elevation. Figure 12 gives an example of 24 shot at 2 elevations (0 ° and 45 °) and 12 azimuths (30 ° step). The overlapping rate is 50% horizontally and 70% vertically. This configuration leads to a panoramic photograph with a 360° horizontal angle of view and 155 ° vertical angle of view .

III. PHYSICAL ANALYSIS OF THE PROPAGATION CHANNEL

Locations Rx1 to Rx10 are depicted in Figure 3 and Figure 4.

A) Propagation mechanisms

Location Rx1 as shown in Figure 13 highlights two basic phenomena of electromagnetic waves propagation in outdoor environments: the reflection on building walls and the diffraction at horizontal building edges. Vertical building edges can also diffract the electromagnetic wave (Figure 14) although this phenomena is less frequent in macrocell environments where the transmit antenna is well above the rooftop level. The first two examples were simple cases as the preponderant paths impinging the MS undergo less than 2 phenomena. Location Rx2 as shown in Figure 15 shows a much more complex case and illustrates the different propagation mechanisms that have also been described in [START_REF] Conrat | Typical MIMO propagation channels in urban macrocells at 2 GHz[END_REF][START_REF] Kalliola | 3-D double-directional radio channel characterization for urban macrocellular applications[END_REF][START_REF] Toeltsch | Statistical characterization of urban spatial radio channels[END_REF][START_REF] Kuchar | Directional macro-cell channel characterization from urban measurements[END_REF].

Diffraction over Rooftops: Waves arrive at the MS from the rooftop level by diffraction at the edge of roofs. The direction of arrival is generally close to the BS-MS direction, the elevation may be high if the receiver is located near the wall of the building and the relative delays are short.

Street Guided Propagation: Waves arrive at the receiver at street level after travelling through street canyons by bouncing off walls along the street. Elevations associated with this mechanism are generally close to 90°.

Far Scatterer Reflection: Waves arrive at MS after reflection on a building, the height of which greatly exceeds the average rooftop level. A delay of about a few µs may be observed.

Miscellaneous: Other cases include reflection or diffraction on buildings or objects surrounding the MS. When the street axis corresponds to the BS-MS direction, the street canyon effect is enhanced as the different propagation mechanisms described above are combined to increase the received power in the street axis as shown in Figure 16. Similar results were reported in [START_REF] Conrat | Typical MIMO propagation channels in urban macrocells at 2 GHz[END_REF]. [START_REF]WINNER II channel models[END_REF]. Therefore, they are appropriate channel metrics in order to identify typical propagation channels. AS, ES and DS are processed from the discrete representation of the continuous function h(θ,φ,τ). Only rays within a dynamic range of 20 dB are considered in the spread processing. ,, i i i P  and i  are respectively, the power, the delay, the elevation and the azimuth of the i th ray.

DS and ES are calculated using equations ( 5) and ( 6)

( )² with i i i i i i i i i i x P P DS x P P            (5) ( )² with i i i i i i i i i i x P P ES x P P            (6)
AS is calculated according to equation ( 7) and complies with the 3GPP specification [START_REF] Gpp | Spatial Channel model for MIMO Simulations[END_REF] 

           2 mod mod - min with with i i i i i i 1..2 i i i i x P P MS AS AS AS x P P                     (7)
Mod designed the modulo-2π function. and ES is about 8°. Therefore, typical propagation channels described below are not strongly specific to the measurement campaign environment and may be generalized to many types of urban areas. The typical propagation channel selection was done manually and based on a simple observation as shown in Figure 17: ES and AS are correlated, thereafter the measurement points may be classified according to their spatial diversity degree at MS. Three typical channels were defined: "Quasi-Los", "High Spatial Diversity" and "Standard". The first two typical channels are extreme cases that represent roughly 30 % of the measurement points. Furthermore, typical channels are not associated with the BS-MS distance. This statement is motivated both by the joint radiophoto and aerial view visual inspection and by the low correlation between the BS-MS distance and AS or ES illustrated by Figure 18.

