Celine Loscos
email: c.loscos@cs.ucl.ac.uk

David Marchal

Alexandre Meyer
email: a.meyer@cs.ucl.ac.uk

Intuitive Crowd Behaviour in Dense Urban Environments using Local Laws

In games, entertainment, medical and architectural applications, the creation of populated virtual city environments has recently become widespread. In this paper we want to provide a technique that allows the simulation of up to 10,000 pedestrians walking in real-time. Simulation for such environments is difficult as a trade off needs to be found between realism and real-time simulation. This paper presents a pedestrian crowd simulation method aiming at improving the local and global reactions of the pedestrians. The method uses a subdivision of space into a 2D grid for pedestrian-to-pedestrian collision avoidance, while assigning goals to pedestrians to make their trajectories smoother and coherent. Goals are computed automatically and connected into a graph that reflects the structure of the city and triggers a spatial repartition of the density of pedestrians. In order to create realistic reactions when areas become crowded, local directions are stored and updated in realtime, allowing the apparition of pedestrians streams. Combining the different methods contributes to a more realistic model, while keeping a real-time frame rate for up to 10,000 simulated pedestrians.

Introduction

Pedestrian traffic may be quite congested in large cities. The design of most public facilities could often enable the passage of hundreds of thousands pedestrians per day. Railway stations, shopping centres or tourist places attract high density crowds. In order to help in the design or to demonstrate such places to a public, it is essential to be able to simulate the movement of a pedestrian crowd in such environments. Other applications such as games and entertainment should also benefit from such techniques.

Our primary goal is to provide a simulation that runs in real-time with no pre-computation. This simulation is part of a system that renders a city, simulates the crowd behaviour and renders each of the individuals. Resources are therefore shared, and the cost of the behaviour simulation must be restricted. This paper presents a way of simulating an intelligent crowd without using more complex approaches such as artificial intelligence techniques. Whereas most available models which describe the behaviour of a crowd usually deal with macroscopic variables like average and flow, we developed an individual-based model.

In the next section 2, we present related work dealing with virtual cities, real crowd features and former work on crowd modelling. In section 3 we describe how we define accessible regions by the pedestrians, using a 2D-analysis of the space. Then, in section 4, we explain the methods used to perform our main contributions: making the agents aware of the city environment, performing a realistic agentto-agent collision avoidance, managing the self-emergence of flows and making the agents walk in small groups. In section 5, we present some details of implementation and the results.

Background

A good overview of human behaviour simulation in virtual environments is presented in [START_REF] Aylett | Intelligent Virtual Environments-A state-of-the-art report[END_REF]. In this section we will restrict the description of work to non-AI approaches. Moreover our main purpose is to give agents realistic trajectories at an individual level and to give the crowd movement a realistic look at a macroscopic level. There has been a lot of work intending to improve individual features of agents: in [START_REF] Ashida | Pedestrians: creating agent behaviors through statistical analysis of observation data[END_REF], for example, they add subconscious actions (such as walking stooped when sad) so that there may be a greater diversity of behaviours.

It is often interesting to base a simulation on real observations. Feurtey [START_REF] Feurtey | Simulating the collision avoidance of pedestrians[END_REF] collected a great number of crowd features, such as for example, the intimacy of the space needed, the relation between the flow of pedestrians, the density of the area and the speed, or the influence of the weather on the behaviour. These data can help to find what is significant in the way a crowd behaves so as to be able to incorporate this into a model. Space syntax techniques [START_REF] Jiang | An integration of space syntax into GIS for modelling urban spaces[END_REF] have a long tradition in urban studies. In the context of cities, space syntax aims at describing some areas in the sense of integration and segre-gation. A location is more integrated if all the other places can be reached from it after going through a small number of intermediate places. With such parameters, the movement pattern in a city can be understood and predicted: for example, the more a region has extensive visibility from the surrounding area (with the longest lines of site), the busier it will be. Some space syntax analysis results even show correlations between predicted segregated areas and areas of high incidence of burglary.

Collision detection

In most simulations, attention is centered on collision detection and reaction. Bouvier and Cohen [START_REF] Bouvier | simulation of human flow with particles systems[END_REF] implemented a simulation of a crowd involving 45,000 persons, based on Newtonian mechanics. However the simulated behaviour remains quite simple.

Reynolds [START_REF] Reynolds | Steering behaviours for autonomous characters, Game Developers Conference[END_REF] implemented, based on an exact mathematical computation, a large set of individual or group behaviours such as pursuing a moving goal, obstacle avoidance, path following, or flow field following. The overall behaviour simulation combines these numerous kinds of individual behaviours. Although the results are really impressive, they are not scalable to a crowd of several thousands agents for real-time simulation.

Feurtey [START_REF] Feurtey | Simulating the collision avoidance of pedestrians[END_REF] proposed a new approach for collision detection based on predicting and modifying trajectories in a (x, y, t) space, using a cone to delimitate the available space based on the analysis of others' trajectories and speeds. Although this method allows pedestrians to evaluate the cost of moving away from their goal, changing direction, accelerating and decelerating, it is yet not scalable to a larger population.

Musse et al. [START_REF] Musse | a model of human crowd behavior: group inter-relationship and collision detection analysis[END_REF] proposed a multi-resolution collision detection algorithm based on two different collision avoidance laws: the slower of two agents stops just before the collision occurs, or both agents go round each other. As the second method is much more expensive in computational time, it is used only when the observer position is really nearby the collision place. However this techniques has not been tested for a large number of pedestrians.

An original and efficient technique is proposed in [START_REF] Franco Tecchia | Real-time visualisation of densily populated urban Environments: a simple and fast algorithm for collision detection[END_REF], for the simulation of around 10,000 virtual humans. In this paper, the position of the pedestrians are managed with a 2D grid. They access, in the surrounding cells, local information for collision with the environment and other pedestrians. The idea has then been extended [START_REF] Franco Tecchia | Yiorgos Chrysanthou: Agent behavior simulator (ABS): a platform for urban behavior development[END_REF] for creating more complex behaviours using the same discretization approach, using different layers.

Group behaviour

In a city, less than a half of the pedestrians walk alone. Indeed most people walk in pairs. To simulate a realistic environment, it is necessary to implement group behaviour.

Using steering behaviours, Reynolds [START_REF] Reynolds | Steering behaviours for autonomous characters, Game Developers Conference[END_REF] invented the well-known boids (for bird-oid). Boids abide by a flocking rule that is simulated using the three Separation, Cohesion and Alignment basic steering behaviours. The result of the combination of these three laws was very good for the modelling of flocks, herds or schools but not collections of humans.

Musse et al. [START_REF] Soraia | Crowd Modelling in Collaborative Virtual Environments[END_REF] defined a crowd as a set of groups formed by human agents, each group having a list of goals. Agents from a same group share the same list of goals but social effects can occur: agents may change group. To generate the group behaviour, they used 3 main laws: the agents from the same groups walk at the same speed, follow the same predefined path and can wait for each other when one agent is missing. In another paper, Musse et al. [START_REF] Musse | a model of human crowd behavior: group inter-relationship and collision detection analysis[END_REF] used sociological rules to enable more human-like reactions. Each agent is specified by a level of dominance, a level of relationship and an emotional status, and is ruled by seeking and flocking laws. Using the same list of goals as before, the behaviour of the crowd is significantly improved: for example, they have implemented the simulation of 4 groups visiting a museum, each of the group aggregating gradually as time elapses. This is an example of global behaviour generated by local laws.

