Nicolas Dufour

Audrey Chapelle

Fabien Mesnilgrente

Véronique Conédéra

Philippe Menini

V Conedera

TECHNOLOGICAL IMPROVEMENTS OF A METAL OXIDE GAS MULTI-SENSOR BASED ON A MICRO-HOTPLATE STRUCTURE AND INKJET DEPOSI-TION FOR AN AUTOMOTIVE AIR QUALITY SENSOR APPLICATION

Keywords: Gas Sensors, Metal Oxides (MOX), Micro-hotplate, Inkjet

teaching and research institutions in France or abroad, or from public or private research centers.

Technological improvements of a metal oxide gas multi-sensor based on a micro-hotplate structure and inkjet deposition for an automotive air quality sensor application

I -Introduction

Currently available gas sensors are often rather expensive to fabricate, have high energy consumption and low selectivity. Thus, using novel fabrication techniques and nanomaterials, such as inkjet [1] and screen printing [2], new approaches and functionality of the gas sensors can be achieved.

Up to now, inkjet printing has been less studied than the others techniques [3], but it has the potential to be a versatile, cost-efficient and easy-to-scale-up method for producing a large variety of microstructured materials.

Metal oxide thin film sensors have been widely used for gas sensing applications thanks to their sensitivity toward a large variety of gases [4], but they suffer from a lack of selectivity. Therefore, in this paper, we have explored two ways to overcome this problem: the technological way and the chemical and electrical way.

In the first case, the inkjet printing method has been used to deposit three materials widely used in automotive domain [5]: copper oxide (CuO), zinc oxide (ZnO) and tin dioxide (SnO 2 ). Each of these materials has a particular behavior under gases, allowing the growth of the selectivity of a multi-sensor composed by them.

In the second case, a post-technological process has been studied to increase selectivity by controlling the heater and sensing resistance. Concerning the heater control, a well-known method which consists in using a temperature modulation of the sensor, by changing the power consumption [6], has been used. This kind of profile allows fast chemical reaction changes, and consequently fast gas sensitivities changes. Sensing resistance changes, which are a less studied specifica-tion, are based on the fact that the current/voltage characteristic of MOX films exhibits a non-linear behavior [7]. More details about this phenomenon have already been presented in previous work [START_REF] Dufour | ICST Conference[END_REF]. Thus, a specific profile associated to the temperature modulation has been developed.

Finally, in order to use the multi-sensor as an electronic nose, a multivariate classifier has to be associated to the dynamical profile of the system. The most used classifier in this domain is the Principal Component Analysis (PCA) [START_REF] Penza | [END_REF].

The work presented here aims at presenting and characterizing an optimized MOX multi-sensor structure developed in order to improve sensitivity and selectivity, under different gases related to the automotive domain.

II -Experimental Details

A. Device micro-machining and improvement

The first tested platform consists of a silicon bulk on which a thermally resistive bilayer SiO 2 /SiN x membrane was grown. On top of this bilayer two conductive platinum electrodes were sputtered: one as a heater and a second for the sensing resistor, separated from each other by a passivation layer SiO 2 . Finally, the rear side of the chip was etched to reach a suspended membrane in order to increase the thermal resistance and to limit the thermal dissipation (see figure 1). However, it occurs randomly leakage current problem between the two Pt metallization.

In order to solve that random failure, a new platform was then designed. The new model consists in sputtering Pt electrodes: heater resistor and sensing resistor, simultaneously, on top of the bilayer SiO 2 /SiN x . Contacts are opened in a previously deposited passivation layer (see figure 2). This change allows reducing cost and the distance between the two metallization was multiplied by four (from 500 nm to 20 µm).

B. Inkjet printing

Before jetting, a surface treatment was realized on the substrate in order to reduce the surface energy leading to a hydrophobic surface except the deposition zone. Perfluorodecyltrichlorosilane (FDTS) molecules bond onto surfaces, terminated with hydroxyl (-OH) groups, such as SiO 2 forming a regular covalent bond. The coating is performed by vapor phase deposition and the substrate was treated with standard processes as oxygen plasma prior to the FDTS deposition.

For the ink fabrication, 5 or 10 wt% of each material were dispersed in ethylene glycol by ultrasonic bath for an hour. The obtained dispersion was left to sediment for 1 day. The supernatant dispersion on top was collected and then used for inkjet printing.

Inkjet printing was performed with Altadrop equipment using a 50µm nozzle head from Microfab.