Figure 1 :

 1 Figure 1: Radiophoto example

Figure 2 :Figure 3 :Figure 4 :

 234 Figure 2: Receiving and transmitting antennas

Figure 5 :

 5 Figure 5: Impulse response noise estimation with 8 segments

Figure 6 :

 6 Figure 6: Power profiles computed from the directional impulse response, location Rx10

Figure 7 :

 7 Figure 7: Path estimation via local maxima detection. Estimated paths are plotted with red crosses on the DPP plot and with dark points on the ADPP plot.

Figure 8 :

 8 Figure 8: Tule division example

Figure 10 :

 10 Figure 10: Noise estimation as a function of delay, N elev = 4, N azi = 4, margin = 10 dB

Figure 11 :Figure 12 :

 1112 Figure 11: Camera set on the panoramic heading

Figure 13 :Figure 14 :

 1314 Figure 13: Radiophoto location Rx1

Figure 15 :

 15 Figure 15: Radiophoto location Rx3

Figure 16 :

 16 Figure 16: Radiophoto location Rx4

Figure 18 :

 18 Figure 17: AS vs ES

Table 1 :

 1 Profile definitions Delay Power Profile (DPP)

  Before the typical measurement file selection itself, AS, ES, DS mean values are compared with those extracted from similar measurement campaigns and reported in Table2. The carrier frequency ranged between 2 and 6 GHz and the measurement setup enabled direction of arrival estimation at MS. The environment was the outdoor macrocell environment and corresponds to scenario C2 defined by the WINNER European project[START_REF]WINNER II channel models[END_REF]. Results are almost homogeneous, despite the fact that measurements have been performed in different European or Asian cities with a specific architecture or urban density. With the exception of extreme values, DS mean values range from 100 ns to 300 ns, AS mean values are within 50 and 70°

	The minimum and maximum spread values are analytically calculated in case of an equally
	powered azimuth distribution ranged in the interval [0,α]. Spread values ranged from 0
	to		12	. For instance, AS ranges from 0° to 104° and ES ranges from 0° to 28°.

Table 2 :

 2 Measurement campaign summary

		Distance	Bandwidth	Carrier		AS-MS	ES-MS
	Place				DS (ns)			Ref.
		(m)	(MHz)	freq. (GHz)		(°)	(°)
	Bristol	1500	20	2	300	73.6	NA	[19]
	Helsinki	NA	60	5.3	NA	52.3	7.7	[20]
	Stockholm	100	200	5.25	250	80	20	[21]
	Helsinki	800	100	5.3	80-220	48	NA	[22]
	Seoul	500	100	3.7	770	72	NA	[23]
	Beijing	1000	100	2.35	210	65	NA	[24]
	Illmenau	500	90	2.53	70	40	20	[25]
	Winner C2	NA	100	2-6	230	52.5	NA	[17]

Channel "High spatial diversity" (Figure 20): The strongest path propagates less than 20% of the total received power. Some files exhibit a relatively high elevation spread. This occurs when two conditions are met as shown in Figure 21. Firstly, a high building is in the MS vicinity or the MS is close to the building wall. This geometrical configuration creates paths with an elevation ranged roughly between 40° and 70 °. Secondly, a significant part of the power arrives at MS with a low elevation. The addition of paths with low and large elevation creates ES close to 20°. Channel "Standard": Except for some files that exhibit enhanced street canyon behaviour as described in section A), it is impossible to refine the selection as each measurement point is a particular case. Figure 22 and Figure 23 are two examples. The groups "High spatial diversity" and "Standard" have the same mean DS value around 200 ns. Most of the DS are comprised between 30 ns and 400 ns but some files exhibit very large DS between 500 and 700 ns. This usual situation occurs when the impulse response is divided into two discontinuous parts as shown in Figure 24 and Figure 6. The first part is due to the interaction of the electromagnetic wave with the environment in the MS vicinity or close to the line BS-MS. The second part is generally an interaction with a far building and generates paths with excess delay higher than 1.5 µs. This type of files agrees with environment C3 defined by the Winner project and labelled as "macro-cell bad urban ".