Discussion

Our work was inspired by techniques previously described, and have been adapted to local-control-based techniques used by Tecchia et al. [START_REF] Franco Tecchia | Yiorgos Chrysanthou: Agent behavior simulator (ABS): a platform for urban behavior development[END_REF]. We use a 2D-grid to localise the pedestrians, and for each of our contributions, we compute the decisions made by the pedestrians using information stored in the local surrounding cells. Although this seems to be very simplistic, it allows a high degree of control in the realism of the simulation with the development of complex rules, while keeping the computation cost low. In particular, our space analysis build on 2D maps to retrieve non-modelled information such as pavements and pedestrian crossings. Moreover we use several call-back layers proposed in [START_REF] Franco Tecchia | Yiorgos Chrysanthou: Agent behavior simulator (ABS): a platform for urban behavior development[END_REF] to manage flow streams, guidance in the directions of movement (goals) and group behaviour. We also used the crowd observations made by Feurtey [START_REF] Feurtey | Simulating the collision avoidance of pedestrians[END_REF] to assess the realistic appearance of our simulation. Finally, some collision detection algorithms developed by Musse et al. [START_REF] Soraia | Crowd Modelling in Collaborative Virtual Environments[END_REF][START_REF] Musse | a model of human crowd behavior: group inter-relationship and collision detection analysis[END_REF] and Reynolds [START_REF] Reynolds | Steering behaviours for autonomous characters, Game Developers Conference[END_REF] contribute to our methods. Our collision detection algorithm is inspired by the previous observations [START_REF] Feurtey | Simulating the collision avoidance of pedestrians[END_REF][START_REF] Soraia | Crowd Modelling in Collaborative Virtual Environments[END_REF][START_REF] Musse | a model of human crowd behavior: group inter-relationship and collision detection analysis[END_REF] while implemented using simple local rules on the 2D discreet representation of the environment [START_REF] Franco Tecchia | Yiorgos Chrysanthou: Agent behavior simulator (ABS): a platform for urban behavior development[END_REF]. Architectural theories such as Space Syntax [START_REF] Jiang | An integration of space syntax into GIS for modelling urban spaces[END_REF] link the physical aspects and the pedestrian business associated with a place. Although we do not use directly space syntax techniques, these make it possible to extend our algorithm using accurate laws, e.g. for taking into account of density.

As it has been done before [START_REF] Franco Tecchia | Real-time visualisation of densily populated urban Environments: a simple and fast algorithm for collision detection[END_REF][START_REF] Franco Tecchia | Yiorgos Chrysanthou: Agent behavior simulator (ABS): a platform for urban behavior development[END_REF][START_REF] Tecchia | Real-Time Rendering of Densely Populated Urban Environments[END_REF] and for efficiency reasons, we use a 2D array with a certain resolution to represent the physical city. The input data is a binary map representing only the position of the buildings and the ground. An example of such a map is shown in Fig. 1, where white regions represent the ground, and black regions the areas covered by the buildings. If pedestrians directly access this map, they can be given access to regions tagged by a GROUND value, and refused access to those tagged by a BUILDING value. Giving access to GROUND would only enforce the agents avoiding penetration of the buildings, but it would result in a very unrealistic behaviour. Streets are not only for pedestrians, and it would be more realistic to provide pavements and pedestrian crossings. We used the 2D map given in input to redesign the area accessible by the pedestrians, by defining pedestrian pavements and crossings. Note that we did not model these components in 3D. In the following, we present the algorithm used, which is in three steps. First, we build the pavement areas directly from the 2D map. Second, we detect the corners of the pavements, and finally define pedestrian crossings. The method described in this section is the one we implemented, but we are aware that other techniques could be used. This one has the advantage of being simple and 2D-based.

Definition of the pavements

As our interest is to make the method work for any type of city, the input binary map representing the location of buildings is constructed by rendering the city from above using an orthographic projection. We render only the buildings without the polygons representing the road. In the resulting image, pixels with a depth value equal to infinity are the road (white) and others are the buildings (black). To define the pavement areas, we enlarge the area covered by the buildings using a convolution filter. The new pixels defined by the convolution are tagged as PAVEMENT.

Goal positioning

The following algorithm is invoked after having built pavements around buildings. To place the goals on the corners, we use a new more flexible method that needs to detect the succession of three different levels: GROUND-PAVEMENT-GROUND or PAVEMENT-BUILDING-PAVEMENT. This succession has to be found several times for the same corner with different angles (see Fig. 2) to validate the existence of a goal. This corner detection is performed on the pavements once we have built them with half of their final size. Afterwards, pavements are widened to their full width and the detected corners thus appeared to be in the centre of the pavements.

The inter-visible corners are formed into a graph. Although the number of goals is large, the method is conservative as there is no pavement area without a goal. This ensures, as later on described in section 4.1.1, that every pavement can be occupied by the pedestrians. The number of goals is then decreased by merging neighbour goals together when they are close, while keeping connectivity in the graph. If two goals A and B are to be merged, the resulting goal position is the barycentre of A and B and the new neighbours are those of A and B. This method is summarised in Fig. 3. It can be noticed that the property of the visibility between two connected goals in the graph might not be conserved after the merge. But this does not have a consequence for the rest of the algorithm.

Pedestrian crossings

Using the graph of goals, it is possible to automatically design the pedestrian crossings. For every pair of goals in the graph, it is possible to know if they are on opposite sides of a street. If it is the case, these two goals will be set connected in the graph, and the corresponding 2D area covered tagged with a CROSSING value. Although it does not reflect exactly the way real pedestrian crossings lay, the pedestrian crossings created with this method correspond to a true necessity in terms of traffic (see Fig. 4).

Behaviour

In the previous section, we explained how we encoded the necessary information in a 2D grid to indicate to the pedestrians the accessible areas (CROSSING, PAVEMENT) and the non-accessible ones (BUILDING, GROUND). In section 4.1, we explain how this information is used to significantly improve the individual behaviour while keeping an interactive frame rate. We exploit the precomputed information to define trajectories and perform collision detection with the environment. We then explain how we enhanced the pedestrian behaviour to make it more realistic using simple local rules. In section 4.2, we describe a first attempt on simulating groups of people, still using 2D information.

Individual Behaviour

Trajectories

With our existing system, we were concerned with how to provide relatively straight trajectories to the pedestrians without adding too much complexity. The natural way is by giving virtual agents a direction. However, this results easily in a pedestrian moving toward an obstacle and then reacting to it. This easily makes the simulation similar to the behaviour of "ants" . A more sophisticated way is to give each agent a goals to attain, to retrieve a path to go from one point to another. This could be precomputed to avoid expensive computations at run time.

However we wanted a system that could be run without any precomputation, to encourage behavioural diversity and to reduce the memory storage need, while resulting in an interactive update of the behaviours. For this, we make use of the goals previously placed and used in the system (see section 3). At the begining of the simulation, every pedestrian has been placed next to a goal and has been assigned a new goal to reach, adjacent in the graph of goals, and thus visible from the current position. The direction from one goal to another corresponds roughly to the trajectory of a pedestrian on a pavement or a crossing. It is interesting to note that, therefore, frontal collision with static obstacles are unlikely to happen. When a pedestrian reaches a goal, a new goal is assigned from the list of the adjacent goals in the graph. To avoid virtual agents to do some u-turns, we stored into memory the last three goals assigned to prevent them to be assigned again.

Collision Detection

The pedestrians perform collision detection with the environment (buildings) and between themselves. As was previously proposed [START_REF] Franco Tecchia | Real-time visualisation of densily populated urban Environments: a simple and fast algorithm for collision detection[END_REF], we perform collision detection with the buildings using a collision map. In our simulation, we check up to five tiles ahead to avoid unpredicted collision, and therefore to contribute for the smoothness of the trajectory.

Detecting collision with people is also performed using the information stored in a grid. We consider three main cases of collision between two agents, front, following and perpendicular, as illustrated in Fig. 5. We compare the direction of trajectory of each agent, the velocity factor, and the distance between the agents. According to these parameters, a decision is taken, to deviate from an appropriate angle, to slow down, or to completely stop; the deviation being the preferred option. Depending on the distance to the other agent, the chosen variation can vary from ±Π/8 when the distance is large to ±Π/2 when the distance is short. The list of possible decisions is summarized in Fig. 7.