The chuck temperature and the addition of diethylene glycol (DEG) were optimized for each sensing material in order to avoid the coffee ring effect. The jetting parameters are gathered in the table1.

C. Characterization technique

The sensor was placed into a chamber flown by different gases. The composition and humidity of the gas mixture was controlled by Mass Flow Controllers and the global flow rate was checked by a debimeter and set to 200 sscm. Temperature and sensor resistance were controlled by Source Measurement Units.

The sensing layer was heated to 500°C prior to the test in order to desorb the surface from contaminating species. The working temperature was set in the range of 200-500°C. After a period of stabilization under dry synthetic air (two hours), the specific gases were introduced during a given time while ensuring the global flow remains constant.

Responses of different materials gas sensor toward four gases (CO, NH 3 , C 2 H 4 O and NO 2 ) were characterized and the corresponding sensitivities were compared.

Sensing material morphologies were characterized using a classic SEM S4800 (see figure 3).

III -Results and Discussion

A. Comparison of the two designs

Before performing the new technological process in clean room, a multiphysics modeling has been realized with COMSOL software to compare both designs.

The physics model used to simulate this work was the Joule Effect model. Different heater voltages have been applied (between 0.5 and 6V) in stationary mode and the temperature changes at the surface of the microhotplate have been observed (see figure 4).

In addition to reducing costs, achieving the two metallization simultaneously made several significant improvements on temperature distribution. First, the fact to intercalate the sensing electrode in the heater one generates a more circular distribution of the temperature. Moreover, the inkjet deposits being circular too, so this characteristic was very important. Then, the temperature gradient between the center of the surface of the micro-hotplate and the outer limit of the contact open area has been improved by 25% with the new design. Thus, the temperature uniformity of the sensing layer has been improved too and the chemical reactions are better controlled.

The metallization modification brought a better temperature distribution at the surface of the micro-hotplate.

4: Temperature variation at the surface of the microhotplate; comparison between old and new design; heater voltage = 5V

In order to validate the results obtained, the maximum temperature obtained by simulations for different voltages applied across the heater has been compared to infrared measurements with a real sensor (see figure 5). The two plotted curves are very close and the maximum deviation was below 25°C.

B. Gas testing

As presented in the introduction, a specific dynamic test profile has been developed into obtained the best information of each sensor. This profile includes heater and sensing layer bias changes.

Several profiles have been observed, and one that allows good reproducibility, the fastest stabilization and the best discrimination was selected (see figure 6). The temperature profile is the following: each step lasts 2 seconds; the temperature baseline is the highest operating temperature (500°C in this case); the others steps are at lower temperatures (400-300-200°C); a complete cycle lasts 12 seconds.

During one temperature cycle, one bias current is applied to the sensing resistor; three different polarizations are successively set (0.01-0.1-1 µA in this example); a complete cycle lasts 36 seconds. Both profiles are repeated throughout the test, under various gaseous atmospheres.

Four different gases at two different concentrations have been injected, firstly individually and then by binary mixtures. One gas injection lasts fifteen minutes and two air sequences are interposed between each one.

The sensitivity (at a given concentration) is calculated as the ratio of the absolute difference between the stabilized resistances of the device under dry air and under the specific gas to the resistance under dry air:

( ) . (1)
During one complete cycle (36 seconds), we obtained 36 different mean values and information about each sensor. It therefore generates a huge amount of information for each test and several comparison parameters are possible (temperature, sensing layer bias, sensor, gas …). For example, if we only consider 4 gases at 1 concentration (CO-100ppm, NO 2 -0.2ppm, NH 3 -5ppm, C 2 H 4 O-2ppm), 2 temperature from the profile (500 and 200°C), and 2 bias current (10 nA and 1 µA), we can already draw several conclusions (see figure 7).

Concerning the CuO-layer, at 10 nA, decrease the temperature reduces the response to CO; the opposite was observed at 1 µA. For the ZnO-layer, the sensor was not responding to NH 3 at 10 nA, but it had a good response at 1 µA. And for the SnO 2 -layer, responses were almost always the same whatever the current applied. Each sensing layer has presented a specific behavior towards various changes, so it is necessary to use a decision-making system to ensure all information.

C. Towards an electronic nose

As presented before, each sensing material has furnished a specific behavior in presence of different gaseous ambiances. The best way to associate all information was to use a multivariate analysis. The most commonly used is the PCA. This kind of analysis has been tested on our samples. The correlation matrix was composed by the normalized responses in function of the temperature and the bias current for each gaseous ambiance. Each sensor has been treated individually and then all together (see figure 8).