IV. CONCLUSION

The radiophoto examples given in this paper have shown the huge potential of radiophotos for propagation measurement data validation and analysis. In particular, the impact of the environment on the space-time propagation channel is immediately identifiable. Future work based on this technique will focus on the validation of simulation techniques, such as ray tracing or ray launching. Simulation results will be added to radiophotos, which should improve the understanding of the influence of geographical database accuracy on simulation reliability.

APPENDIX

Radiophotos do not provide any information about the path relative delay due to the intrinsic nature of the panoramic photo. However, the radiophoto principle can be extended to the video domain by generating radiovideos that show the AEPP time evolution dynamically. Radiovideos are multimedia documents that cannot be inserted in a journal paper but radiovideos related to the radiophoto examples given in this paper are available at http://perso.telecom-bretagne.eu/patricepajusco/jmwt/.

Bibliographies


Robert Thomas

Beillas Philippe

VERRIEST Maupas Alain

Conditions of possible head impacts for standing passengers in public transportation: an experimental study

Standing passenger safety is currently a major challenge for the development of public transportation. One of the current difficulties to overcome this problem is the lack of knowledge about the motion of passengers following a minor incident, such as an emergency braking or a slight collision.

This study brings new experimental data about the head kinematics of volunteer subjects in situations representative of typical public transportation incidents. Two levels of perturbation were tested -representing an emergency braking and a minor collision -combined with 3 initial postures -free standing, using a back rest and holding a vertical bar. Data obtained were: 1/ the 3D head trajectories; 2/ the maximal excursions of the head along the longitudinal axis; 3/ Corridors of the head tangential velocity versus its longitudinal displacement.

Even though there are limitations, these data can be used as a first estimate to predict the risks of impact between the head of passengers and their surrounding environment. They also provide insight into the impact velocity of this eventual collision. Furthermore, they highlight the influence of the level of perturbation and of the restraint device possibly used. These data are mainly intended to be used by designers of public transportation vehicles to improve the future interior layouts with regard to the standing passengers safety.

Introduction

Passenger safety is currently a major challenge for the development of public transportation.

One of the key issues is related to minor but frequent incidents. The injury data about incidents without collision (e.g. emergency braking) in public transportation highlight this problem For example, the American Bureau of Transportation Statistics reported 8 deaths and 20967 persons injured in one year due to incidents without collision in urban and interurban buses in the United States [START_REF]Table 2-15[END_REF]. Similarly, De Graaf and Van Weperen reported that from the 2300 persons brought to the Emergency Department following an incident in public transportation in one year in Netherlands, 1200 of them were injured in incident without collision [START_REF] De Graaf | The retention of balance: an exploratory study into the limits of acceleration the human body can withstand without losing equilibrium[END_REF].

Recent surveys on transport safety [START_REF] Pereira | Passive Safety in Urban Railway Systems, Light Rail Vehicle (LRV) -Accident Statistics[END_REF][START_REF] Björnstig | Injury Events Among Bus And Coach Occupants -Non-crash Injuries as Important as Crash Injuries[END_REF][START_REF] Halpern | Non-collision injuries in public buses: a national survey of a neglected problem[END_REF] have provided a better knowledge of the risks associated with this type of incident. These studies report that the most exposed passengers are the standing passengers: even slight incidents can induce important losses of balance, which may lead to secondary impacts with other occupants or interior equipments, and thus cause injuries. These studies also report that the most injured segment is the head (between 23% and 33 % of the injuries), followed by the upper limbs (between 20% and 28%) and the lower limbs (between 18% and 21%). Most of these injuries appear to be minor or moderate (within 85% and 98 % of AIS ≤ 2). However their social cost and their high frequency led the authors to judge that this situation is situation the most critical one [START_REF] Pereira | Passive Safety in Urban Railway Systems, Light Rail Vehicle (LRV) -Accident Statistics[END_REF].

Moreover, there are some aggravating factors: 1/ there will be more vulnerable passengers in the future because of the general ageing of the population and because of the increasing proportion of standing passengers in the new vehicle interior designs; 2/ the frequency of critical incidents should increase due to the apparition of new, complex, traffic situation (e.g. hybrid train-tram vehicles); 3/ this safety issue was widely neglected and starts only to be considered at its true value [START_REF] Halpern | Non-collision injuries in public buses: a national survey of a neglected problem[END_REF].

