Aleksandr Oseev
email: a.oseevaleksandr.oseev@femto-st.fr

Fabien Nikolay Mukhin

Guillaume Mourey

Alain Rouleau

Ophélie Bourgeois

Benoit Le Roy De Boiseaumarié

Emmanuel De Maistre

Ralf Lucklum

F Remy-Martin

C Elie-Caille

T Lecompte

F Chollet

J.-F Manceau

W Boireau

T Leblois

Presenter : A Oseev

Besançon

Magdeburg

Geneva

Dijon Oseev

Assessment of primary hemostasis with an acoustic biosensor using shear dependent kinetics behavior: principle and limitations

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Background and Objective:

Primary hemostasis involves in-flow interactions between platelets and sub-endothelial matrix at the wall of the damaged vessel. Assessing primary hemostasis defects would benefit from evaluation of the whole sequence of processes involved in platelet plug formation. We propose a novel label-free approach based on characterization of shear-dependent kinetics to evaluate the early stages of primary hemostasis. We developed a quartz crystal microbalance (QCM) biosensor to measure the amount of platelet deposited over time. With experiments and numerical simulations, we investigated the relevance of this approach and its limitations.

Methods:

We designed and built an acoustic biosensor based on a QCM whose gold surface was functionalized with Horm® collagen and used as the floor of a microfluidic chamber. We recorded with an impedance analyzer the variations of the QCM sensor resonance frequency during a 5-minutes perfusion through the chamber with anticoagulated whole blood from two healthy donors. The realtime QCM measurements performed at 500 -1500/s range shear rate were supplemented with atomic force microcopy (AFM) observation at the end of the perfusion to evaluate the final morphology of the deposit and the surface coverage. Numerical simulations were used to understand the influence of deposit topology on the acoustic response.

Results

:

For analyzing the complex kinetics profile of the frequency shift, we defined three metrics: total frequency shift, lag time, and growth rate. These metrics enabled the characterization of the kinetics of platelet deposition with good repeatability. We showed that these parameters measured at different shear rates, gave precise indications on the processes involved in the early stage of primary hemostasis, opening the way to analyze abnormal behavior. However we observed that the frequency shift was not always a direct measure of the platelet amount and depends on the surface topology of the deposit, which varies with the shear rate. The numerical simulation confirmed that if a platelet deposits is modeled as a structured viscoelastic load, the surface coverage affects the frequency shift of the sensor.

Conclusion:

Shear-dependent kinetics assays seems to be a promising method for studying primary hemostasis and its defects. We showed that QCM sensor measurements have to be combined with a precise evaluation of deposit topology to be fully usable.

Submitted Abstract

Instructions  Submission of the abstracts according to subject area and interdisciplinary area.  The authors will have to tag each abstract with one subject area and up to three interdisciplinary area.  The jury will decide on the abstracts to be presented as an Oral Communication or Poster presentation. Only anonymized abstract submissions will be graded. After acceptance, the authors will receive a notice from the organization / the scientific committee about the decision of acceptance by e-mail.

Background and Objective

Primary haemostasis is a complex dynamic process, which involves in-flow interactions between platelets and sub-endothelial matrix at the area of the damaged vessel wall. The diagnosis of primary haemostasis defect would benefit from evaluation of the whole sequence of mechanisms involved in platelet plug formation. This work proposes a new approach that is based on characterization of the shear-dependent kinetics that enables the evaluation of the early stages of primary haemostasis. We propose to use a label-free method with a quartz crystal microbalance (QCM) biosensor to measure the platelet deposits over time onto covalently immobilized type I fibrillar collagen. Using experiments and numerical simulations, we investigate the relevance of the approach and its limitations.

Methods

We designed and built an acoustic biosensor based on a QCM sensor placed at the bottom of a microfluidic chamber. The chamber was designed to obtain wall shear rates in a range of 500 -1500s -1 with whole blood flow. The QCM gold surface was functionalized to immobilize Horm® collagen by its primary amines at room temperature overnight. The biointerface was then blocked with 0.1% of BSA in sodium acetate buffer solution 10mM pH 5.5 to prevent non-specific binding.

We recorded the variations of the QCM sensor resonance frequency during five minutes perfusion with anticoagulated whole blood with an impedance analyzer. In the experiments, we used the whole blood of two healthy donors, testing shear rate in the 500 -1500s -1 range. The real-time QCM measurements were completed with atomic force microcopy (AFM) observation of the deposit at the end of the perfusion to evaluate the final morphology of the deposit and its surface coverage.

Numerical simulations were used to understand the behavior of the system and particularly the influence of surface coverage on the acoustic response.

Results

For analyzing the complex kinetics profile of the frequency shift we defined three metrics: total frequency shift, lag time, and growth rate. These metrics allowed to characterize the complex kinetics of the platelet deposit for the different shear rates with good repeatability. We show that these parameters measured at different shear rate, give a precise picture of the different mechanisms involved in the early stage of primary heamostasis, opening the way to analyze pathological behavior.

However we observed that the frequency response was not always a direct measure of the platelet amount and depends on the surface coverage of the deposit, a topology that varied with the shear rate. The numerical simulation confirmed that if the platelet deposits is modelled as a structured viscoelastic loads, the surface coverage affects the frequency shift of the sensor.

Conclusion

Shear-dependent kinetics assays seems to be a promising advanced method for screening of primary haemostasis. However, the use of QCM sensor alone is not sufficient to evaluate accurately the amount of deposited platelet and it has to be combined with a separate evaluation of the deposit surface coverage. left) Height profile of platelet deposits across the biointerface for shear rates 500s -1 (blue curve) and 1500s -1 (orange curve) (average height for 500s -1 (blue) and 1500s -1 (orange) are shown with dotted lines). Insets: AFM images (height trace) of platelet deposits obtained at 500s -1 (on the left) and 1500s -1 (on the right) shear rates respectively. (right) Optical microscopy image of platelets deposits on collagen biointerface 500s - 1 (on the left) and 1500s -1 (on the right).



  Author-names, institution/clinic names MUST NOT appear in the abstract-title, abstract-text, in the annexes or in the remarks. You can cite your previous work, without reporting references of your publications. Important: The jury has to REJECT your abstract, if the above-mentioned requirements are not met.  The author and the co-authors have full responsibility for the content of the abstract! o Writing style: Clarity and simplicity, fluidly written, no unexplained abbreviations/terms. o ACCURACY: Be careful to avoid spelling and grammatical errors. o Accepted abstracts will be published WITHOUT REVISION OR CORRECTION.  Abstract language is English only. Please enter both the abstract and the information on the authors completely in English.  Deadline for the abstract submission is October 18, 2020 (12:00 a.m.).  Abstract should have 2400 Total characters between hemostasis and other systems Diagnostics and laboratory tests Hereditary bleeding disorders In vitro and in vivo models of hemostasis Innovation and Novelties Platelets -Disorders of platelet function and numbers Platelets -Physiology Vascular wall biology and disorders heamostasis with an acoustic biosensor using shear dependent kinetics behavior: principle and limitations

Fig. 1 .