Using all these results, it has been possible to classify all tested ambiances.

IV -Conclusion

The optimization of MOX sensors thanks to technological and electrical control improvements has been investigated.

A new optimized micro-hotplate has been presented. This structure has shown a better temperature distribution, due to the metallization rearrangement, avoiding any leakage current through the passivation layer. The inkjet deposition has been realized directly on the membrane, what constitutes a technological novelty. The deposited layers have shown a stable and reproducible behavior.

A specific optimized dynamic temperature and bias current has been developed. It includes temperature changes between 200 and 500°C, and bias current changes between 10 nA and 1 µA.

The responses to three sensing materials (CuO, ZnO and SnO 2 ) with four different gases related to the automotive domain (CO, NO 2 , NH 3 and C 2 H 4 O) have been observed. The behavior under gases of each sensing layer was totally different with the temperature and bias influence. Thus, these responses have been analyzed with PCA, and a fully classification of all ambiances have been realized.

To complete this work, it could be considered to replace the PCA by a supervised decision-making system, as a decision tree or a discriminant analysis, to design a recognition system.

Figure 1 :

 1 Figure 1: Old design micro-hotplate gas sensor: (a) chip top view, (b) cross sectional view

Figure 2 :

 2 Figure 2: New design micro-hotplate gas sensor: (a) chip top view, (b) cross sectional view

Table 1 :Figure 3 :

 13 Figure 3: SEM micrographies of (a) CuO, (b) ZnO, (c) SnO 2.

Figure 5 :

 5 Figure 5: Temperature versus power consumption at the surface of the micro-hotplate; comparison between simulation and measurement

Figure 6 :

 6 Figure 6: Power consumption and bias current profiles applied to the sensors

Figure 7 :

 7 Figure 7: Normalized responses for the 3 sensing layers at the 4 gases with 2 bias current (10 nA and 1 µA)

Figure 8 :

 8 Figure 8: Cross-PCA of the 3 sensing layers

Acknowledgments

The authors express their gratitude to RENAULT S.A.S. for the financial support and to the Coordination Chemical Laboratory of Toulouse for the preparation of the tin oxide nanoparticles powder.


Charalampos Rotsos

Gianni Antichi

Marc Bruyère

Philippe Owezarski

Andrew W Moore

Marc Bruyere

OFLOPS-Turbo: Testing the Next-Generation OpenFlow switch

Keywords: SDN, OpenFlow, Open-Source, High Performance, Testing, NetFPGA

published or not. The documents may come   L'archive ouverte pluridisciplinaire

I. INTRODUCTION

Research on SDN technologies and primarily its predominant realisation, the OpenFlow protocol, has highlighted a wide range of applications to improve modern network functionality. OpenFlow control applications can improve network management, monitoring and performance, while being backwards-compatible with protocol and network stacks. As a result, many vendors, within only a few years, have integrated production-level support in their products, in a effort to transfer innovative research output to the market. Nonetheless, such continuous network innovation introduces a dilemma for network testing, as relevant platforms remain closed and proprietary and provide limited flexibility. To achieve compliant and functional equipment, effort must be put into all parts of the network-equipment life-cycle, from design to production. Furthermore, the philosophy of the OpenFlow protocol introduces new performance challenges in network device design, dissimilar to the challenges of traditional network equipment, and sets testing flexibility as a primary requirement. Specifically, OpenFlow introduces a reduction in the network control timescales which closely approximate flow control timescales. As a result, the implementation of the OpenFlow switch driver, the interface between the control and data plane, becomes a complex and critical performance bottleneck, influencing the control architecture of the network and, effectively, the overall performance.

In this paper we claim that the effective integration of the OpenFlow protocol in production requires a flexible and high-precision open-source measurement platform which will provide a deep understanding of switch capabilities to users. The openness and flexibility is important in order to establish an evolvable community tool. The OpenFlow protocol, currently defining its 1.3 version, has greatly transformed its design over the years, reflecting both the development and deployment experience of the technology on a constantly widening range of network environments. The OpenFlow community requires a performance testing platform capable to co-evolve with the protocol and to support rapid prototyping of experimentation scenarios which highlight the impact of new protocol features. Furthermore, as network link speeds increase exponentially, meaningful packet-level measurements increase their precision requirements. For example, 10GbE has become the de-facto solution for the aggregation layer of modern datacenter networks, a first-class citizen of the SDN ecosystem. For 10GbE links, a measurement precision in the order of hundreds of µsec is insufficient for OpenFlow applications in the security domain, as it translates into a potential unauthorized transmission of 100KB of sensitive information.