Flows

We observed that in everyday life, when a congestion occurs, people tend to follow the person in front of them. This is because we trust that the person in front of us probably made the right decision. We implemented this idea, using local information rather than space understanding or dynamic fluid simulation. We represent flows as a direction field, stored into the 2D grid. Every time an agent reaches a cell, its direction is stored. This direction field then fades over time to completely disappear. When an agent reaches a cell, it checks which direction was chosen by a previous agent and when. It then uses this information to make its own decision. The direction field is updated depending on this new direction, but taking into account for the previous direction stored. The equation used is

--→ D(t) = t 0 -t τ + 1 . -→ D 0 (1) ---→ newD = 1 2 t 0 -t τ + 1 . -→ D 0 + -→ D 1 (2)
where D(t) is the way the stored direction fades over time, the maximum time interval being τ , and t 0 being the time when an agent updated D. Using equation [START_REF] Aylett | Intelligent Virtual Environments-A state-of-the-art report[END_REF], a new direction newD is computed, where D 0 is the previously stored direction and D 1 the original direction of the new agent. An example of a direction field is shown in Fig. 6. It is interesting to notice that agents in this context cooperate. A decision taken by an agent in a cell influences the next occupier.

Adaptation to density

In our everyday life, depending on the density of a crowd, we usually adopt different behaviours. We wanted to take this into account in our simulation by encouraging pedestrians to queue in busy areas and to adapt their speed instead of overtaking. Instead of analysing the density at each step of the simulation, we use a density prediction map. In our case we noticed that the density is highest in the region where we placed more goals. This is coherent since it is more likely that a pedestrian is assigned a goal from a high-density goal area. However, this has nothing to do with a space analysis and it would be more interesting to use a density prediction map as produced by space syntax techniques. If we were to use these maps, we could then either simply assign more goals in the area where higher intensity is predicted, or weight the choice of the goals by the space syntax density encoded information.

In our simulation, the density factor influences decisions when pedestrians are to collide with one another. In a congested area, the far range is ignored (we do not check ahead as cells are often closely occupied). In Fig. 7, we summarize the decision taken by an agent when a collision is predicted. When the distance is close, it means that the target cell is occupied. When the distance is near, it means that an agent occupies a cell either 2 or 3 cells away. If the distance is far, it means that an agent occupies a cell either 4 or 5 cells away.

Group Behaviour

The issue of making people walk in small groups has two main goals: the first one is obviously to improve the reality of the simulation. From observations in the street, one can conclude that around half of the pedestrians walk in pairs or more. Moreover, using groups could be a mean to reduce the computing time for the same number of agents. The definition of a group [START_REF] Musse | a model of human crowd behavior: group inter-relationship and collision detection analysis[END_REF] is often given by a set of people who have the same goals or list of goals and the same emotional parameters, that means, in our case, the same average speed and way of accelerating. We define a group by a leader and members. The leader takes the decision relative to the direction to take, and the members follow. Members still perform collision detection before moving using the laws described in section 4.1, but their choice is influenced by the leader. Providing the number of operations necessary to compute the members' behaviour is smaller than the leader's , the average frame rate of the simulation is likely to increase. In real situations, the size of pedestrian groups is rarely bigger than 3. However, tourist groups can be composed of around 20 people. These differences though are often levelled due to the density of the traffic. It is quite rare to see ten people aligned; the group is more likely to split into smaller sub-groups of 2 or 3 pedestrians, each sub-group following the other. In our simulation, we decided to initialise the number of groups randomly using the probability law: 95% of the groups have a size of 1 or 2; 5% have a size between 3 and 10.

The group moves in a sequential way: the leader of a group always moves first. Once it has moved, it tags 3 tiles behind its position with an advised tile flag. Then, the members of the group move one by one in the same frame: they have to move to an advised tile. In order to have enough advised tiles for the whole group, every member, after moving, advises the 3 tiles behind it with the tag. When every member has moved, all the advised tile tags are deleted. Fig. 8 illustrates this method.

We noticed that this method, as we currently implemented it, suffers from some limitations. First, the leader, taken as a pedestrian, is always ahead of the others. We thus decided not to display it while continuing using it for the lead. Second, disconnections between members of the group occur when advised tiles are not well connected, or when members could not reach an advised tile. We are still working on this method to improve it.

Implementation and Results

We implemented the algorithms described in the previous sections. The architecture of the program is described in Fig. 9. Before starting the simulation, each agent is assigned a goal, a position, a direction, a speed vector, and a group. During the simulation, they access the cells corresponding of their current position. However, they have a discreet number of positions within each cell to make the motion smoother. This can however produces some visual inaccuracy in the collision detection algorithm, when two pedestrians occupy two different cells, but have a very nearby position. We tested the behaviour of the agents by tracking them in the environment. When pedestrians are not in the field of view of the camera, their behaviour is updated, but they do not perform collision detection. In Fig. 10, we tracked one agent in different conditions. The trajectory chosen by the agent is satisfactory since it is coherent and smooth.

We imported the behaviour platform into our rendering system [START_REF] Franco Tecchia | Image-based crowd rendering[END_REF][START_REF] Tecchia | Real-Time Rendering of Densely Populated Urban Environments[END_REF][START_REF] Loscos | Real Time Shadows for Animated Crowds in Virtual Cities[END_REF] and the results are shown in Fig. 11. Our simulation runs on a 2Ghz Intel Pentium PC with a GeForce4 Ti4600 graphics card. We measured the average frame rate when simulating with a different number of pedestrians, as shown in the graph in Fig. 12.

In its current implementation, our algorithm is not sufficient for real-time (more than 25 frame per seconds) when we simulate more than 6,000 people. However, the frame rate is reasonable even for 10,000 as the frame rate stays above 10. Additional results can be seen in

Conclusion and future work

We presented a new technique to improve local behaviour of virtual pedestrians moving in a city while managing the complexity and keeping a real-time frame rate. Our method uses a 2D discretization of the space to perform local decisions. We presented automatic techniques to detect areas accessible by pedestrians. Pedestrians are able to control their trajectory by storing the aimed direction and performing collision detection with the environment. Intercollision detection is also performed and decision are made depending on parameters such as speed, direction, and density. We simulate two kinds of behaviour, one in normal traffic conditions, the other one when congestion occurs. In high-density regions, we adapt the inter-collision detection algorithm and we provide cooperation between agents for making decision. Finally, we implemented an algorithm to simulate group behaviour.

Obviously, even if the behaviour has been greatly improved while keeping a low frame rate, further complex behaviours need to be simulated. Agents should be able to enter buildings, stop at bus stops, slow down to do some window shopping, or walk in pairs as parent and child. More complex animation should be explored, allowing pedestrians to seat on benches, wait, or interact together. Better integration with space syntax techniques could allow more accurate occupation of the space. Finally we believe that we could exploit ideas from this paper to provide a simulation for vehicles in a city.

Figure 1 .

 1 Figure 1. Binary image representing the virtual city. This can be automatically obtained by rendering the scene from above, using orthographic projection and storing the image in a z-buffer. A post-processing step might be needed to level the elevations (white for the ground and black for the buildings).

Figure 2 .

 2 Figure 2. We identify a corner by detecting the sequence GROUND-PAVEMENT-GROUND or PAVEMENT-BUILDING-PAVEMENT. We show in this figure two of the tests on angle variation used to validate the existence of a corner.

Figure 3 .

 3 Figure 3. To decrease the number of goals, our algorithm merges goals. Two goals (left) are replaced by their barycentre (right).

Figure 4 .

 4 Figure 4. This is an example of the automatic designing of pavements (in gray) and pedestrian crossings (in light gray).

Figure 5 .

 5 Figure 5. The three types of collision we consider. Type a: front collision. Type b: following collision. Type c: perpendicular collision.

Figure 6 .

 6 Figure 6. An example of the direction field used to simulate stream of people. Agents are represented by the squares, and the direction field is represented by the blue arrows. Notice that the size of the arrows varies as the validity of the field fades over time.

Figure 8 .

 8 Figure 8. Group behaviour method: the square is the leader and the two triangles are members (triangles). (a) The leader advised 3 tiles (light blue) and so did the first member (light red). (b) The leader has moved to a new cell and advised new tiles. (c) The two members have moved to keep the connectivity of the group.

Figure 9 .

 9 Figure 9. The architecture of the behaviour platform.

Figure 10 .