 1 Fig. 1. Photograph of the whole blood perfusion chamber with an installed QCM biosensor (a) and the experimental setup (b)

Fig. 3 .

 3 Fig. 3. Bar charts of the three defined metrics as a function of shear rates for two healthy donors: TFS (a); lag time (b); growth rate (c). Error bars show the absolute deviation from the mean value at the same shear rate the tests were made for the same donor on three different days.

Figure

  Figure4. (left) Height profile of platelet deposits across the biointerface for shear rates 500s -1 (blue curve) and 1500s -1 (orange curve) (average height for 500s -1 (blue) and 1500s -1 (orange) are shown with dotted lines). Insets: AFM images (height trace) of platelet deposits obtained at 500s -1 (on the left) and 1500s -1 (on the right) shear rates respectively. (right) Optical microscopy image of platelets deposits on collagen biointerface 500s - 1 (on the left) and 1500s -1 (on the right).

Figure 5 .

 5 Figure5. Platelet deposits average thickness versus deposits coverage. (left) Blue dots show the cases that are found within one standard deviation (dashed lines) of the linear regression (solid line). Orange dots correspond to the cases with deviation from linear regression larger than one standard deviation. Linear regression (solid line) and corresponding coefficient of determination R 2 are shown for the data points within one standard deviation (blue dots). (right) Schematic illustration of the deposits growth for the "balanced", "localized" and "spread" cases.


G Antoun

A Girard

T Tillocher

P Lefaucheux

J Faguet

K Maekawa

C Cardinaud

R Dussart
email: zremi.dussart@univ-orleans.fr

Quasi in situ XPS on a SiOxFy layer deposited on Silicon by a cryogenic process

A silicon oxyfluoride layer was deposited on a-Si samples using SiF4 / O2 plasma at different temperatures between -100°C and -40°C. In situ X-Ray Photoelectron Spectroscopy measurements were then performed in order to characterize the deposited layer. The sample was then brought back to room temperature and analyzed again. It has been shown that a temperature below -65°C is needed in order to significantly enhance the physisorption of SiFx species. Hence, in this condition, a F-rich oxyfluoride layer, stable at low temperature only, is physisorbed. Above this threshold temperature, the native silicon oxide layer is fluorinated and the proportion of O in the deposited layer is higher and remains stable even when the sample is brought back to room temperature.

Introduction

The formation of a silicon oxyfluoride layer has been widely studied for several applications. First, due to its low dielectric constant, the SiOxFy layer can be used as an interlayer dielectric in Back-End-Of Line applications and Very-Large-Scale Integration devices [1][START_REF] Geisler | AIP Conference Proceedings[END_REF][START_REF] Lee | [END_REF] . Its low refractive index makes it also interesting as optical layer [START_REF] Lee | [END_REF][4][5] . It is also used in plasma structuring [6][START_REF] Gaudig | 32nd European Photovoltaic Solar Energy Conference and Exhibition[END_REF][START_REF] Gao | [END_REF][9][10][11] . This layer is also well-known as the passivation layer used to perform anisotropic deep etch of Si in cryogenic standard and STiGer processes [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31] . As far as we know, the behavior of the SiOxFy layer at cryogenic temperatures has only been investigated for passivation purpose in etching processes, so the knowledge on this layer was based on these studies. It was shown that the SiOxFy passivation layer is either built from the SiFx sites remaining at the silicon surface or by the SiFx radicals species produced in the plasma from the dissociation of SiF4 etch by-products. These SiFx species react with oxygen radicals produced in the plasma to form the SiOxFy layer on the cold silicon surface. This layer grows efficiently at low temperature only, and is modified when the sample is brought back to room temperature 30 . Moreover, when the sample is in contact with air, the fluorine content of the SiOxFy decreases further and leads to a SiOx like layer 9,32 . In 29 , in situ X-Ray Photoelectron Spectroscopy (XPS) analyses on Si samples after exposure to SF6 / O2 plasma at -100°C were reported. The authors assumed that the deposited SiOxFy layer was mainly composed of SiOF3 molecules at the top surface. During the warming of the sample, oxygen and fluorine diffuse more easily at the layer surface. SiF4 molecules are then formed, and being very volatile, desorb immediately from the surface leading to the layer thickness decrease. In 32 , SiF4 / O2 plasma mixture has been produced and studied to evidence the importance of SiFx physisorption in the formation of the SiOxFy layer. It was also shown by Spectroscopic Ellipsometry (SE) measurements that, for the same plasma parameters, the thickness of the deposited layer increases when the sample temperature decreases. Once this layer is brought back to room temperature, a part of it desorbs. The refractive index of the latter layer is close to the one of SiO2 (1.45) when the deposition is performed above -50°C. However, for a deposition temperature below -50°C, the refractive index decreases down to 1.2 after venting, due to the desorption of physisorbed species, making the layer more porous. In order to have a better understanding of the mechanisms involved in the growth of the SiOxFy layer and its desorption, quasi in situ XPS measurements have been performed and are presented in this paper. The silicon oxyfluoride layer was deposited at three different temperatures between -100°C and -40°C using SiF4 / O2 plasma. The layer was analyzed without venting, a first time at the deposition temperature and a second time after warming up the sample.