In this paper, we present our effort to enhance the measurement capabilities of the OFLOPS [START_REF] Rotsos | Oflops: An open framework for openflow switch evaluation[END_REF] switch performance evaluation framework. More specifically, we present the integration we provide with the NetFPGA-10G platform 1 and the design of the Open Source Network Tester (OSNT2 ) platform [START_REF] Antichi | Osnt: Open source network tester[END_REF].OSNT enhances OFLOPS with sub-µsec precision and 40 Gbps full bi-directional traffic generation and capturing, providing a highly flexible and open platform for OpenFlow experimentation at high data rates.

II. OFLOPS-TURBO DESIGN

A schematic of the design of the OFLOPS-Turbo design is depicted in Figure 1. The OFLOPS-Turbo architecture consists of a software and a hardware subsystem. The software subsystem runs the core OFLOPS functionality, along with the measurement module of the user. A measurement module contains both the control and data plane functionality of the experiment. The hardware subsystem consists of a series of NetFPGA-10G cards running the OSNT design and is responsible to fulfil the data plane requirements of the experiment. The user can interconnect the OFLOPS-Turbo host with one or more switches in arbitrary topologies and measure with high precision specific aspects of the network architecture, both on the data and control plane. For the rest of the section, we describe the design of the OSNT hardware design and the OFLOPS software architecture.

A. OSNT

The OSNT is a fully open-source traffic generation and capturing system. Its architecture is motivated by limitations in existing network testing solutions: closedsource/proprietary, high costs, inflexibility, and lack of important features such as high-precision timestamping and packet transmission. Primarily designed for the research and teaching community, its key design goals were low cost, high-precision time-stamping and packet transmission, as well as, scalability. In addition, the opensource nature of the system gives the flexibility to allow new protocol tests to be added to the system. The prototype implementation builds upon the NetFPGA-10G platform -an open-source hardware platform designed to support full line-rate programmable packet forwarding. The combination of Traffic Generator and Traffic Monitor into a single FPGA-equipped device allows a per-flow characterization of a networking system (i.e., OpenFlowenabled switch) within a single card. Using one or more synchronized OSNT cards, the architecture enables a user to perform measurements throughout the network, characterizing aspects such as end-to-end latency and jitter, packet-loss, congestion events and more.

The OSNT Traffic Capture subsystem is intended to provide high-precision inbound timestamping with a losslimited path that gets (a subset of) captured packets into the host for further processing. The design associates packets with a 64-bit timestamp on receipt by the MAC module, thus minimizing queueing noise. The timestamp resolution is 6.25nsec with clock drift and phase coordination maintained by a GPS input. The OSNT Traffic Generation subsystem consists of several micro-engines, each of which generates traffic according to a given generator function. The traffic generator has an accurate timestamping mechanism, located just before the transmit 10GbE MAC. The mechanism, identical to the one used in the traffic monitoring unit, is used for timing-related measurements of the network, permitting characterization of measurements such as latency and jitter. When enabled, the timestamp is embedded within the packet at a preconfigured location and can be extracted at the receiver as required.

B. OFLOPS

Measuring OpenFlow switch implementations is a challenging task in terms of characterization accuracy and precision. Predominantly, production-level OpenFlow-switch devices provide proprietary OpenFlow drivers with minimum logging capabilities. Characterising the performance of an OpenFlow-switch requires a black box approach to the problem, where multiple input measurements must be monitored concurrently in order to characterise the behaviour of the switch.

OFLOPS is an holistic measurement platform which enables the development of custom OpenFlow-based experiments. The platform provides a unified API that allows developers to control and receive information from the data and control channels, as well as, SNMP switchstate information. Experimenters can develop measurement modules on top of OFLOPS, implementing custom OpenFlow applications and measure their performance through the data plane.

Currently OFLOPS provides a wide range of elementary testing modules evaluating the performance of flow actions, flow table management, flow counter extraction, OpenFlow-based packet injection and capturing and detecting potential performance penalties due to OpenFlow operation co-interaction. OFLOPS uses OSNT primarily to control the data plane of the network using a simple interface, provided by the OSNT user-space library. The re-desgined DMA engine of the NetFPGA-10G platform improves OFLOPS user-space capturing and transmission capabilities, compared to the earlier OFLOPS hardware design for the NetFPGA-1G platform.