 10 Figure 10. On (a) and (b), we can see the trajectory followed by an agent without being seen by the camera (that is without agent-toagent collision detection performed) during simulation involving respectively 1,000 and 10,000 agents. On (c), an agent has been tracked ( with the agent-to agent collision detection activated) in a busy street; the simulation runs with 5,000 agents. We can see that the trajectory is less smooth than in (a) and (b) due to collision avoidance with other agents.

  http://www.cs.ucl.ac.uk/research/vr/Projects/Crowds/EGUK03/.

Figure 11 .

 11 Figure 11. Example of simulation of our work integrated in the rendering system. The simulation runs with 7,000 pedestrians.

Figure 12 .

 12 Figure 12. The number of frame per second decreases less and less with the number of pedestrians. For up to 6,000 agents, the animation remains fluid.

Acknowledgement

The research described in this paper has been funded with an EPSRC project, Equator, and with an IST EU project, CREATE (IST-2001-34231).


Sophie Giffard-Roisin

Hervé Delingette

Thomas Jackson

Lauren Fovargue

Jack Lee

Aldo Rinaldi

Nicholas Ayache

Reza Razavi

Maxime Sermesant

Sparse Bayesian Non-linear Regression for Multiple Onsets Estimation in Non-invasive Cardiac Electrophysiology

Keywords: ECG Imaging, Personalisation, Relevance Vector Machine, Cardiac Electrophysiology

In the scope of modelling cardiac electrophysiology (EP) for understanding pathologies and predicting the response to therapies, patient-specific model parameters need to be estimated. Although personalisation from non-invasive data (body surface potential mapping, BSPM) has been investigated on simple cases mostly with a single pacing site, there is a need for a method able to handle more complex situations such as sinus rhythm with several onsets. In the scope of estimating cardiac activation maps, we propose a sparse Bayesian kernel-based regression (relevance vector machine, RVM) from a large patient-specific simulated database. RVM additionally provides a confidence on the result and an automatic selection of relevant features. With the use of specific BSPM descriptors and a reduced space for the myocardial geometry, we detail this framework on a real case of simultaneous biventricular pacing where both onsets were precisely localised. The obtained results (mean distance to the two ground truth pacing leads is 18.4mm) demonstrate the usefulness of this non-linear approach.

Introduction

Modelling cardiac electrophysiology (EP) can help in understanding pathologies and predicting the response to therapies such as cardiac resynchronization therapy (CRT). However estimating accurately patient-specific model parameters is then crucial, and it often involves invasive measurements [START_REF] Sermesant | Patient-specific electromechanical models of the heart for the prediction of pacing acute effects in crt: A preliminary clinical validation[END_REF]. In order to replace these invasive measurements -risky for the patient-, Giffard-Roisin et al. [START_REF] Giffard-Roisin | Non-Invasive Personalisation of a Cardiac Electrophysiology Model from Body Surface Potential Mapping[END_REF] personalised the cardiac EP model from body surface potential mappings (BSPM). The onset activation location and the global conduction velocity were estimated in different pacing locations from several patients. However, personalisation may often be needed in more complex situations, such as multiple activation onsets, heterogeneous myocardial tissue (scar) or a particular pathology. Estimation of heterogeneous myocardial conduction using a Bayesian framework has been recently explored by Dhamala et al. [START_REF] Dhamala | Spatially-Adaptive Multi-scale Optimization for Local Parameter Estimation: Application in Cardiac Electrophysiological Models[END_REF], but the other parameters such as the onset are supposed to be known, and the uncertainty on the result is not estimated. The contributions of this work are the extension of a cardiac EP personalisation in order to handle non-linear situations (from single to multiple onsets) and to acquire information relative to the confidence on the results. The methodology, based on a relevance vector regression and on a myocardial shape dimension reduction, is tested and compared with the method from [START_REF] Giffard-Roisin | Non-Invasive Personalisation of a Cardiac Electrophysiology Model from Body Surface Potential Mapping[END_REF] on a real biventricular pacing dataset. The methodology is summarised in Figure 1.

Fig. 1. Pipeline: The RVM (relevance vector machine) regression is performed between BSPM features and a reduced shape space where the patient-specific training activation maps were projected. For a new sample (BSPM data) we estimated its activation map and the regression error. In this paper, the onset locations were further extracted from this estimated activation map.

Materials and Methods

Clinical Data

In this study, we considered a patient dataset composed of BSPM signals, ventricular myocardial geometry (since only the QRS complex is studied, the atria were not included in the model), torso leads and pacing leads locations. The BSPM potentials (from a CardioInsight jacket) were acquired during the procedure at a sampling rate of 1kHz, with a number of torso sensors of 205. The protocol of this study was approved by the local research ethics committee. The approximated myocardial surface as well as the location of the torso sensors and the pacing leads were extracted from 3D imaging (CT scanner). On a rigidly registered generic and volumetric mesh, cardiac fiber orientations were estimated with a rule-based method (elevation angle between -70 • to 70 • ).

Non-invasive Personalisation of a Cardiac EP Model

We used a fast forward EP model derived from the anisotropic Mitchell-Schaeffer cardiac model and a current dipole formulation for computing simultaneously the cardiac electrical sources and body surface potentials [START_REF] Giffard-Roisin | Non-Invasive Personalisation of a Cardiac Electrophysiology Model from Body Surface Potential Mapping[END_REF]. From the generation of a large database of simulated transmural myocardial potentials and torso signals, we learned patient-specific parameters of the EP cardiac model. In order to retain the important aspects of the QRS complex from the BSPM signals, specific descriptors on the normalized BSPM signals were extracted (such as timings, area under the curve, sign of extremum). Specifically, the location of the onset activation was estimated by firstly regressing the activation times of the transmural myocardial mesh, before localizing the minimal time. Every simulated set of BSPM signals v i was perturbed by a Gaussian random noise of mean 0 and std = 2e -3 × norm(v i ). This was done for robustness and in order to give more confidence to electrodes closer to the heart: where the potentials are of higher amplitude, the signal-to-noise ratio would be larger.

Dimensionality Reduction of the Myocardial Shape

The myocardial tetrahedral mesh can have a large number of elements or vertices. At the same time, the signal to be reconstructed, the activation map, is strongly correlated spatially due to the propagation of the electric potential throughout the myocardium. Therefore, it is meaningful to reduce the dimension of the regression variable, the activation times. A simple way would be to use a coarser mesh but this would be at the expense of reducing the accuracy of the onset locations. Instead, we propose to use a hierarchical decomposition of the mesh, naturally provided by the eigenmodes of a structural matrix. To this end we chose the eigen-decomposition of the stiffness matrix associated with the Laplacian operator. This decomposition has been widely used in various spectral shape analysis [START_REF] Reuter | Laplace-spectra as fingerprints for shape matching[END_REF][START_REF] Umeyama | An eigendecomposition approach to weighted graph matching problems[END_REF] and is closely related to the modes of vibration of the myocardium. The extracted eigenvectors are naturally sorted by ascending order to spatial frequency. By selecting the first few eigenmodes, we only keep the large spatial variations. If we call t the vector of N activation times at each vertex of the myocardial mesh, we get the following reduction and reconstruction formulas:

t red = V M t ; t rec = V M T t red
with t red the coordinates of t in the reduced space, V M the N ×M matrix of the first M eigenvectors of the stiffness matrix, and t rec the reconstructed activation times. The matrix V M is independent of t and is thus computed only once. An example of reconstructed activation map (on 14K vertices) using 400 modes is shown in Figure 2(c). From Figure 2(a), we can see that the mean reconstruction error is less than 2 ms (max: 7 ms) for 400 modes.

Parameter Estimation using Relevance Vector Regression

In order to regress the myocardial activation times from the BSPM features, we use the Relevance Vector Machine (RVM) regression method [START_REF] Tipping | Fast marginal likelihood maximisation for sparse Bayesian models[END_REF]. This ap- proach performs sparse kernel regression based on a sparsity inducing prior on the weight parameters within a Bayesian framework. Unlike the commonly used Elastic-Net or Lasso approaches (based on L1 Norm a.k.a Laplacian prior), the RVM method does not require to set any regularization parameters through cross-validation. Instead, it automatically estimates the noise level in the input data and performs a trade-off between the number of basis (complexity of the representation) and the ability to represent the signal. Furthermore, unlike SVM regression or Elastic-Net, it provides a posterior probability of each estimated quantity which is reasonably meaningful if that quantity is similar to the training set. In our setting, we used Gaussian kernels for the non-linear regression whose variance of the kernel parameters need to be defined. The RVM regression only selects the input BSPM feature set that can best explain the activation map in the training set, thus limiting the risk of overfitting. RVM is a multivariate but single-valued approach and therefore the regression was directly performed on the reduced space of section 2.3: only 400 regressions are needed to perform an estimation of the 14K activation times. We used a Gaussian radial basis function with a kernel bandwidth of 1e4 (from cross-validation). On an EliteBook Intel Core i7, a regression of 1000 training samples and 1235 features runs in 40 sec.