Experimental

The experiments were conducted on the OPTIMIST platform (Opening of a Technical Platform for the Investigation of the Mechanisms of Interaction between plasma and Surface on a large Temperature range) composed of a home-made ICP reactor connected to a X-ray Photoelectron Spectroscope (XPS). A sketch of this platform is presented in 33 . Tests were performed on a 50 nm thick a-Si layer deposited by Chemical Vapor Deposition (CVD) on a 100 nm thick SiO2 layer, previously grown on Si substrates. Thus, the SiOxFy layer was deposited in the ICP reactor on a 10 x 10 mm² a Si coupon. Then, the a-Si coupon was loaded on a transfer sample rod whose temperature is adjustable using liquid nitrogen in combination with a heating resistance. The sample rod can navigate from the reactor chamber to the XPS chamber while keeping the sample under vacuum and at the desired temperature. Leakage tests were carried out before the experiments in order to ensure the absence of leaks and contaminations, and a plasma clean was performed after each experiment. XPS analysis is carried out with an Al Kα monochromatised x-ray source (SPECS XR 50M and Focus 500) operated at 400 W. The analyser (SPECS Phoibos 150HR) operates in fixed transmission and medium area mode. Survey scans are acquired at 30 eV pass energy (PE) with 0.5 eV steps, and high resolution narrow scans at 14 eV PE with 0.05 eV steps. No charge neutraliser was used during data acquisition. Post-acquisition charge correction was found necessary for the -100°C sample only. Considering the overall composition of the layer, this was carried out by aligning the F 1s principal component with that of the F-Si identified for the -65°C sample; the concomitant observation of CF2 species now at 291.2 eV brought a high confidence in the procedure. We used the software CasaXPS for the quantification and spectral analysis 34 . The a-Si sample was exposed to a 30 s long SiF4 / O2 plasma with 6.6 sccm SiF4 and 20 sccm O2 flow rates (25 % of SiF4 in the mixture). The pressure was set to 3.0 Pa and the ICP power was 200 W.

Results and Discussion

Figure 1. shows the XPS survey spectra of the a-Si surface before and after exposure to SiF4 / O2 plasma at -40°C, -65°C and -100°C. Figure 1. a) shows the reference wide scan before SiF4 / O2 exposure. Contribution of the Si 2s and Si 2p core levels is observed. Presence of oxygen (O 1s) is due to the native oxide on Si, presence of carbon (C 1s) comes from atmospheric contamination.

According to Figure 1. b) and c), the survey scans obtained after deposition at -40°C and -65°C are quite similar. The main change in comparison to the reference sample is the fluorine contribution (F 1s) and the disappearance of carbon. At -100°C, the fluorine peak becomes very intense, whereas the intensity of the silicon and oxygen peaks decreases sharply. Some physisorbed nitrogen (N 1s) is also detected. It may come from some residual N2 in the chamber dissociating during the plasma and that physisorb on the cold surface.

For a more accurate analysis, a narrow scan of each core level was performed. As the scans at -40°C and -65°C are quite similar, only -40°C narrow scans will be presented. Figure 2. shows the narrow scans of the F 1s, O 1s and Si 2p core levels after deposition at -40°C and after warm up. Figure 2. (a) shows F 1s core level. One peak is observed at 687.2 eV, corresponding to F-Si(O) bonds 9,35 .

Regarding the O 1s core level in Figure 2. (b), a peak is detected at 533.0 eV. Its slight shift, as compared to the reference sample (532.7 eV) indicates the presence of some O-SiF bonds from the deposited layer in addition to the O-Si from the native oxide. Figure 2. (c) shows the Si 2p spectra. Two main structures are observed: at 99.2 eV and 103.9 eV, corresponding respectively to the a-Si material and Si4 + oxidation state. On the raw sample, the latter is detected at 103.4 eV and comes from the native oxide layer. The shift observed here shows that part of the native oxide has been fluorinated. It also confirms the existence of SiOxFy species from the deposited silicon oxyfluoride layer due to the presence of fluorine in the layer 9,16,22,29,35,36 . After warming up, the peaks remain unchanged.

The thickness of the deposited layer after warming up the sample to 20°C was estimated using two different means of calculation. First the QUASES-Tougaard software 37 was used to analyse the evolution of the inelastic background of the Si2s-Si2p spectral region from the reference and the plasma treated samples. Second, the attenuation of the Si 2p (a-Si) component resulting from the presence of the deposited layer was used. Photoelectron attenuation length in the deposited layer was considered similar to that in amorphous SiO2 and taken as 37.5 Å for Si 2p photoelectrons 38 . Both means of estimation give converging values of 7 ± 3 Å for the SiOF deposited layer. Confidence in this estimation can be evaluated from the calculation of a 17 ± 1 Å thick native oxide on a-Si using the same procedures. Figure 3. presents the narrow scans of each core levels after deposition at -100°C and after warm up. The F 1s scan in Figure 3. (a) after deposition shows two peaks. The first one is very intense and is detected at 685.5 eV. It is related to F-Si bonds 39 and its intensity strongly decreases after warming up. The second peak is located at 688.5 eV corresponds to F-C bonds 39 due to the physisorption of CFx species etched from the reactor wall. In agreement, the C 1s spectrum (not shown) presents evidence for CFx (x = 1 to 3) species. Two others peaks are also observed after warm up. The first peak at 687.2 eV corresponds to F-SiO bonds, as at -40°C. The second peak at 683.9 eV are identified as F-Cr bonds, such contamination comes from the physisorption of CrF2 species due to the etching of the Ni/NiCr thermocouple measuring the sample temperature. In fact the sample at low temperature also shows some Cr contamination, but the signal is much weaker; as the latter increases upon warm-up we emphasize that the (nonvolatile) CrFx species that were dispersed within the SiOF layer are now concentrated at the surface.

Figure 3 (c) presents the Si 2p spectra. After deposition, only the peak at 103.6 eV is detected. The contribution from the a-Si material (99.2 eV) is not observed. This indicates that the sample surface has been totally covered with a SiOxFy layer thicker than 10 nm (typical XPS analysis depth assuming an attenuation length of ~3 nm for Si 2p photoelectrons). At this temperature, the desorption rate of physisorbed SiFx species is lower than at higher temperatures, which leads to an accumulation of SiFx species at the surface 40 . After warm up, the substrate contribution can be detected again due to the desorption of fluorinated species. Figure 3 (b) shows the O 1s spectra. The low temperature SiOxFy layer gives a contribution at 534.1 eV. Two other oxygen peaks are detected at 541.2 eV and 545.2 eV. They could not be clearly identified. Nevertheless, they may come from some residual species trapped in the physisorbed layer. A similar double contribution is observed on the N 1s region (not shown). After warming up, these species disappear and only one peak, located at 533.0 eV is detected. It is the same as at -40°C and corresponds to O-SiF bonds from the SiOxFy layer. Such evolution shows that the modification of the surface after the deposition at -100°C and after warm up is due to the desorption of SiFx species. Moreover, this desorption indicates that the deposited layer at -100°C is a mix of a physisorbed layer, only stable at low temperature, and a chemisorbed layer that remains even at room temperature. Figure 4. shows F 1s and O 1s spectra after deposition at the three different temperatures. It allows to evidence the intermediate behavior at -65°C. Indeed, in Figure 4. (a) a small tail is already detected at 685.5 eV related to F-Si bonds. The intensity of this peak strongly increases when the deposition is performed at -100°C. Moreover, Figure 4. (b) shows that the intensity of O-SiF peak at 533.0 eV slightly drops from about 24000 cps at -40°C to about 20300 cps at -65°C. Moreover, at -100°C, that at 534.1 eV is only about 6100 cps.