III. CONCLUSIONS

In this paper we have presented OFLOPS-Turbo, an open and flexible OpenFlow testing framework for the next-generation of OpenFlow switches. OFLOPS-Turbo takes advantage of the OSNT hardware design for the NetFPGA-10G platform and provides support for 10GbE traffic generation and capture, coupled with a high precision timestamping functionality. We believe that OFLOPS-Turbo will provide the tool for the OpenFlow community to better understand the performance impact of OpenFlow implementations and motivate a community of rigorous OpenFlow testing.

Figure 1 .

 1 Figure 1. OFLOPS-Turbo design

http://www.netfpga.org

http://www.osnt.org

Acknowledgements

This work was jointly supported by the EPSRC INTER-NET Project EP/H040536/1 and the Defense Advanced Research Projects Agency (DARPA) and the Air Force Research Laboratory (AFRL), under contract FA8750-11-C-0249. The views, opinions, and/or findings contained in this article/presentation are those of the author/ presenter and should not be interpreted as representing the official views or policies, either expressed or implied, of the Defense Advanced Research Projects Agency or the Department of Defense.


Sotiris Manitsaris
email: sotiris.manitsaris@uom.edu.gr

Alina Glushkova
email: alina.glushkova@uom.edu.gr

Frédéric Bevilacqua
email: frederic.bevilacqua@ircam.fr.

Fabien

F Moutarde
email: fabien.moutarde@mines-paristech.fr.

ACM Journal on Computing and Cultural Heritage, Special issue "Interacting with the past" Capture, modeling and recognition of expert technical gestures in wheel-throwing art of pottery

Keywords: Technical gestures, Know-how, Modeling, Recognition, Wheel-throwing Perception, HCI, Inertial sensors, Machine-Learning

published or not. The documents may come

INTRODUCTION

Cultural expression is not limited in architecture, monuments, collection of objects, or music. It also includes fragile intangible live expressions, which involve knowledge and skills. Such characteristics can be human gestures, the color of our voice or facial expressions. They are controlled by the intelligence of the human creativeness depicted in music, dance, singing, theatre, human skills and handicraft. This kind of culture has been termed Intangible Cultural Heritage (ICH), which is manifested inter alia in oral traditions and expressions, performing arts (music, dance, theatre etc.) social practices, knowledge and practices concerning nature, universe and the traditional craftsmanship [Unesco 2003]. In traditional craftsmanship, the rare gestural knowledge primarily consists of hand and finger motions.

The project "ArtiMuse" aims at proposing a multidisciplinary research approach for the gesture recognition methodologies applied in musical and handicraft interactions. In order to preserve these gestural skills that require high expertise it is necessary to identify, record, analyze, model and recognize them. This paper presents the development of a methodology for the modeling of kinematic aspects of technical gestures using gesture recognition technologies based on wireless inertial sensors. This methodology has been implemented, applied and evaluated on the gestural skills of two expert potters for simple objects, such as bowls. The Human-Computer Interface called "ArtOrasis" has been developed for gesture capturing, modeling and recognition of expert gestures. It also proposes the time alignment of a gesture compared to a model gesture, which is a quite promising perspective for a performance comparison between expert and learner.

STATE OF THE ART

The study of human gestures has been of special interest in different research fields. In the ICH domain in particular, body and hand gestures are important means of communication, of expression and of creativity. Preservation and transmission of handicraft skills is often done by studying and analyzing recordings, verbal descriptions and documents. However, the important role of gestures has lead several researchers to model them using motion capture and gesture recognition technologies.

E-DOCUMENTATION AND DIGITAL ETHNOGRAPHY

Most existing methods for skill preservation are based on verbal descriptions of movements, often in conjunction with multimedia content such as graphic / photographic material [D. [START_REF] Chevallier | Savoir faire et pouvoir transmettre: Transmission et apprentissage des savoir faire et des techniques[END_REF]. Another widespread method is the video recording of skills and techniques of the gesture accompanied with verbal commenting. This method was applied to preserve knowledge of technical gestures in power stations in France during the manipulation of different technical tools. A video camera has been placed on the helmet of the workers to record their movements [Le Bellu 2012].