3 Application to the Personalisation of a Simultaneous Biventricular Pacing

Simultaneous Biventricular Pacing Personalisation

We consider here a non-ischaemic implanted CRT patient that underwent a pacing lead optimization procedure with a BSPM device. In particular, the 205 working torso electrodes recorded a biventricular simultaneous pacing. The anatomy as well as the location of the torso sensors and the pacing leads were extracted from CT images. Because of important artifacts coming from the pacemaker, only a coarse epicardial geometry of the myocardium is visible. That is why a generic volumetric myocardial mesh of roughly 65K tetrahedra was manually registered and scaled to the epicardial geometry. The CardioInsight Technologies software also solves the inverse problem on this epicardial surface (based on the standard formulation using a Tikhonov regularization and the generalized minimal residual algorithm), see Figure 3(a). In this work, we used this activation map as part of the evaluation of our method. From this simultaneous pacing, we want to retrieve the two onset locations. Locating both activation onset locations by means of an EP model parameter estimation has first been studied by He et al. [START_REF] He | Noninvasive three-dimensional activation time imaging of ventricular excitation by means of a heart-excitation model[END_REF], but only on synthetic data. For this particular goal, our simulated training set was composed of 1000 simulations with fixed conduction velocity (0.5m/s) and with two onset locations randomly selected on the surface of the myocardial mesh (endocardium and epicardium). We compare here the results of the activation map regression of (i) the Kernel Ridge Regression provided by [START_REF] Giffard-Roisin | Non-Invasive Personalisation of a Cardiac Electrophysiology Model from Body Surface Potential Mapping[END_REF], (ii) the RVM regression independently on each point of the cardiac mesh and (iii) the RVM regression using the reduced shape space (Figure 3). The two ground truth onset locations (pacing sites) are the red dots. On top is the flattened representation of the left ventricular endocardium, where the apex is at the center [START_REF] Soto-Iglesias | Integration of electro-anatomical and imaging data of the left ventricle: An evaluation framework[END_REF]. The activation map provided by the CardioInsight system on the coarse surface mesh is shown in Figure 3(a). Although this map is not precise and cannot be used as a ground truth, it gives an idea of the wave shape. We can see that the kernel ridge regression acts as a blurry mixture of both initializations, not able to separate them. The RVM regression performed on each vertex captures the two onset zones, but the resulting activation maps are noisy and the two minimal values are not easy to capture. The error distances to the true pacing leads are 31.7 mm (left lead) and 41.2 mm (right lead). On the other hand, the RVM performed on the reduced space captures two onset locations with an error distance of less than 23mm for both leads, while having a smooth solution. The mean number of relevance vectors retained was 213 (out of 1000 samples).

Results

The RVM provides the result as a Gaussian probability distribution, where the retained solution of each regression was the mean. By looking at the estimated variance across each mode, we can estimate the confidence on our result. On Figure 4 are plotted the projections on the first spectral modes of the simulated activation maps (training set) as well as the estimated solution. We have also represented the confidence interval (+/-standard deviation, std). First, we can see that the retained sample lies inside the training set point cloud, which is important for RVM to perform well. We have projected the point-wise estimated std onto the myocardial mesh (Figure 5(a)) where we can see that all the vertices from the left ventricle have an estimated std below 17ms (and below 14 ms for the septum). Although higher values (max: 28.9 ms) are found in the right ventricle, we can notice that the estimated activation times from this region were greater than 100 ms. However, the confidence on the activation times is not sufficient to quantify the onset location error. This is why we additionally randomly sampled 10K coordinates from the estimated Gaussian distribution in the reduced space (see red ellipsoid from Figure 4). We then reconstructed the activation maps for each one of them and retrieved their minima as estimations of left and the right onsets (see Figure 5(b) for the right onset). For the most probable location, the distance error to the ground truth location was 12.3mm for the left lead and 24.5mm for the right lead. The standard deviation of the error was 18mm for the left onset and 11.4mm for the right onset. We can notice that the left onset was located more accurately, however with less confidence.

Discussion

The presented method holds several limitations. One current limitation of the RVM regression is the fact that it only performs multivariate regression of a single output scalar value but not of a vector value (unlike KRR for instance) although multivalued versions have been recently proposed [START_REF] Folgoc | Sparse Bayesian registration of medical images for self-tuning of parameters and spatially adaptive parametrization of displacements[END_REF]. Concerning the reconstruction from the reduced space, we also tested it with sharp edges in the activation map, like scar tissue blocking the activation wave. Despite higher errors of activation times in the scar, the boundaries of the scar were well captured so we hope we could extend our method to ischemic patients.

The estimations of the covariance matrix of the activation map are provided as a by-product of the RVM regression, giving an estimation of the regression error. However, other sources of error may be taken into account in order to have a full apprehension on the confidence of the result. We think it is the reason why the cloud of possible onsets of Figure 5(b) did not exactly include the true lead. If the torso sensors were well localized by the CT scan, we had to use a generic mesh because no precise myocardial shape was available. We would like to test our method on patient-specific myocardial meshes which will also help in modelling the error source when using generic meshes. Regarding the errors coming from the BSPM data, they were considerably reduced by the fact that the disconnected or bad leads were automatically excluded, and that we added some Gaussian noise to our simulated BSPM for robustness. Finally, we should also evaluate the EP modelling errors, for instance by analyzing the discrepancy between the true and simulated BSPM signals.

This new method has been tested on only one patient so more experiments are needed for its validation. As a future work, we want to exploit the generic properties of this approach to robustly estimate additional cardiac parameters, like the (local) conduction velocity for instance.

Conclusion

We presented a new methodology for multiple onset estimation from BSPM and applied it to one clinical dataset. This was integrated in a novel framework for the personalisation of cardiac EP parameters from BSPM data. This method relies on the generation of a simulated patient-specific database on which a relevance vector regression estimates the activation map from a new set of BSPM signals.

As input, specific shape-related features were extracted from the BSPM. As output, the activation map was projected onto a reduced space defined from myocardial shape oscillations. This pipeline enables the estimation of parameters in more complex situations, as the example presented here with the location of two onsets from a real biventricular pacing BSPM sequence. We think this method could be useful for a generalization of cardiac EP personalisation, one of the advantages being the confidence on the regression provided by the RVM.

Fig. 2 .

 2 Fig. 2. Example of reconstruction of an activation map (on 14430 vertices) from the eigenvectors of the stiffness matrix: (a) Reconstruction error wrt. the number of modes (b) original activation map (c) reconstructed activation map from 400 modes.

Fig. 3 .

 3 Fig. 3. Results for locating both onsets: ground truth lead locations are the red dots. (a) CardioInsight inverse solution (b) kernel ridge regression activation map result (c) relevance vector regression separately for the 14K vertices (d) relevance vector regression on 400 modes of the reduced space. The color maps are showing short activation times (red) to large ones (purple). On top: flattened representation of the left endocardium in the conventional orientation (i.e. anterior on top).

Fig. 4 .Fig. 5 .

 45 Fig. 4. Confidence interval in the reduced space (+/-std): (a) projection on modes 1 and 2 (b) projection on modes 2 and 3. blue dots: projection of the 1000 simulated activation maps used for training. Red dot: estimated activation map.

Acknowledgments. The research leading to these results has received funding from the Seventh Framework Programme (FP7/2007-2013) under grant agreement VP2HF n • 611823.