Using the acquired scans, the relative surface composition has been quantified after deposition and after heating the sample back to 20°C. The obtained results are presented below in Figure 5.

The experiments performed at -40°C and -65°C lead to very similar results that do not significantly change after heating. The composition averaged on the analyzed depth is about 59% of Si, 23% of O and 17% of F. At -100°C, it was shown in Figure 3. (c) that the surface is totally covered by a SiOxFy layer. According to Figure 5., this layer, including the C, O, N trapped species is composed of 11% of Si, 18% of O, 52 % of F, 4% of C and 15% of N. Skipping the trapped physisorbed species, one obtains 74% F, 8% O and 18% Si. After heating, a significant part of this layer desorbs, which is in agreement with the SE observations reported in 23,29,32 . The F content decreases to 19.5%, which is close to the amount detected at the surface at -40°C and -65°C. These XPS measurements have first evidenced the existence of a temperature threshold. Indeed, at -65°C and above, the native oxide is fluorinated and a layer with a low amount of fluorine can be deposited. This SiOxFy layer is stable in temperature, as it does not change after bringing the sample back to 20°C. According to 17,32 , the material of this layer is close to SiO2. Being rich in oxygen, it is consequently more resistant to SF6 plasma.

Below -65°C and more specifically at a temperature close to -100°C, the surface residence time of SiFx species is significantly increased 40 . The deposited silicon oxyfluoride layer is consequently a mix of chemisorbed molecules and physisorbed fluorine-based species. The physisorbed part of this SiOxFy layer is only stable at low temperature, as a significant part of SiFx is converted in SiF4 molecules which immediately desorb from the surface once formed during the sample warming up 23 . The thickness decrease observed in 32 is hence due to the depletion of SiFx species from the surface layer.

Conclusions

This study presents the effect of temperature on the deposition of a SiOxFy layer on a-Si. It shows that the concentration of fluorine atoms in the deposited SiOxFy layer increases when decreasing the temperature below -65°C. Then, a decrease in the Si-O peak intensity and the formation of a small peak related to Si-F bonds can be clearly observed by in situ XPS analysis. At -100°C, the SiOxFy layer has a stoichiometric composition close to a SiOF3 as reported in 29 with a SF6 / O2 plasma in overpassivating regime. This study also confirms that the layer is significantly modified during the warming of the sample with a large depletion of SiFx species from the layer. Thus, once warmed up, there is no significant difference in the composition of the deposited SiOxFy layer regarding the deposition temperature. However, in a previous study 32 although performed under different conditions, the authors showed that when depositing a SiOxFy layer at low temperature (close to -100°C) and then warming it up, the created layer is porous. This is due to the desorption of SiFx species, and the layer will consequently have a different optical index than if it was deposited at room temperature. Finally, this study showed that at -40°C and -65°C, a chemisorbed SiOxFy layer can be formed by first fluorinating the native oxide, whereas at -100°C, the physisorption of SiFx species is enhanced and thus the deposited layer is a mix of chemisorbed and physisorbed species.

Thus, this study offers further insights into the mechanisms involved in processes using SiF4 / O2 plasma, which is used in the so-called STiGer cryogenic etching processes 41 . However, it also offers the possibility to tune the SiOxFy chemical and optical layer properties as desired, by adjusting the temperature of deposition.

FiguresFigure 1 .

 1 Figures

Figure 2 .Figure 3 .Figure 4 .Figure 5 .

 2345 Figure 1. XPS survey scans on a-Si coupon before and after SiF4 / O2 plasma exposure at three different temperatures (Experimental conditions: SiF4 / O2 plasma: 25%, 30 s, 3 Pa, 200 W)

Acknowledgments

The authors gratefully thank Shigeru Tahara from TEL for all the helpful discussions and also Kumiko Yamazaki and Nagisa Sato from TEL for their support to the project. This work was supported by CERTeM 5.0 platform, which provides most of the equipment; the CNRS-Réseau des Plasmas Froids is also acknowledged for giving access to the Optimist platform. This work was partially financed by the French 'Agence Nationale de la Recherche' through Contract No ANR-20-CE24-0014-02 under the name PSICRYO project.


Nima Mehdi

Vincent Thomas

Serena Ivaldi

Francis Colas

Simultaneous Pose and Posture Estimation with a Two-stage Particle Filter for Visuo-inertial Fusion

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Simultaneous Pose and Posture Estimation with a Two-stage Particle Filter for Visuo-inertial Fusion

Nima Mehdi 1 , Vincent Thomas 1 , Serena Ivaldi 1 , Francis Colas 1

Abstract-We address the problem of human pose and posture estimation without any high precision marker-based motion capture systems, by merging inertial data from wearable sensors and a single RGB camera. Our proposition is based on a biomechanical model of the human body and two coupled filters: the first filter takes advantage of the accurate posture observations provided by wearable sensors and a factorization of joints to estimate the human posture with a reduced number of particles while the second filter uses RGB camera observations to estimate the drift of the wearable sensor so as to estimate the global state (pose and posture). In order to combine those filters, the estimated human posture distribution of the first filter is used as a proposal distribution for the second fusion filter so as to focus on particles with an already highlikelihood posture and to improve the efficiency of the pose estimation. Results showed this approach can perform online estimation of the human posture and the human pose (through the drift of the wearable sensor) and performed better than techniques relying only on inertial sensor or on direct pose estimation.

I. INTRODUCTION

Human posture and pose estimation is a particular focus in the computer vision and robotics communities. Much progress has been made in tracking the human posture from visual scenes [START_REF] Cao | OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields[END_REF]. However, the majority of these methods do not seek to deliver a kinematics-consistent estimation of the human posture where the plausible biomechanical properties of the human body are considered from the body segments to the joint angles. This is particularly critical for collaborative robotics where precise estimation and of the human's body posture is necessary for the robot to plan accurate assistance and estimate elements allowing it to access the human movement such as dynamics or ergonomics [START_REF] Gomes | Multi-Objective Trajectory Optimization to Improve Ergonomics in Human Motion[END_REF]. Wearable inertial measurement units (IMUs) have partially solved this problem as they enable a rather precise posture tracking even in industrial contexts [START_REF] Malaisé | Activity Recognition With Multiple Wearable Sensors for Industrial Applications[END_REF], but they estimate poorly the 3D pose, i.e, the location of the human in the 3D space as they are subject to drift and often asymmetric and larges errors due to error in odometry caused for instance by irregular terrains, which limit their usability in case of mobile robots operating in close vicinity of humans especially when interacting actively with human agents. Motivated by these observations, we address here the case where the mobile This work was partly supported by the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 731540 (project AnDy), the French Research Agency (ANR) under Grant No. ANR-18-CE33-0001 (project Flying Coworker), the European FEDER in the context of the CPER Sciarat and the Creativ'Lab.