One possible solution to improve the safety of passengers would be to limit the aggressiveness of vehicle interior designs. To achieve this, it would be crucial to be able to evaluate the risk of injuries associated with incident conditions (interior design, location and posture of the passengers, level of perturbation, etc). This would mean: 1/ to determine whether a impact may occur and against which structure; 2/ to determine the conditions of this impact (e.g. impact velocity); 3/ to estimate the injury risks associated with the impact.

Existing biomechanical data collected for other applications such as automotive safety or sport injury could be used to try to achieve this last point. However, one of the current difficulties is the determination of the impact conditions, and particularly the kinematics of the impacting segment (step 1 and 2). Although many studies about postural balance have been carried out (e.g. [START_REF] Do | A biomechanical study of the balance recovery during the fall forward[END_REF][START_REF] Maki | Influence of lateral destabilization on compensatory stepping responses[END_REF][START_REF] Pai | Center of mass velocity-position predictions for balance control[END_REF][START_REF] Runge | Ankle and hip postural strategies defined by joint torques[END_REF][START_REF] Hsiao | Biomechanical influences on balance recovery by stepping[END_REF][START_REF] Henry | Effect of stance width on multidirectional postural responses[END_REF]), the configurations tested did not reflect the real situations encountered in public transportations. Therefore, there is currently a lack of pertinent data on the kinematics of standing human in situation that is representative of public transportation incidents.

As a first priority, it would be of interest to study the risks of impact for the head since: 1/ this segment is the most frequently injured; 2/ some of the arm injuries (the second most injured segment) can be explained by their use to protect the head [START_REF] Mcilroy | Compensatory arm movements evoked by transient perturbations of upright stance[END_REF][START_REF] Maki | The role of limb movements in maintaining upright stance: the "change in support" strategy[END_REF][START_REF] Kim | Biomechanics of fall arrest using the upper extremity: age differences[END_REF].

In this study, 10 volunteer subjects were placed in situations representative of public transportation incidents (e.g. emergency braking and light collision). Their balance recovery was measured and analyzed in terms of 1/ head 3D trajectories; 2/ maximal head excursion; 3/ tangential velocity versus of head position corridors. These data can be used to help estimating: 1/ the risk of secondary impact with an element of the interior design; 2/ the impact velocity of this impact. They could be used by the designers to improve the future interior layouts with regard to the standing passenger safety.

Test set-up

The aim was to reproduce in the laboratory the situation of a standing passenger in a public transport vehicle submitted to a real-life incident (e.g. an emergency braking or a minor collision). Volunteer subjects were standing on a platform, initially immobile. The platform was connected to a sled by an elastic link and a cable. The sled was launched by a catapult, tightening the cable and applying a pulse to the platform (see Fig. 1 a). Because the link was elastic, the shape of the acceleration pulse was a half sinusoid.

Experimental situations tested

Two independent variables were investigated: 1/ the level of perturbation; 2/ the initial posture of the subjects, corresponding to the restraint device possibly used.

Two acceleration pulses, representing two levels of perturbation, were tested. Both had a duration of approximately 400 ms and a maximum acceleration of 2 m/s² or 10 m/s² (see Fig. 1 b). Those were defined by the EC funded SAFETRAM project consortium as equivalent to an emergency braking situation and a low severity collision respectively. The perturbations were applied in the AP direction, with the platform moving towards their back.

Fig. 1 about here Three initial postures were used by the volunteers: free standing with no device, holding a vertical bar, and resting their back against the back of a seat (see Figure 2). The volunteers were subjected to each possible combination of these two independent variables, resulting in six configurations per subject tested in a random order (see Table 1).

Table 1 about here

Measurements

Kinematics of balance recovery was recorded using a Motion Analysis® opto-electronic system. Fifty reflective markers (25mm spheres) were placed on specific external points of the volunteers. Five red light cameras, with a sampling frequency of 200 Hz, measured their 3D trajectories during the movement.