In the field of ICH, for many years, ethnologists have studied the characteristics (arts and techniques, oral traditions and living expressions) of groups and communities in their surroundings. [D. [START_REF] Chevallier | Savoir faire et pouvoir transmettre: Transmission et apprentissage des savoir faire et des techniques[END_REF]. Enora Gandon worked on wheel-throwing gestures in a cultural context. She studied the impact of the cultural background of the human on the development of motor skills in Wheel-throwing art [E. [START_REF] Gandon | Influence of cultural constraints in the organization of the human movement: proposition of a theoretical framework and empirical support through the example of pottery-throwing (France / India Prajapati / India Multani Khumar)[END_REF]. Kuo-An Wang studied the case of weaving Chinese traditional items with Bamboo [Kuo- [START_REF] Wang | Digitization and Value-Add Application of Bamboo Weaving Artifacts[END_REF]. He created a digital archive with approximately1200 objects accompanied by images and videos presenting the gestures involved in the creation of the 9: 3 ACM Journal on Computing and Cultural Heritage, Special issue "Interacting with the past" objects. Through meetings and interviews conducted with the craftsman, Kuo-An Wang has identified a set of 20 basic gestural patterns of weaving. Then, he connected each of the digitized objects file with a combination of those patterns.

However some significant limitations can be identified in these methods. While describing a gesture on a piece of paper using photos or figures, the gesture is limited in two dimensions and it does not represent any realistic information about how the gesture has been performed. In the case of video recording, the gesture is also represented in two dimensions but still limits the information that can be extracted.

MOTION CAPTURE AND GESTURE RECOGNITION

The use of innovative technologies for motion capture permit to overcome some of the limitations mentioned above, to achieve a faithful record of the gesture and model it stochastically. A significant number of studies have been based on various techniques for modeling and recognition of gestures based on motion capture. These technologies can be subdivided in 3 categories: a) marker-based, b) marker-less and c) inertial motion sensors.

Marker-based approaches use optical-markers and active computer vision, which require expansive commercial systems, such as Vicon Peak or Optitrack. This type of sensor has been used for the modeling of music performances of a violin player [START_REF] Rasamimanana | Effort-based analysis of bowing movements : evidence of anticipation effects[END_REF][START_REF] Demoucron | Observations on bow changes in violin performance[END_REF]. One of the most important limitations of the marker-based gesture recognition systems is that they are not robust to occlusions.

Marker-less technologies do not require subjects to wear special equipment for tracking and are usually based on passive computer vision approaches. For example, Microsoft Kinect is a low-cost depth camera that provides good results for the recognition of global body postures, such as dance gestures [START_REF] Raptis | Real-Time Classification of Dance Gestures from Skeleton Animation[END_REF]. It provides Cartesian representation of the human motion and it is usually used for tracking joints of the body. Nevertheless, it is less precise for hand gestures. To solve this problem there is an ongoing research aiming at the creation of a hand skeletal model for finger detection. A hand skeletal model for depth images, provided by the PMD CamBoard Nano time of flight camera, has been applied to capture music-like finger gestures [START_REF] Dapogny | Towards a Hand Skeletal Model for Depth Images Applied to Capture Music-like Finger Gestures[END_REF] based on Shotton's algorithm and pixel wise classification through Random Decision Forest. This model is currently being developed and adapted for pottery-like finger gestures, as a training database is required. An elaborated algorithm is also necessary for scene and object segmentation in the case of technical gesture recognition in wheel-throwing art of pottery. Moreover, the depth cameras are self and scene occlusion-dependent and the development of hand/finger skeletal model for the capturing of finger gestures in pottery is a challenging approach in a medium term vision.

Inertial Motion Sensors [R. [START_REF] Aylward | A Compact, Wireless, Wearable Sensor Network for Interactive Dance Ensembles[END_REF][START_REF] Coduys | TOASTER and KROONDE: High-Resolution and High-Speed Real-time Sensor Interfaces[END_REF][START_REF] Todoroff | Wireless digital/analog sensors for music and dance performances[END_REF], E. Fléty et al. 2011] or commercial interfaces, such as the Wii joystick [D. [START_REF] Grunberg | Gesture Recognition for Conducting Computer Music[END_REF], permit to track gesture features continuously and in real-time. These sensors have been tested and used in dance and music performances [START_REF] Bevilacqua | Wireless sensor interface and gesture-follower for music pedagogy[END_REF][START_REF] Bevilacqua | Continuous realtime gesture following and recognition[END_REF]. For example, inertial sensors have been used for motion capture aiming at the archaeological reconstruction, understanding and interpretation of different possibilities of use of an ancient Iron Age roundhouse [S. [START_REF] Dunn | Motion in Place: a Case Study of Archaeological Reconstruction Using Motion Capture, CAA2011 -Revive the Past[END_REF].