1 Université de Lorraine, CNRS, Inria, LORIA, F-54000 Nancy, France. firstname.lastname@inria.fr robots need both postural and locational information about the human agent.

To do so, we propose to leverage both wearable sensors and cameras. Our main contribution in this article consists in the definition and evaluation of two coupled particle filters. A first factorized filter constructs a posterior distribution over the posture given inertial measurements, which is used as a proposal distribution in a second filter which merges inertial measurements and camera images to estimate the inertial drift as well as the posture. The contribution proposed in this paper relies on three key ideas: (1) estimating the drift of the inertial sensor (instead of the actual pose of the human) by using camera observations; (2) using the posture estimate based on the inertial observations as a proposal for the fusion estimation filter; (3), and, to reduce the amount of required particles in the posture estimation process, factorize the posture space by taking advantage of independencies between joints belonging to different kinematic chains. It allows estimating robustly the human posture in a biomechanical plausible state as well as locate the human agent in the 3D space in the context of human-robot interaction. In section II, we present related works on pose and posture estimation. Then, in section III, we formally describe the addressed problem, present an overview of our contribution and then detail the assumptions, the models and the equations used for each part of our contribution. The conducted experiments and the obtained results are described in section IV before a conclusion in section V.

II. RELATED WORKS

There are several directions that have been explored in the literature to estimate the 3D pose and posture of a human. They can be sorted based on the kind of sensor they use.

A. IMU-based

With their current size, inertial and magnetic sensors can be easily integrated into wearable sensors with minimal footprint at a relatively low cost. Both Yun et al. [START_REF] Yun | Design, Implementation, and Experimental Results of a Quaternion-Based Kalman Filter for Human Body Motion Tracking[END_REF] and Roetenberg et al. [START_REF] Roetenberg | Xsens MVN: Full 6DOF Human Motion Tracking Using Miniature Inertial Sensors[END_REF] track the human body using a Kalman filter. A particle filter using directional distributions is proposed to track the gait of a human operator by To et al. [START_REF] To | Quaternionic Attitude Estimation for Robotic and Human Motion Tracking Using Sequential Monte Carlo Methods With von Mises-Fisher and Nonuniform Densities Simulations[END_REF].

As small as IMUs can be, they are still intrusive. Hence, solutions were proposed to learn to reconstruct the 3D human pose from a sparse IMU configuration. Marcard et al. [START_REF] Von Marcard | Sparse inertial poser: Automatic 3d human pose estimation from sparse imus[END_REF] suggest a join optimization framework and make use of an anthropomorphic constraint for a realistic human model. Huang et al. [START_REF] Huang | Deep inertial poser: learning to reconstruct human pose from sparse inertial measurements in real time[END_REF] use a Recurrent Neural Network (RNN)based method to learn human pose with IMU data. Inertial-based methods are especially adapted to retrieve a precise 3D posture as well as the body's dynamics, but they suffer from drift on the absolute pose since it is not observable.

B. Vision-based

Marker-less vision based systems have received major attention as cameras allows low setup and cheaper costs than other motion capture systems. Particle filters were proven to be an effective means for visual tracking and many works were aiming to adapt and perfect these methods to estimate the articulated human body from images: Deutscher et al. [START_REF] Deutscher | Automatic partitioning of high dimensional search spaces associated with articulated body motion capture[END_REF] use an annealed particle filter associated with a hierarchical search framework over images to estimate the human pose. Sedai et al. [START_REF] Sedai | A Gaussian Process Guided Particle Filter for Tracking 3D Human Pose in Video[END_REF] suggest using a Gaussian-process-guided particle filter to estimate a 3D model from a video sequence.

More recently, with the popularity and efficiency of deep learning due to the increase of computational power and dataset sizes, many approaches aim to solve 3D pose estimation with artificial neural networks applied on images. Chen et al. [START_REF] Chen | 3D Human Pose Estimation = 2D Pose Estimation + Matching[END_REF] propose a Convolutional Neural Network to generate a 2D pose and then match the generated 2D pose to a 3D pose using a non-parametric nearest neighbor model. Pavllo et al. [START_REF] Pavllo | 3d human pose estimation in video with temporal convolutions and semi-supervised training[END_REF] introduce a temporal convolution model for neural networks to generate 3D poses from 2D poses. Moon et al. [START_REF] Moon | Camera Distance-Aware Top-Down Approach for 3D Multi-Person Pose Estimation From a Single RGB Image[END_REF] estimate the 3D pose using a single RGB image by recovering the camera-centered coordinates using a dedicated neural network. All these methods can be highly effective but often need important amount of data and suffer from occlusion in clustered environments. Furthermore, their precision is often lower as it is easier for inertial units to follow dynamic movement.

C. Hybrid approaches

IMU-based techniques are subject to drift while visionbased ones can suffer from occlusions. Therefore, fusing image data with inertial data allows to obtain a more robust estimation of the human location and kinematics. Trumble et al. [START_REF] Trumble | Total Capture: 3D Human Pose Estimation Fusing Video and Inertial Sensors[END_REF] separately estimate 3D poses from IMUs and images from multiple views cameras before merging them using several neural networks to obtain the estimated pose. Marcard et al. [START_REF] Marcard | Recovering Accurate 3D Human Pose in the Wild Using IMUs and a Moving Camera[END_REF] assign 2D pose from images to 3D posture estimated from IMUs through optimization before feeding back camera poses to estimate the 3D posture and pose of tracked humans.

We draw inspiration from these approaches and propose to fuse inertial and camera measurements, but we do so using Bayesian filtering. It allows us to avoid the large amount of data needed in machine-learning-based methods and enables us to work with a biomechanical constrained model which can be personalized to the person tracked.

III. METHODS

A. Problem Statement

We want to estimate the full body pose (position, orientation, and posture) of a human based on inertial and camera measurements. Due to uncertainties, we formulate this problem as a Bayesian filtering problem. That is, we aim to compute the probability distribution over the full pose x k at time step k given the series of observations z 1:k from the beginning up to time step k:

p(x k | z 1:k ). (1)
The observations z k are composed of the inertial motion capture measurements z x,k , which are the position and orientation of all body segments with respect to an arbitrary origin [START_REF] Roetenberg | Xsens MVN: Full 6DOF Human Motion Tracking Using Miniature Inertial Sensors[END_REF], and of the camera measurements z c,k , which, preprocessed by OpenPose [START_REF] Cao | OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields[END_REF], are the 2D coordinates on the image of the main joints.