Population

For this first study, only one homogeneous group of population was investigated. It was composed of ten young male subjects (mean: 25.6 years old, s.d.: 2.4), with an average stature (mean: 176.8 cm, s.d.: 3.1) and mass (mean: 72.6 kg, s.d.: 4.8). They were recruited outside the institute and were thus naïve to the experiments. They were submitted to a preliminary medical examination to ensure that they never had equilibrium troubles or any medical interventions on the lower limbs.

Instructions and surprise effect

For each test, volunteers were asked to stand relaxed on the platform, as if they were on board of a public transport. They were only instructed to use the available devices (vertical bar or bump rest).

Although the subjects knew that a longitudinal perturbation would occur, measures were taken in order to observe the most natural responses possible. Volunteers could not see the moving sled starting, and a headphone insulated them from the sounds of the laboratory. As the test order was randomized, they could not anticipate the beginning and nor the magnitude of the pulse. Furthermore, an operator asked them a succession of short questions (such as arithmetic calculations or words spelling) via the headphone, in order to distract their mind from the balance recovery.

Safety of the subjects

In order to prevent any fall or impact with the environment, subjects wore a harness linked to a beam fixed to the platform above them. This device did not interfere with the balance recovery movements. This protocol complied with the terms of the law on biomedical experiments with human living subjects and was approved by the regional ethical committee.

Data processing

Movement reconstruction

Balance recovery movements were reproduced on the numerical dummy Man3D developed in the laboratory [START_REF] Verriest | Simulation of human movement for ergonomic design on numerical mock-up[END_REF]. The kinematic model of this dummy was simplified to 15 segment linked by joints (40 degrees of freedom), and its dimensions were fitted to each subject anthropometry. Then an optimization method was applied at each frame of the movement to find the set of joint angles (i.e. dummy posture) that best fitted the subject measured position [START_REF] Verriest | Simulation of human movement for ergonomic design on numerical mock-up[END_REF]- [START_REF] Robert | Analyse biomécanique du maintien de l'équilibre debout suite à une accélération transitoire de la surface d'appui -Application à l'amélioration de la protection des passagers debout de transport en commun[END_REF].

Joint angles were filtered using Butterworth 2 nd order zero lag filters. Cut-off frequencies were adjusted for each degree of freedom using the residual analysis method [START_REF] Winter | Biomechanics and motor control of human movement[END_REF]- [START_REF] Silva | Human motion analysis using multibody dynamics and optimization tools[END_REF]. This resulted in cut-off frequencies between 7 Hz and 20 Hz, depending on the motion and the degree of freedom.

The accuracy of this method has been evaluated [START_REF] Robert | Analyse biomécanique du maintien de l'équilibre debout suite à une accélération transitoire de la surface d'appui -Application à l'amélioration de la protection des passagers debout de transport en commun[END_REF]: the average distance between the measured positions of the markers and their corresponding points on the virtual dummy was less than 27 mm, which is negligible compared to the amplitudes of the studied motions.

Time range of the motions

The time origin was chosen at the onset of the platform motion. The end was defined at the time when the longitudinal velocity of the head (along X axis) equalled zero. However, the translation of the platform relative to the optoelectronic system induced measurement difficulties, such as loss of markers at the end of the movement. Therefore, some trials could not be reconstructed for the total duration of the movements (see Table 2). These missing parts are near the end of the movement, therefore corresponding to small displacements and low velocities. In those cases, the missing kinematics of the head CoM for the end of the movement was estimated based on the use of raw head markers positions or the trend of the displacement curves. The average correction of the displacement at the end of the motion was at most 14% of the total head displacement along the X-axis (case of the experimental situation #4).

3D head kinematics

The head was represented by its Centre of Mass (CoM). As a first approximation, this point was considered to be the cranio-cervical joint centre, which is the closest dummy's point (point teco). The distance between these two points (teco and CoM) was estimated to be around 40 mm, which is small compared to the amplitude of the motion. Moreover, it was verified that this assumption had a negligible influence on the computed head velocity.

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For each frame, the CoM coordinates in the reference frame of the platform was computed using the reconstructed data. In order to compare the 3D head trajectories for the different trials, the CoM coordinates were set to zero at the onset of the platform.