PRESSURE MEASUREMENT AND MECHANICAL STRESS

In case of wheel throwing pottery, a study has been conducted to evaluate potters' skills by taking into consideration the mechanical characteristics of the objects created by the potters. The Von Mises stress index [J. [START_REF] Lemaitre | Mécanique des Matériaux Solides[END_REF] has been used to measure the mechanical stress operating in the object and it has been related to the throwing difficulty, proposing thus an idea for potters' skills assessment [E. [START_REF] Gandon | A proxy of potters' throwing skill: ceramic vessels considered in terms of mechanical stress[END_REF]. In this object-oriented approach, the pressure is measured by analyzing the mechanical characteristics of the created object after completion, and not in real-time during the creation of the vessel. Therefore, despite its interest, this mechanical stress estimation cannot easily be used to build an interactive pedagogical tool, because interactivity requires real-time analysis of the gesture performed. Since designing this type of tool is one of our final goals, our approach for the preservation and modeling of gestural skills is based on the analysis of the kinematic aspects of the gesture required for the creation of the object, which can be easily captured and analyzed in real-time.

METHODOLOGY

In the methodology proposed below, the goal is not a simple video recording with verbal descriptions, nor just the digitalization of information. The objective is to study and to model rare gestural know-how involved in handicrafts, to gather data about different biomechanical, kinematic aspects of a technical gesture (distances between the hands, angles of the vertebral axis, rotations of the joints, gesture's trajectory etc.), as well as the body postures and to create information about its various parameters. The modeling of the gestural know-how and the effective recognition of gestures have been done in different methodological phases as described below and represented in the figure 1. Capturing, modeling, online recognition and time alignment, are fundamental stages and extensions towards the creation of an interactive pedagogical tool for the transmission of gestural skills based on sensorimotor learning. This comparison could be done in real time in order to provide a sonic or optical feedback to the learner, and thus drive him/her to correct his/her gestural errors.

HANDICRAFT AND EXPERT SELECTION

The first step is to select the type of handicraft and the craftsman to be studied. The handicraft selected should respect some criteria and be compatible with the goal of the research and with its' technical conditions and constraints. Since we are using gesture capture technologies, one of the important criteria for technical feasibility and application of the methodology is to avoid use of specific tools during gesture execution.

IDENTIFICATION OF THE EFFECTIVE GESTURES

The second phase concerns the identification of effective gestures. Effective gestures have a direct impact on the material and it is important to identify them and to distinguish them from other auxiliary gestures. This goal can be achieved in interaction and collaboration with the expert, by conducting interviews and observing him while working. He should show and describe a complete sequence of gestures, effective and auxiliary, in order to define a dictionary = ∈ℕ of his/her effective gestures.

GESTURE CAPTURING

In the context of expert technical gestures, a possible simplification of the complexity of the human body can be based on rotation of segments, which is the case of the data stream (observation vector) provided by the sensors. Therefore, the expert's body segments are tracked and the sensor captures kinematic properties about angle rotations in 3D space and record them as a sequence of observation vectors 0: = 1 … ∈ℕ , where ∈ℕ is one observation vector and the total number of observations. The vector

= 1 … , ∈ℕ
represents the kinematic descriptors for a given time stamp .

GESTURE MODELING AND MACHINE-LEARNING

GESTURE REPRESENTATION

Once the data acquisition is completed, then : should be normalized. Euler angles, Quaternions are possible rotation representations of the motion. Euler rotation is a rotation about a single Cartesian axis. According to the Euler's Rotation Theorem, every orientation can be described as a rotation from some other reference orientation as a sequence of three elemental rotations (precession, nutation, and intrinsic rotation). Quaternions are representing orientations and rotations of objects in 4D, where there is one real axis and three imaginary axes (i, j, and k). Another way to model the rotations of the motion of the human body is the Direction Cosine Matrix (DCM), which is based on a triad of unit vectors. The rotation is described by specifying the coordinates of the triad of unit vectors in its current position, based on a non-rotated coordinate axes that is used as a reference.