The full body pose x k is composed of the pelvis pose x P k , as a position and quaternion for the orientation, and of the posture θ k . Our long-term aim is to use the posture for activity recognition [START_REF] Malaisé | Activity Recognition for Ergonomics Assessment of Industrial Tasks with Automatic Feature Selection[END_REF] as such, we need the posture to be complete yet compact and expressed as joint angles. The inertial motion capture system we use can output joint angles but with 66 degrees-of-freedom and without biomechanical constraints. We chose to represent our posture using a Digital Human Model (DHM) composed of 13 segments linked by 20 revolute joints (see Figure 1). This choice is a trade-off between a high fidelity biomechanical model and the size of the state space. We refer to the review by Vianello et al. [START_REF] Vianello | Human-Humanoid Interaction and Cooperation: a Review[END_REF] on the importance of biomechanical models in context of Human-Robot collaboration. The mismatch between the observation DHM and our state DHM requires a form of retargeting, which will be taken care of by the estimation algorithm.

In our case, we assume that we know the pose of the camera. This can be obtained, for instance, through visual odometry or visual SLAM algorithms [START_REF] Campos | ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial, and Multimap SLAM[END_REF].

B. Filter Overview

As our problem is neither linear nor Gaussian, we use particle filtering (see tutorial from Arulampalam et al. [START_REF] Arulampalam | A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking[END_REF]) for our inference of Equation 1. However, the large dimension of the state space (26) would require a huge number of particles.

This can be solved traditionally in two manners. The first one relies on leveraging the structure of the state space by ways of conditional or marginal independencies (as, for instance Djuric et al. [START_REF] Djuric | Multiple Particle Filtering[END_REF]). However, all our observations (inertial or visual) depend on the pelvis pose, which prevents defining a partition of the state space. Our first key idea is to preprocess the inertial observations to decouple the postural state between each body part (arms, legs, and trunk). Concretely, it amounts to computing the relative position z r

x,i,k of each segment (e.g., the left hand) with respect to the root of the body part (e.g., the left shoulder). This is feasible with the inertial measurements since they contain the 3D position and orientation of each segment with respect to an arbitrary frame but this is not the case with the visual observation.

The second solution to limit the number of particles need is to optimize their location thanks to an adequate proposal distribution [START_REF] Doucet | On sequential Monte Carlo sampling methods for Bayesian filtering[END_REF]. Our second key idea is to use the output of the individual segment filters as a proposal for a second filter, which uses the camera observations. Therefore, we design our filter in two parts:

1) a first one, which we call Multiple Posture Filter (MPF) using only preprocessed inertial measurements which splits the posture estimation into five body parts (the left and right arms and legs and the trunk with the head), 2) and a second part, which we call Fusion Filter with the full state space but using a proposal distribution from the MPF. This is further justified by the observation that the inertial measurements are quite accurate on the posture but suffer from drift whereas the camera observation is less precise on the posture but exempt from drift. The role of the fusion filter is therefore to compensate for the drift ξ k of the reference frame of the inertial measurements as well as improve the posture estimate with the camera.

We actually propose two variants of the second filter: Pose-FF where the state is composed of the pelvis pose x P k and the posture θ k , and the Drift-FF where the state is composed of the drift ξ k and the posture θ k . In the latter filter, the pelvis pose can be recovered by composing the estimated drift with the inertial measurement of the pelvis z P

x,k . The overall structure of our filter can thus be summarized as in Figure 2. The remaining of this section details each of those parts.

C. Multiple Posture Filter

As explained above, the first part of the filter estimates the posture by splitting it into five sub-filters, each for a body part: left and right arms and legs and the trunk including the head. Each of those sub-filters is a particle filter estimating the joint angles θ j,k , j ∈ 1 . . 5 based on the inertial observations preprocessed into the relative position of each segment with respect to the root of the part.

The transition model p(θ j,k+1 | θ j,k ) is chosen to be a multivariate normal distribution centered on the previous joint angles. The next particle is then computed as follows:

θ j,k = θ j,k-1 + w j,k , w j,k ∼ N (0, Σ θ,j ) ∀j ∈ 1 . . 5.
We relate the state θ j,k to the relative observation z r x,j,k through forward kinematics calculations. Therefore, the associated observation model is: zx,j,k = h f k,j (θ j,k ), ∀j ∈ 1 . . 5 h f k,j being the forward kinematics transform associated to the subtree j.

The associated likelihood for weight update p(z r x,j,k | θ j,k ) is defined as a multivariate normal distribution of mean h f k,j (θ j,k ) and covariance Σ x,j,k over z r x,j,k . The weight update for the particle θ i j,k is defined as follows:

w i k+1 ∝ w i k • p(z r x,j,k |θ i j,k ) ∀j ∈ 1 . . 5.
This weight update is then followed by a resampling step allowing to minimize high variance in our particle population and propagate more often particle with more important weights [START_REF] Li | Resampling Methods for Particle Filtering: Classification, implementation, and strategies[END_REF].

Then, from our hypothesis of independence between each body part, the belief over the whole posture is simply:

p(θ k |z r x,1:k ) = 5 j p(θ j,k |z r x,j,1:k ) (2)

D. Pose Fusion Filter

The Pose Fusion Filter (Pose-FF) is a particle filter estimating directly the posterior distribution on the pose of the pelvis x P k and the joint angles θ k through a weighted set of N particles:

p(x P k , θ k | z 1:k ) ≈ N i w i k δ x P k -x P,i k , θ k -θ i k , ( 3
)
where δ is the Dirac delta function.

Rather than using the transition model to sample new particles x P,i k , θ i k as for the MPF, we rely on a proposal distribution π(x P k , θ k ). The aim of a proposal distribution is to generate particles at the best locations to represent the posterior p(x P k , θ k | z 1:k ). In particular, we can leverage our estimation from the MPF (from Equation 2) to have a good estimate on p(θ k | z 1:k ). For the pose, we use a multivariate normal transition function:

p(x P k | x P k-1 ) = N x P k-1 , Σ p .
We can thus build our proposal as:

π(x P k , θ k ) = p(x P k | z 1:k )p(θ k | z r x,1:k+1 ) = p(θ k | z r x,1:k+1 ) x P k-1 p(x P k | x P k-1 )p(x P k-1 | z 1:k ) = p(θ k | z r x,1:k+1 ) N i p(x P k | x P,i k-1 )w i k-1 δ x P k-1 -x P,i k-1 (4)
where Equation 4is obtained by substituting the previous posterior according to Equation 3 and marginalizing on the pose.