Velocities of the head relative to the reference frame of the platform were computed along the three axes by differentiating the head CoM coordinates. The tangential velocity V t was then computed as the norm along the 3 axis of these velocity vectors.

Response corridors for the head tangential velocity

In order to represent the variability in the subject reactions in each of the six configurations, response corridors of the head tangential velocity as a function of the head location were built as follows.

The head tangential velocity (ordinate) was computed as a function of the head location (abscissa) along one of the 3 reference axes. All velocity data were then interpolated on a common, equally spaced, abscissa vector (one point every 1 mm). The mean and standard deviation of the tangential velocity of the head were calculated at each point of this common vector. Corridors boundaries were defined as the mean head tangential velocity plus or minus one standard deviation.

The length of the common abscissa vector was defined by the movement with the maximal head excursion. If a movement ended normally (head velocity along X-axis equals zero), its velocity was assumed to remain equal to zero after this point. If a movement ended because it was not fully reconstructed (see Time range of the motions above), it was not considered anymore after this point and the mean and standard deviation were calculated using the remaining movements.

Results

Head trajectories

3D head trajectories were plotted for each experimental conditions and every subject (Figure 3). Three positions of the whole body model were superimposed in order to illustrate the whole body kinematics of the balance recovery (first, intermediate and last frame of a typical subject).

Fig. 3 about here

This figure shows that the balance recovery kinematics and the head trajectories varied widely with the experimental situation. The low level of perturbation, while using a back rest, induced only a really small motion of the top of the body. Besides, although none of the subject fell on the ground, the highest level of perturbation induced a very important disequilibrium for the free standing posture.

The head of the subjects moved mainly along the X-axis (longitudinal axis). This was verified even for the asymmetric situations where the subject grasped the bar: for the experimental situation number 6 (high perturbation, grasping a bar) the maximal excursion along the Y-axis averaged for every subjects was only 17,4 cm -approximately the same as for the Z-axis (15,8 cm) -while this value was 96,6 cm along the X-axis. Consequently, in the rest of this paper, head kinematics is only investigated along the longitudinal axis (X-axis).

Maximal excursion of the head

The maximal excursion of the head was defined as the maximal X-coordinate of the head CoM during the movement.

Results are displayed in Table 2. They highlight the differences of motion amplitude depending on the experimental situation: The head excursions were lower than 150 mm for the experimental situation #3 (use of a back rest, low level of perturbation), while they were higher than 1500 mm for the experimental situation #2 (free standing, high level of perturbation).

The use of a restraint device limited this excursion of the head. At low level of perturbation, the back rest was extremely efficient. However, at higher level of perturbation, the opposite was observed: gripping a vertical bar limits more the head longitudinal displacement than using a back rest.

Table 2 about here

Alternative strategies

Subjects could adopt different strategies to restore their balance, as already described in a detailed analysis of the whole body kinematics ( [START_REF] Robert | Analyse biomécanique du maintien de l'équilibre debout suite à une accélération transitoire de la surface d'appui -Application à l'amélioration de la protection des passagers debout de transport en commun[END_REF], [START_REF] Robert | Kinematic modelling of balance recovery movements of a standing passenger following its support surface translation[END_REF]). Therefore, presence of alternative strategies was investigated on the basis of the maximal excursions of the head.

For each experimental situation, bar charts of the maximal head excursion were plotted (see Fig. 4). From these graphs, two different strategies (main strategy and alternate strategy) can be identified for the experimental situation 1 and 5 (low level of perturbation, free standing and grasping a vertical bar respectively). The meaning of these strategies will be discussed in the next section of this paper.

Fig. 4 about here.

Head velocity

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It is important to notice the difference of amplitude, both for X and V t , between the corridors corresponding to the low and high level of perturbation: 100 to 2500 mm for the maximal head excursion, and from 300 to 2500 mm/s for the maximal head velocity.