Mathilde Pulh
email: mathilde.pulh@u-bourgogne.fr

Remi Mencarelli
email: remimencarelli@yahoo.fr

Rémi Mencarelli

Web 2.0: Is the museum-visitor relationship being redefined ?

Keywords: participatory marketing, Web 2.0, museums, desacralization, legitimacy

Growing integration of Web 2.0 techniques by museums is indicative of their readiness to redefine their relationship with their audience. Where visitors were traditionally kept at arm's length, the use of these techniques now involves them at every stage in implementing the museum offer on the Internet. From an inductive perspective and through an extended examination of various sites and tools offered by organizations, this article aims first to highlight the different roles currently devolved to the public in terms of communication, mediation and artistic creation. It then goes on to analyse the ramifications of visitor involvement. If these tools are able to develop the audience competences and to make museums less stuffy, their implementation may also induce tensions by challenging their authority and legitimacy and by disenchanting their visit experience.

Introduction

In the museum sphere, a new pattern has emerged in recent years through the mobilization of Web 2.0, a "participation architecture" based on the online sharing [START_REF] Cooke | Web 2.0, social networks and the future of market research[END_REF]Divart, 2010). This form of interactive marketing uses various tools, as social networking, podcasts, wikis, blogs (Varadarajan and Yadav, 2009), in order to encourage the visitors to take an active part in the production of museum content [START_REF] Cairns | Mutualizing Museum Knowledge: Folksonomies and the Changing Shape of Expertise[END_REF][START_REF] Proctor | Crowdsourcing -An Introduction: From Public Goods to Public Good[END_REF][START_REF] Ridge | From Tagging to Theorizing: Deepening Engagement with Cultural Heritage through Crowdsourcing[END_REF] and to transform them in real contributors [START_REF] Anderson | What is Web 2.0? Ideas, technologies and implications for education[END_REF][START_REF] Sigala | Web 2.0, Social Marketing Strategies and Distribution Channels for City Destinations: Enhancing the Participatory Role of Travelers and Exploiting their Collective Intelligence[END_REF].

While many commentators readily speak of a new digital and participatory revolution [START_REF] Jenkins | Convergence Culture. Where Old and New Media Collide[END_REF], this change in the relationship between museums and their public is actually part of a movement already initiated many years back. Operating in an increasingly competitive environment while subject to growing budget constraints, museums have long since re-appraised their mission of disseminating their collections to the public [START_REF] Caves | Creative Industries, Contracts Between Arts and Commerce[END_REF].

Being aware of the need to attract more visitors, they now make wide use of techniques from the commercial sphere [START_REF] Baumgarth | Brand Orientation of Museums: Model and Empirical Results[END_REF] such as branding strategies [START_REF] Caldwell | The Emergence of Museum Brands[END_REF], sophisticated admission pricing [START_REF] Bailey | Charging for Admission to Museums and Galleries: Arguments and Evidence[END_REF] or even peripheral paying offers with shops and restaurants [START_REF] Tobelem | Le nouvel âge des musées: Les institutions culturelles au défi de la gestion[END_REF]. This commercial turn has helped to place the public at the heart of museums' concerns and it has grown in scope with the emergence of a collaborative logic, notably through the use of ICT.

Thus, new mediation practices were first developed to enrich in situ visits and to promote visitor participation in immersive exhibits. The use of the multimedia terminal in the 1980s or the current usage of huge tactile screens, tablets and smartphone applications are part of this logic [START_REF] Cordier | Une culture scientifique et technique au service d'une co-construction des savoirs[END_REF][START_REF] Jarrier | Impact of Mediation Devices on the Museum Visit Experience and on Visitors' Behavioural Intentions[END_REF]. And now, ICT, especially 2.0 ones, help to strengthen the relationship with the audience within the framework of development of on-line museum activities [START_REF] Bertacchini | The Future of Museum in the Digital Age: New Models for Access to and Use of Digital Collections[END_REF].

In view of these developments, it is necessary to relativize the idea of a far-reaching change in the visitor-museum relationship with the emergence of Web 2.0. Moreover, it is important to highlight that the use of these interactive tools is not specific to the museum sector. Indeed, ICT have greatly transformed society in terms of private relations between individuals and of business relations between consumers and companies [START_REF] Castells | The Internet Galaxy[END_REF][START_REF] Rheingold | Using Participatory Media and Public Voice to Encourage Civic Engagement, Civic Life Online: Learning How Digital Media Can Engage Youth[END_REF].

However, the current talk about 2.0 tools as a new managerial panacea in the museum sphere, calls for a closer examination of these practices. First, because this sector is largely more advanced than other areas of culture [START_REF] Castells | Museums in the Information Era: Cultural Connectors of Time and Space[END_REF] and its strategies of redefinition of the relationship to the public via the 2.0 tools could help and inspire other structures. Second, because the implementation of these tools may induce paradoxical tensions in the relationship with the visitor. This analysis is all the more necessary because practitioners often face an emphatic discourse about the possibilities offered by these tools, which need to be renewed at an extremely rapid pace [START_REF] Parry | Museums in a digital age[END_REF]. They do not aware of the risks they face in seeking to involve the visitor.

The objective of this article is to question the roles assigned by the museum to the visitor through the use of 2.0 tools, in order, firstly, to understand the different forms of participation mobilized and their positive impacts, but also, secondly, to highlight theirs difficulties and ambiguities. Realizing, in an inductive perspective, an extended examination of various internet sites and devices used by different kinds of organizations (see Appendix 1), the first part allows us to expose different forms of visitor participation at the heart of museum offers.

Three roles have been identified and are defined in terms of communication, mediation and artistic creation. Each of these functions will be presented and illustrated with representative examples from the sector considered by researchers as emblematic cases of 2.0 techniques used by museums because of their exemplary approach and the innovative nature of the applications. The second part analyses the consequences of participatory engagement of museums in relation to the public. While this emerging collaboration seems conducive to the development of visitors' skills and to the abolition of symbolic barriers of museum access, there is a need in parallel to enquire into its potential to challenge museum' authority and legitimacy, and also into the risk of disenchanting the museum experience.

-The new-look visitor in the museum 2.0 era

Museums have long held an important position in the world of the Internet as illustrated by the myriad virtual museums and on-line exhibitions [START_REF] Mctavish | Visiting the Virtual Museum: Art and Experience Online[END_REF] or the Google Art Project which lets web users view close to 40,000 digitized works from the collections of renowned institutions (like the MoMA, the National Gallery, Tate Britain and the Van Gogh Museum). But this digital presence is based primarily on a unilateral definition of the museum offer, with very little input sought from users. The development of 2.0 devices invites visitors, on the contrary, to get involved, to make the museum offer their own, or even to create content.

In order to identify the different levels of audience participation, we have maintained a careful watch over current museum practices. Our results are based on an enlarged intelligence around 2.0 devices in 64 U.S. and European museums (Appendix 1). The museums were selected to cover a wide variety of organizations in terms of size (attendance) and thematic collections (art, science and history) to also be able to compare their practices. During the year 2012-2013, for each selected museum, the 2.0 devices used were identified and described (type, goal, implementation, results obtained). We then grouped these devices in accordance with criteria established in the marketing literature to describe consumer participation (intensity, centrality, complexity) and the own expertise of researchers. At the end, three forms of participation stood out, intimating the existence of three figures of visitors: the "communicator-visitor", the "curator-visitor" and the "artist-visitor".

-Visitor involvement in defining museum communication

Integrating 2.0 techniques initially meant breaking away from the unilateral scheme of communication (defined and controlled by the museum) so as to benefit from the ability of each web user to disseminate information and promote exchange on behalf of the museum (role 1). Some institutions were quick to understand the importance of ensuring a proactive presence on the web and so to join social networks. These sites, by providing tools and interfaces for interaction, presentation and communication [START_REF] Castells | Why Networks Matter[END_REF], allow museums to engage their audiences like so many channels of communication. The MoMA in New York, for example, joined Facebook in March 2008 and currently has more than 1.5 million 'friends', with a present and active community. The word 'museum' brings up thousands of Facebook accounts for museums offering to share photos, articles, videos and events in addition to traditional information.