Sampling from this proposal amounts to:

• sampling from the output of the MPF according to Equation 2 for the joint angles, • sampling from p(x P k | x P,i k-1 ) for the pelvis pose. Sampling the joint angles can be further simplified by reusing the very same particle locations as the individual filters in the MPF. Now, the weights of the particles need to be computed according to this proposal. More precisely, we have:

w i k := p(x P k , θ k | z 1:k+1 ) π(x P k , θ k ) . (5)
If we substitute Equation 4into Equation 5, we can simplify the expression of the weights into:

w i k ∝ w i k-1 p(z c,k | x P,i k , θ i k ), (6)
where p(z c,k | x P k , θ k ) is defined as a multivariate normal distribution centered on the 2D projection of the 3D positions of each joint as computed by the forward kinematics and with a covariance Σ C . With these weights, it is then possible to compute an estimate as the mode or the average of the weighted samples and to sample new particles for the next time step.

E. Drift Fusion Filter

The pose fusion filter can estimate the pose but relies in particular on a uninformed Gaussian transition model for the pelvis. Without odometry, it needs to be wide enough to account for the possible motions of the human and the variance of the particles might be relatively high.

However, the inertial measurements do provide an estimate of the pelvis position up to its global drift. An alternative expression of fusion filter can thus be obtained by estimating the drift ξ k instead of the pelvis pose (Drift-FF).

The expressions are the same as for the pose fusion filter, except that the covariance of the transition model Σ d can be assumed to be significantly smaller than Σ p . A second, minor, difference is that the observation function also now depends on the inertial measurement of the pelvis z P

x,k . Finally, the drift fusion filter is described in 1.

Algorithm 1 Drift Fusion Filter

Require:

{x i k-1 , w i k-1 } N i=1 ,z x,k ,z c,k with x i k-1 = [θ i k-1 , ξ i k-1 ] p(θ k |z x,k ) = MPF ({θ j,k } j∈1..5 | z x,k ) for i ∈ 1 . . N do θ i k ∼ p(θ k | z x,k ) ξ i k ∼ p(ξ k | ξ i k-1 ) w i k ← w i k p(z c,k | ξ i k , θ i k , z P x,k ) Resample with replacement {x i k } using {w i k } end for return {x i k , w i k } N i=1

IV. EXPERIMENTS AND RESULTS

A. Implementation and Experimental Setup

Setup: Experiments were conducted with 3 healthy adults (3 males, 0 female, aged 24-26), recruited by word of mouth in the University. Each participant had its height, sole length as well as different anthropometric measurements taken in order to parametrize the DHM corresponding to their physiology.

The experiments were carried out inside an arena of approximately 4×4 meters, whose area is completely tracked by an OptiTrack Motion Capture system with 8 cameras. Each participant was equipped with the Xsens MVN suit with 17 embedded IMUs of the system. For ground truth, reflective markers from the OptiTrack Motion Capture system were placed on the motion tracking suit in the locations of pelvis, head, shoulder, elbows and wrists. A calibrated Intel RealSense Camera (RGB-D), marked as well with reflecting markers in order to retrieve its pose, was placed in the arena.

Tasks: The participants were instructed to perform a 5 to 10 minutes walk inside the arena, following rectangle shaped path. They were also instructed various hand motions both while standing still and walking.

Before each experimentation, the Xsens system was calibrated by walking forward and then back before staying put in a neutral pose, as instructed by the Xsens software. Each calibration was to be validated good enough to proceed by the software.

Implementation: The algorithm was implemented in Python 3.8 and run on a Ubuntu 20.04 with a 8-core Intel i7. The kinematic human model was encoded using the Unified Robot Description (URDF) which the angle joints defined in the files will constitute the posture part of the state space and the pose of the root frame will be used to estimate the drift. Forward kinematics calculations were performed using the Pinocchio library [START_REF] Carpentier | The Pinocchio C++ library -A fast and flexible implementation of rigid body dynamics algorithms and their analytical derivatives[END_REF]. The 2D pose estimation by OpenPose was ran on camera data from the experimentation, on a Ubuntu 20.04 machine equipped with a NVIDIA 2070 RTX GPU. The captured data were downsampled at 5Hz and run offline. But the current implementation has the capability to run at 10Hz online for 1200 particles totals (200 for the fusion filter and 1000 in total for the MPF). One of the limiting elements is the 2D pose estimation, which can run at maximum around 10fps on our system.

B. Results

To confirm that our method can estimate the pose and posture while negating the drift, we carried different tracking scenarios. These scenarios compare 3 main methods: (1) our method, based on the sensor fusion and the proposal over the posture distribution, using the coupled Multiple Posture Filter (MPF) and estimating the drift, denoted Drift-FF, (2) a similar method with the same exact architecture, but without using the inertial odometry and tracking directly the pose instead of the drift, denoted Pose-FF, and, (3) the inertial based method corresponding to the raw Xsens observations and denoted XS-IMU. It must be noted that the Xsens system has already been designed to handle drift issues and to provide an accurate estimate of its user pose, therefore, the following experiments have the objective to assess if our system can improve the already efficient Xsens solution, which constitutes our baseline.

Drift Estimation and pose correction

As our system uses inertial based observations, we wanted to confront the method's resilience to drift. We ran both the Pose-FF and the Drift-FF on data gathered during an experiment where the human agent walked forward, circling in the dedicated space. We then computed the root squared error in position for all three previously enumerated methods. The results corresponding to approximately 30 minutes total of walking were compiled within Figure 3 Drift-FF XS-IMU Pose-FF From these results, Drift-FF provides an overall better estimate than Pose-FF and XS-IMU. On the right of Figure 3, we can notice that both fusion filters (Drift-FF and Pose-FF) provide better long-term results than XS-IMU with best results when the drift is estimated (Drift-FF). It can also be noticed that the error distribution of XS-IMU is very acceptable the 2 first cycles but this error and its interquartile range increase with time. The significant (apparently linearly) increase of the median value can be explained from the inertial drift. The larger interquartile range of the XS-IMU error distribution can be partially explained by the high sensitivity of the observed error to the Xsens calibration. Nevertheless, our method based on the drift estimate (Drift-FF) manages to generate better results with the exact same data, being more robust to the calibration step.The experiments, as illustrated by Figure 4, showed the drift is large and asymmetrical, expanding more significantly in a particular direction. Our method (Drift-FF) computes a good estimate of the pose through the drift, while not considering yet that assymetry. Further investigations will be considered in order to improve our drift estimate. One of the typical tasks in collaborative robotics is the hand-over of an object. It requires a rather accurate estimate of the position of the hand. The position of the head can also be of interest to avoid particularly dangerous collisions or even to estimate the human focus of attention. Both require accurate estimates of the global pose of the pelvis and of the position of the hand or head with respect to the pelvis. In absence of ground-truth on the joint angles, checking the accuracy of those positions is also an indirect means to evaluate the quality of the posture estimate.