The effect of the restraint devices was negligible at the beginning of the motion: corridors are nearly superimposed during the first 30 mm or 300 mm for the situations at low or high level of perturbation respectively. It can be noticed that although these common parts of the motions represents only almost 10% of the maximal head excursion, the tangential head velocities reached in this area from 30% (free standing -low perturbation) to 100% (back rest -high perturbation) of its maxima.

However, the use of a restraint device allowed limiting both the maximal head excursion and the maximal head tangential velocity. This influence was dependant of the level of perturbation, mainly for the head velocity. Indeed, the capacity of a device to limit the maximal head velocity decreased when the level of perturbation increased: at low level of perturbation, the maximal head velocity was divided by almost 2 or 2.5 using a vertical bar or a back rest respectively, while at high level of perturbation these factors were only within 1.2 and 1.7 (vertical bar or a back rest respectively).

Discussion

Influence of the restraint devices

The analysis suggested that the two restraint devices tested (vertical bar and back rest) acted differently. This can be seen on the head velocity versus head displacement curves. The use of the back rest limited the duration of the first part of the curves -that corresponds to the speed increase -without affecting the overall shape of the curves. On the opposite, the vertical bar affected mainly the second part of the curves: it increased the slope of this descending part, thus limiting the head excursion. A schematic illustration of this effect is provided on Fig. 6. One possible explanation for this behaviour, and that is in accordance with the proposed interpretation of the feeling of disequilibrium indicated by the subjects [START_REF] Robert | Experimental study of the balance control of standing passengers in public transport[END_REF], is the following:

1/ The use of a back rest increase the steadiness of the initial posture, and thus limits the disequilibrium. However, it does not help the subject to restore their balance once the disequilibrium is induced.

2/ Grasping a vertical bar does not limit the initial disequilibrium, as it does not modify significantly the initial steadiness of the standing posture. However, it offers the possibility to restore the balance after the start of the fall forward.

Meaning of the alternative strategies

For two experimental situations (free standing and grasping a vertical bar, low level of perturbation), alternative strategies were observed (see Table 3). These alternative strategies may be due to the low level of constraints imposed on the subject responses. Purposefully, few instructions were given to the subjects in order to observe the diversity of individual responses. Since the disequilibrium induced by the low level of perturbation was small, it may not have been sufficient to force the subjects to use the most efficient strategy to restore their balance. This was obvious in the experimental situation #1 (free standing -low level of perturbation): while most subjects tried to restore their balance as fast as possible, three subjects took advantage of the space available in front of them and "let themselves walk" (see Fig. 7). Thus, their movement was characterized by a higher distance to restore their balance and a lower head tangential velocity.

Similarly, for the experimental situation #5, all subjects were gripping the vertical bar and most of them used it to shorten their recovery motion. However, 3 of the subjects did not: their head motions were similar to those observed in the main strategy (2 of the 3 subjects) or the alternative strategy (1 of the 3 subject) of the experimental situation #1. This is illustrated by the head tangential velocity versus its X-coordinate curves on Fig. 7. Fig. 7 about here When submitted to the higher level of perturbation, subjects had to use the most efficient strategies to avoid falling. As a consequence, the dispersion of maximal excursions of the head was lower for trials at high level of perturbation, and no real alternative strategies could be observed.

The reason why certain subjects chose alternative strategies is uncertain. One possibility is that the choice of an alternative strategy was done voluntarily by the subjects, in order to restore their balance in an easier way than the main (most efficient) strategy.

If such a voluntary choice is possible, we could suppose that, in presence of an obstacle in front of them, subjects would adopt the most efficient strategy (here presented as the main strategy) to avoid the risk of impact. This is however only a working hypothesis and the influence of obstacles on the balance recovery motion should be studied separately.

Risks and conditions of secondary impacts

Predicting of the risk and condition of impact between subjects and their environment using the experimental data presented in this paper requires to make an hypothesis: the presence of an obstacle on the trajectory of the subjects would not have modified their balance recovery kinematics, measured here in an open space.

At first, this appears like a strong assumption. However, the analysis of the strategies indicated that, for the main strategies, subjects may have tried to restore their balance in the most efficient way possible. If the subjects were already near their maximum level of performance, then it is likely that the balance recovery kinematics would be only slightly modified by an attempt of obstacle avoidance. The potential use of the upper limbs would however have to be investigated.