Whereas these tools have enabled museums to become familiar with participatory marketing, while maintaining close control over the content generated, other initiatives have arisen that play more on involving the general public in diffusing content. They can be classified in terms of the level of interaction they engender:

• Given the popularity of blogs, many organizations have adopted this tool (akin to a logbook; [START_REF] Rheingold | Using Participatory Media and Public Voice to Encourage Civic Engagement, Civic Life Online: Learning How Digital Media Can Engage Youth[END_REF]

-Visitors as museum mediation agents

Some institutions have decided to go a step further by making visitor action and participation more central. Some tools allow the general public to take on a role traditionally held by the curator and to freely arrange the museum content and define the mediation components (role 2). This turns essentially on two forms of action.

First, museums may invite the public to take a hand in defining systems for classifying their works. This 'folksonomy' approach refers to a form of 'folk classification' whereby users index digital documents as opposed to traditional systems of classification by experts [START_REF] Peters | Folksonomies: Indexing and Retrieval in the Web 2[END_REF]. Thus, like Philadelphia Museum of Art, Brooklyn Museum encourages its visitors to help in indexing collections by associating their own key words (tags) with the on-line museum content ('Tag, You're it!') or by choosing to validate tags assigned by other users ('Freeze Tag!'). This form of social tagging by the public therefore seems to be a new way to describe, encounter and understand cultural projects [START_REF] Cairns | Mutualizing Museum Knowledge: Folksonomies and the Changing Shape of Expertise[END_REF][START_REF] Ridge | From Tagging to Theorizing: Deepening Engagement with Cultural Heritage through Crowdsourcing[END_REF], providing a counterweight to the often complex scientific thesaurus drawn up by curators.

These particular forms of testimonials from other visitors define an alternative way of discovering the museum, without any specialist guidance. The folksonomies allow a form of intuitive navigation, different from the official visit based on the expertise of art historians.

Moreover, this 'naïvigation' may also be guided simply by serendipity [START_REF] Sinclair | The folksonomy tag cloud: when is it useful?[END_REF] and may be an excellent way for web users to make visitor-centered discoveries.

Similarly, the general public can play a big part by helping to select works to be exhibited. In late 2013, the Museum of Fine Arts in Boston communicated on the establishment of a 'crowdsourced exhibit' entitled 'Boston Loves Impressionism', for which it invited its visitors to play the role of the curator, allowing them to vote (via a specific website but also via facebook) for their favourites among 50 works from its impressionist collection. The public could select 10 paintings that would be highlighted with a heart symbol to indicate their status as 'audience favourite' and the three favourite works were displayed at the entrance to the exhibition.

By allowing such action from virtual communities about the definition of the offer, museums do indeed seem to be moving towards 'living' culture to suit their audience.

-Redefining visitors as full-blown artists

Other initiatives go even further in constructing collaborative museums insofar as they enable visitors to create their own 'works' for inclusion in the collections (role 3). The aim is for museums to more directly embrace the 'consumer made' concept, defined as the outcome of bringing to bear the skills of one or more consumers so as to modify or improve firms' offers and so come up with an original creation [START_REF] Cova | On the Road to Prosumption: Marketing Discourse and the Development of Consumer Competencies[END_REF]. Review of the devices developed recently by museums reveals this ever greater involvement of visitors in creating the exhibits, with such collaboration being either marginal or central to the museum's offer. Some museums encourage their web-user public to create, but merely include such creations as a side-line to their central offer. This is the case of institutions which look to give a role to visitor input simply by capitalising on their previous museum visit like the Cité de l'Architecture. In 2012, this museal organization invited visitors to take photographs in the exhibition galleries and to publish them on a dedicated blog to highlight the public's interpretation of the architecture. Some operations also invite visitors to create cultural objects. But these are often relegated to play a peripheral role in the cultural offer. Thus, in 2012, as part of a temporary exhibition on Bob Dylan, the Cité de la Musique suggested users should replay one of the American musician's song and post the video on Dailymotion. The movies were subsequently presented to visitors to the exhibition.

In other instances, visitor contributions may be far more central, as when they are asked to bring in or create an artwork in response to a specific cultural project. In this way, in 2012, the National Museum of American History of Washington invited its web-user public to post family photographs on Flickr, a selection of which was included in the 'Growing Up in 1950-1965' exhibition. Finally, some initiatives come about around virtual museums which are created by and for their own users on a common theme. For example, the National Museum of African American History and Culture, the brick-and-mortar venue for which is due to open in 2015, was officially created in 2003, through a contributory virtual space, providing Afro-Americans with the opportunity to immortalise their own history in a social memory network of collective recollections.

Whereas only a short while back visitors were considered as harmful in museums [START_REF] Lindauer | The Critical Museum Visitor[END_REF], examination of these 2.0 devices attests to a major renewal of the roles that museums assign to their audiences. It also demonstrates that the use of Web 2.0 is not only the fact of superstar museums [START_REF] Frey | Superstar museums: an economic analysis[END_REF]. These innovations are also carried by smaller structures, which place this research of interaction at the heart of their strategy. But this development is not without its consequences for the visitor-museum relationship. The impact this has needs to be understood.

-The paradoxical tensions of the 2.0 devices on the visitormuseum relationship

Museum directors hope to achieve a number of managerial objectives through such participatory actions. Museums count on enhancing the status of visitors who thus become partners of the institution and so are liable to display greater commitment and attachment to it (Troye and Supphellen, 2012). By building a closer relationship, museums hope to promote frequentation of their website and the actual museum, to benefit from positive word-of-mouth recommendations and from a broader audience in the long term.

However, beyond this fallout, the emplacement of this participatory logic changes the nature of the relationship between visitors and museums and raises questions about the position occupied by the public and the museum in the social space. Consequently, it is important to show that this collaborative trend can have beneficial effects by developing the visitors' skills and facilitating access to museums. But it can also have adverse effects by questioning the museum authority and identity and by disenchanting the museum experience.

-Enhancing visitor skills and desacralizing museums

The massive mobilisation of 2.0 devices by museums leads logically enough to the emergence of a hyper-actor consumer who is able to communicate, cooperate and even debate with the museum in a more egalitarian way, much like a full member of the institution [START_REF] Russo | Participatory communication with social media[END_REF]. This valuation of 'amateurs', who are neither novices nor professionals, who are increasingly called for by organisations [START_REF] Ritzer | Production, consumption, presumption. The nature of capitalism in the age of the digital 'prosumer[END_REF], reflects the true democratisation of abilities, leading, in the museum context, to the content being made more accessible.

These collaborative movements also prompt the emergence of a form of 'collective' intelligence arising from the pooling and synergy of individual knowledge which cyberspace tends to accelerate [START_REF] Levy | Collective Intelligence: Mankind's Emerging World in Cyberspace[END_REF]. Museums can harness these collective inputs especially as, according to [START_REF] Surowiecki | The Wisdom of Crowds. Why the Many are Smarter than the Few[END_REF], crowds prove far better than any expert at solving even the most complex problems. Like contributory communication operations (role 1), these devices enable institutions to take advantage of the creative potential of visitors by engaging them in a co-promotional logic [START_REF] Cova | Critical Perspectives on Consumers' Role as Producers': Broadening the Debate on Value Co-Creation in Marketing Processes[END_REF], while contributing to the development of a strong sense of belonging.

These devices therefore enable visitors to put together their own museum and help to strip the entity of its revered aura. Web 2.0 tools seem able to respond to a recurrent issue with museums: removing the symbolic barriers preventing access to their heritage. The museum is no longer likened to a sanctuary but rather to a Lego museum defined as 'an open and accessible museum […]; a museum one can make one's own, in the same way one can easily construct a complex, personal 'work' from simple Lego bricks designed to be easy to assemble and to free up creative potential' [START_REF] Bausson | Le musée-légo[END_REF]. In this logic, the aim is no longer to provide a 'shop window' for the public to contemplate but to give precedence to a participatory museum architecture to make it easier for visitors to relate to the content. Social tagging techniques are emblematic of this. Insofar as visitors themselves categorise the objects they come across, they can more readily appreciate the value of what is on offer (role 2). A study of tagging behaviour of visitors to US museums (The Cleveland Museum of Art;

The Indianapolis Museum of Art; The Metropolitan Museum of Art, etc.) showed that 86% of visitor tags did not match any of the museums' reference labels. Moreover, more than 88% of the tags were thought useful for research by museum staff [START_REF] Trant | Exploring the potential for social tagging and folksonomy in art museums: proof of concept[END_REF]. These taggings therefore constitute 'alternative knowledge' arising out of a 'democratic' process of reappropriation of decision-making by visitors [START_REF] Cordier | Une culture scientifique et technique au service d'une co-construction des savoirs[END_REF].