Figure 5 compares the errors on the position of the hand for the three methods. On the left, we can see that the error is not significantly different for both our fusion filters, which is expected since they both use the same proposal from the MPF corrected with the same observation. We can also see that this error on the relative position is higher for our filters than for the raw Xsens observation. This is due to additional variance by the MPF in the retargeting process from the full Xsens model to our more biomechanical 20-dof DHM. The camera observation reduces a bit the variance but not enough to reach the same accuracy as XS-IMU.

On the right of Figure 5, are compared the errors on the absolute position of the hand. Those are the most meaningful errors and, as expected from the results on the drift, we can see that Drift-FF presents a significantly lower error than XS-IMU.

V. CONCLUSION

We present an approach to estimate the full pose of a human, including their pelvis position, pelvis orientation, and joint angle. This approach is based on two first ideas:

Fig. 1 :

 1 Fig. 1: Digital Human Model used to represent the posture.

Fig. 2 :

 2 Fig. 2: Method Overview of the Drift-FF. T R transforms inertial poses into relative position and Π computes the join distribution for the proposal density. a IMU and ω IMU are respectively the raw accelerations and angular rates from the IMU.

Fig. 3 :

 3 Fig.3: Comparison in Positioning Error between 3 systems: a hybrid system fusing information between camera and imu (Drift-FF), a system based on vision alone (Pose-FF) and a pure inertial method (XS-IMU)

Fig. 4 :

 4 Fig. 4: The drift is visible from the trajectory estimated from XS-IMU (inertial odometry)

ACKNOWLEDGMENT

The authors wish to thank Pauline Maurice for fruitful discussions on the digital human model.

decoupling inertial observation to split the filtering, and using the posture thus estimated as a proposal distribution for the fusion particle filter. In addition, a third key idea consists in estimating the drift instead of the pose to lower the variance induced by the motion model. The resulting algorithm, Drift-FF, is shown to be effective to estimate a drift-free pose of the pelvis and of the hand. This was demonstrated on a collection of different trajectories taken with an Xsens inertial motion capture suit and a RGB camera. Due to its structure, it is also robust to temporary occlusions such as when the human goes out of the field of view of the camera.

We plan to use this system to estimate the activity of the human in a collaborative robotics scenario. A remaining challenge would be to reduce the number of inertial sensors required by our method, so as to lower costs and ease deployment.


Lionel Savignan
email: lionel.savignan@agro-bordeaux.fr

Guillaume Bonneau

Stéphanie Jalabert

Alexandre Lee

Philippe Chéry
email: philippe.chery@agro-bordeaux.fr

Copper contamination in vineyard soils of Bordeaux: spatial risk assessment for the replanting of vines and crops

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

soil horizons collected

• Moderate or high risk for new plantation is estimated for 50% of Bordeaux vineyard soils (Cu-EDTA concentration superior to 30 mg/kg).

• Highest Cu contamination is found in the oldest and most prestigious appellations (Médoc, Saint-Emilion) which early used Cu as a fungicide.

• Results must be interpreted cautiously as they have limitations such as modelling errors, old data (in relation to Cu accumulation) and spatial missing data that may underestimate Cu-EDTA concentrations in vineyard soils.

RISK: inhibition of growth and low yield for new crops or vine replanting

  Copper (Cu) is widely and historically used in viticulture as a fungicide against mildew. Cu has a strong affinity for soil organic matter and greatly accumulates in topsoil horizons of vineyards 1 . Excess of Cu in soils negatively affects soil organisms and young plants, consequently reducing soil fertility and productivity 2,3 . The Bordeaux vineyards have the largest vineyard surfaces (26%) within French protected designation of origin and a great proportion of French wine production (around 5 million hectolitres per year). The Bordeaux vineyards are also under heavy fungal pressure due to local climate. Considering the local context of vineyard surfaces decreasing (vine uprooting) and the possible new crop plantation, the issue of Cu potential toxicity rises. Therefore, our aims are to evaluate the Cu contamination in vineyard soils of Bordeaux, then to produce a risk assessment map for new vine or crop plantation.Spatial distribution of Cu contamination in vineyard soils and risk assessmentDatabase of Cu contamination in vineyard soils

  4496 soil horizons over 2424 study sites (soil pits), including 1757 georeferenced sites • Data collected from various soil studies within the Gironde area (1984 -2020) • Analysis of Cu : total, EDTA, exchangeable (ammonium acetate)

	• Ponderation of Cu EDTA
	according to soil horizon
	sequence
	• 2424 samples with Cu
	EDTA 0-50cm including
	1757 samples with
	georeferencing

•

Brun, L. A., Maillet, J., Richarte, J., Herrmann, P. & Remy, J. C. Relationships between extractable copper, soil properties and copper uptake by wild plants in vineyard soils. Environ. Pollut. 102, 151-161 (1998).

Michaud, A. M., Bravin, M. N., Galleguillos, M. & Hinsinger, P. Copper uptake and phytotoxicity as assessed in situ for durum wheat (Triticum turgidum durum L.) cultivated in Cu-contaminated, former vineyard soils. Plant Soil 298, 99-111 (2007).

Mackie, K. A., Müller, T., Zikeli, S. & Kandeler, E. Long-term copper application in an organic vineyard modifies spatial distribution of soil micro-organisms. Soil Biol.Biochem. 65, 245-253 (2013)

. 4. El Hadri, H. et al. Assessment of diffuse contamination of agricultural soil by copper in Aquitaine region by using French national databases. Sci. Total Environ. 441, 239-247 (

2012). 5. Baize, D., Saby, N. & Walter, C. Le cuivre extrait à l'EDTA dans les sols de France. Etude Gest. Sols 13, 259-

268 (2006). 6. Darricau, L. Impact du cuivre et de la typologie du sol sur la qualité biologique des sols viticoles du Nord de l'Italie: étude d'indicateurs de la qualité biologique des sols. (Bordeaux Sciences Agro, 2017).

Acknowledgements :

This work received no specific funding. The Fédération Régionale d'Agriculture Biologique de Nouvelle-Aquitaine is thanked for the funding of G.Bonneau internship. We thank all the providers of soil studies and data.