Overall, although further studies should focus on those points, the assumption may be reasonable for those preliminary results. Furthermore, even if the passengers would manage to modify significantly their balance recovery kinematics, the prediction could be considered to be a conservative measure in the process of estimating the risk of impact. Therefore, it appears reasonable to use the corridors of the head tangential velocity function of its location presented in Fig. 5 as insights to estimate the risks and conditions of a potential head impact.

Caution should be used when using the results towards the end of the movement (free standing situation in particular) because of: 1/ the limited number of subjects still in motion and 2/ the fact that they are less constrained by the perturbation since they have almost completely restored their balance.

It can be observed that head excursions were large mainly at high level of perturbation: between 1 and 2 meters (see Table 2). Related to the lack of space available in the interior of a public transportation vehicle, these distances suggest a high risk of secondary impacts.

Moreover, the head tangential velocities were quite large for the higher level of perturbation: they reached up to 3 m/s in free standing posture. These are of the same order as the secondary impact velocity experimentally observed for seated inert dummies in a train crash situation (between 4 and 7 m/s in a crash of a train, running at 110 km/h, against a 15 tons truck stopped on the railway at a crossing [START_REF] Simons | High-Speed Passenger Train Crashworthiness and Occupant Survivability[END_REF][START_REF] Tyrell | Train Crashworthiness Design for Occupant Survivability[END_REF]). Moreover, the maximal head velocities happened relatively far from the initial position (up to 60 cm for the experimental situation #2), which means in zones likely to be impacted. It can also be remarked that in every cases the use of a restraint device limited the risk of head impact and the potential velocities of these impacts. However, if they seemed really effective at low level of perturbation, they may not be sufficient to protect passengers at high level of perturbation. This is illustrated by the fact that, at high level of perturbation, the head velocity was higher than 1 m/s nearly 1 meter away from its initial location, even using a restraint device.

Limitations and perspectives

One of the main limitations of this study concerns the potential influence of obstacles on head motions. Even if indications, such as the analysis of the alternatives strategies, suggest, for the levels of perturbation considered, this influence is limited, future studies should focus on this point.

Moreover, as a first study, the aim was not to be exhaustive. More data should be data collected. Other directions of perturbation should be tested for example. Another point concerns the group of population tested. Young males that performed these experiments did not represent the most critical group of population. It would have been more interesting to study the reactions of elderly or disabled persons. However, due to evident safety problems, this is not experimentally possible. Therefore, a future objective would be to model the balance recovery, based on experimental data collected with young and healthy subjects.

These models could then be modified to fits with other groups of population.

Conclusions

This study brings new experimental data about the head kinematics of volunteer subjects in situations representative of typical public transportation incidents. Two level of perturbation were tested -representing an emergency braking and a minor collision -combined with 3 initial postures -free standing, using a back rest and gripping a vertical bar. Data obtained were: 1/ the 3D head trajectories; 2/ the maximal excursions of the head along the longitudinal axis; 3/ Corridors of the head tangential velocity versus its longitudinal displacement.

Even though there are limitations, these data can be used as a first estimate to predict the risks of secondary impact between the head of passengers and their surrounding environment. They also provide insight into the impact velocity of this eventual collision. Furthermore, they 5 highlight the influence of the level of perturbation and of the restraint device possibly used.

These data are mainly intended to be used by designers of public transportation vehicles to improve the future interior layouts with regard to the standing passenger safety.

Fig. 2

 2 Fig. 2 about here

Figure 5

 5 Figure 5 represents the head tangential velocities (corridors and averages) as a function of its

Fig. 5

 5 Fig. 5 about here.

Fig. 6

 6 Fig.6about here.

Figure 2 :

 2 Figure 1: Experimental setup

Table 2 :

 2 Maximal excursions of the head (mm).

Acknowledgement

These experiments were conducted within the frame of the EC funded SAFETRAM project, in collaboration with the French railways company (SNCF). The authors which to thank the persons who voluntarily agreed to be subjects of these experiments.

Low perturbation