While this collaborative orientation hints at a bright future for museums, it is best to be cautious because the emergence of these new visitors raises some weighty questions at the same time.

-Challenge to the museum's authority and disenchantment with the museum experience

According to [START_REF] Cova | Consumer made : quand le consommateur devient producteur[END_REF], many structures are using these 2.0 devices rather by imitation, without necessarily anticipating the necessary precautions to obtain an effectively participation from consumers [START_REF] Divard | Le marketing participatif[END_REF]. So, before jumping headlong into major investments, museums must also weigh the limits of consumer involvement. In an operational way, structures must be wondering if they are ready to engage in a real dialogue with the consumer. The museums must anticipate the control of this interactivity in order not to be overwhelmed by visitors' contributions, which may be not up to the expected results (authors, 2009). At the same time, museums must be careful to avoid an artificial interaction with the visitors, in which they would be in fact kept away.

Beyond these practical reflections, some experts more generally criticize the fact to appeal to visitor' contributions. Indeed, while for some commentators, the amateur is in these days of the Internet the key figure in a new form of expertise [START_REF] Leadbeater | The Pro-Am revolution[END_REF], others, like [START_REF] Keen | The Cult of the Amateur: How Blogs, MySpace, YouTube, and the Rest of Today's User-Generated Media Are Destroying Our Economy, Our Culture, and Our Values[END_REF], denounce an illusory wisdom of crowds that is highlighted in the 2.0 world.

For them, the democratic definition of content, like devices inviting visitors to become creators (role 3), cannot actually produce anything other than a chaotic jumble of references, from which artists or works might be unable to emerge in the absence of any real authority. Now, historically, the museum is the sole authoritative voice because of its scientific and/or artistic expertise (MacDonald and Fyfe, 1996). This voice is now lowered and even drowned out by visitors' voices. Such a preponderance of 'popular' expertise may therefore impoverish the museum's degree of artistic or scientific innovation. The museum might be tempted to reduce risk-taking and to focus more on a consensual and unifying content, liable to attract the most visitors. Finally by promoting public participation and by defending a certain appropriation of content, it may prove difficult not to take the desire for democratization beyond the point at which it tips into demagogic excess. This might undermine the museum's legitimacy and its true contribution 'in the service of society and its development' (mission defined by ICOM). [START_REF] Simon | The Participatory Museum[END_REF] argues that a balance must be maintained between visitor input and curator expertise to ensure the quality and coherence of museum content.

Open-ended strategies, devoid of any clearly defined project and supervision, may lead to the museum's intentions becoming scrambled. For example, by choosing to allow web-user visitors to have their say by means of blogs or social networks (role 1), the museum proposes a new form of communication by which individuals may speak out freely, by-passing the legitimate, traditional forms of expression (Gunthert, 2009). Museums must therefore accept the codes specific to these instruments while at the same time defending their authority, their reputation and more fundamentally still their expertise.

That expertise largely underpins the museum's legitimacy, which is defined historically as the 'institutional figure of showing' [START_REF] Deloche | Tracking Prosumption on eBay: Desire, Enchantment, and the Challenge of Slow Re-McDonaldization[END_REF] and its traditional distancing of itself in its relationship with visitors. Yet, what becomes of the autonomy of the artist and curator as the fundamental principle of museum cultural policies if visitors become creators and purveyors of museum content (roles 2 and 3)? The decline in the sacralised character of the museum offer is admittedly nothing new (Vaughan, 2001), but by encouraging visitor involvement within the actual cultural offer, participatory devices help to hasten that decline. As the cultural artefact stands apart in being something out of the ordinary, it seems inevitable that by encouraging visitor contributions the museum offer will end up being quite ordinary instead. Moreover, this can lead to forms of 'conversion' of the offer, as with the tools of folksonomy, leading some visitors no longer to work on collective indexing but to make individual value judgments about the works exhibited.

Lastly, whereas museums use 2.0 tools in an attempt to surprise visitors, to involve them and therefore to re-enchant their museum experience, the widespread development of such devices may quite the contrary engender a form of disenchantment [START_REF] Ritzer | The McDonaldization of Society[END_REF]. The effervescence of collective social production may gradually give way to participation becoming 'routine', guiding the offer down a slippery slope towards a form of 'disenchanted pro-sumption' [START_REF] Deloche | Tracking Prosumption on eBay: Desire, Enchantment, and the Challenge of Slow Re-McDonaldization[END_REF]. Apart from the fact that the increasingly common use of such tools is likely to make them run of the mill, their short life span means they must be constantly updated if they are to remain effective and consumers not tire of them.

Conclusion

The analysis proposed here has revealed, from an interpretative perspective, a typology of consumers based on the different roles that museums propose to attribute to them. The use of 2.0 devices gives rise to communicator visitors, mediator visitors and artist visitors as contributors to museum actions, in a wide variety of structures. Apart from identifying these new figures, this article also points out the consequences in terms of how the relationship between the public and museums is being redefined. If these tools are able to develop the audience competences and to make museums less stuffy, their implementation may also induce paradoxical tensions by challenging their authority and legitimacy and by disenchanting their visit experience.

The development of digital devices accentuates a little further the transition museums embarked upon many years back. They no longer present themselves as the inescapable supervisory authority but increasingly as platforms for exchange among their different communities. But, paradoxically, abandoning the heritage authority and the remoteness that museums traditionally maintained in their relations to their audiences may ultimately undermine the legitimacy and the very identity of these institutions. Indeed, in seeking to reinforce the relationship with the public via the democratization and/or the desacralization of collections, institutions can very easily slip into a demagogic attitude, as rightly pointed out by some practitioners.

However, these conclusions have to be moderated given the limits associated with the exploratory and interpretative approach used. First, regarding the methodology, it is difficult to claim to have identified all of the practices associated with 2.0 tools in the museum sphere.

The objective of exhaustive observation is not complete. Second, in a highly changing environment, the rapid lifecycle of these tools and the immediacy of the associated actions mean that any analysis may rapidly become obsolete (Euzeby and Martinez, 2012). Furthermore, the roles of visitors emerged on the basis of an interpretation. Such a construction cannot but be subjective to some extent [START_REF] Cova | Les figures du nouveau consommateur: une genèse de la gouvernementalité du consommateur[END_REF].

Given the limitations, this reflection can only be a first step for further research to analyze the effects of 2.0 devices on the visitor-museum relationship. It could be appropriate to understand more precisely firstly the managerial goals associated with these tools, secondly the impact of these tools on the proximity of visitors with the museum and finally the possible divergences of representations related to these tools between visitors and managers.

  to share with their audience. Examples include the blogs of Toulouse Natural History Museum, of the MoMA, the Guggenheim or the blog 'Art you can get into' proposed by the Mattress Factory Art Museum. In some cases, museums allow visitors to leave their own audio or video testimonials, which are then posted on the museum's channel. Despite their apparent modernity, such blogs are essentially a digital version of traditional visitor's books. • More interactively, some museums go so far as to incorporate messages created by their visitors into their communication campaigns. Following the lead of the 'It's time we MET!' campaign launched by the Metropolitan Museum of Art in 2009, the MUCEM (Musée des Civilisations de l'Europe et de la Méditerranée, Marseille), when it opened in June 2013, used a Facebook application allowing web users to create their own avatars in the museum's colours and insert a slogan to encourage visitors. The operation was made into a contest with the winners seeing their portraits included in the museum's official communication campaign. These examples are concrete illustrations of visitor participation in defining the content of institutions' communication. However, it should be borne in mind that, behind the declared participatory logic, the museums generally control the content, choosing what and what not to publish so as to maintain control of their 'e-reputation'.

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