GESTURE MODELING AND ONLINE CHARACTERISATION

One of the difficulties in gesture recognition is that the same gesture can be performed in a variety of ways, in particular the change in speed of execution. For this reason, one of the authors has developed a system called "Gesture Follower" that can be seen as a hybrid approach between Dynamic Time Warping (DTW) and Hidden Markov Models (HMM) [START_REF] Bevilacqua | Wireless sensor interface and gesture-follower for music pedagogy[END_REF][START_REF] Bevilacqua | Continuous realtime gesture following and recognition[END_REF]. This system is a template-based method, which allows the use of a single gesture to define a gesture class. This requirement is necessary due to the limited access of gesture data in our application. Nevertheless, we use a HMM formalism to compute in real-time computation measures between the template and the incoming data flow.

As described by [START_REF] Rabiner | A tutorial on hidden markov models and selected applications in speech recognition[END_REF], Hidden Markov Model can be used to model recorded time series (training procedure) and to compute the likelihoods (one per HMM) that the hidden state sequence 0: = 1 … ∈ℕ , generated the new observation sequence 0: = 1 … ∈ℕ . In our case, we are interested in the following information:

• The likelihood , that the observation sequence was produced by the different models. The sequence with the maximum likelihood !" to generate : indicates the gesture from the gesture dictionary = ∈ℕ . • The likeliest state sequence : of the observation sequence : . This statesequence allows for obtaining a time-warping between the model and the observed sequence.

In order to greatly simplify the learning procedure and to guarantee a high temporal precision in the gesture modeling, we associate each template to a state sequence. We define one state for each sample data of the template (applying a constant sampling rate). We compute the different likelihoods in real time using the well-known for-ward procedure that return the results incrementally, and that can be implemented efficiently even in the case of models with a large number of states [START_REF] Bevilacqua | Continuous realtime gesture following and recognition[END_REF]].

CASE STUDY

SELECTION OF THE WHEEL-THROWING POTTERY AND OF THE POTTERS

For the implementation of this methodology the wheel-throwing pottery has been chosen mainly for two reasons. First of all, because of the high social and cultural value of this traditional profession for the local communities of the Macedonia Region in northern Greece (community of the potter A) and of the French Riviera (Côte d'Azur) in Southern France, (community of the potter B) which is also famous for the large number of ceramists working there. An important number of associations and independent experts are actively promoting this handicraft in these regions. A list of candidate craftsmen has been established. Two experts from the regions above have been selected. They detain a very high level of expertise in wheel-throwing pottery art and also important pedagogical experience. The potter A is teaching this handicraft in a centre for therapy and social reintegration of people with substance dependencies and the potter B is a senior craftsman with more than twenty years of experience in practicing wheel-throwing pottery. The second criterion is more technical and linked to our system's technical characteristics. The wheel-throwing pottery is based on gesture control of the material. There should be no interference of the hands of the potter and his material with specific tools or other intermediate mechanisms.

Capture, modeling and recognition of expert technical gestures in wheel-throwing art. • 9: 7 ACM Journal on Computing and Cultural Heritage, Special issue "Interacting with the past"

BASIC GESTURES FOR THE CREATION OF A BOWL

The two selected expert potters presented to us a complete sequence of gestures that are used to create a bowl with a simple shape (a bowl) with different quantities of clay.

It has been asked to the potter A to create 5 bowls of 18-20 cm of diameter, 10 cm of height, with approximately 1.3 kg of clay. Additionally, it has been asked from the potter B to create bigger bowls of the same shape of those created by potter A, with 20-23 cm of diameter and 13 cm of height with 1.75 kg of clay.

After meticulous observation of video recordings of the gestures of the two potters and after the interviews conducted with them, we have concluded on the following 4 basic gestural phases of creating a simple bowl (Figure 3). The first configuration -*

The final configuration and removing

Potter B

gestures

) .

Centering the clay

+ .

Opening the bottom , .

The raise -.

The first configuration

/ .

The final configu ration 0 . Since the dimensions of the bowls created by the two experts are different, the required gestures inside the gestural phases are also different. Obviously, there is a common track between the gestures of the two experts, even if the dimensions of their objects are slightly different. The bigger an object is, the more gestural work it requires. Consequently, since the object of the potter A is smaller, the 4 basic gestures that we have identified correspond exactly to the 4 basic gestural phases presented above. For the bigger object, the potter B has more clay to manage and he is paying more attention to shape refining.

Removi ng the object

They are also considered as representative of the high-level wheel-throwing pottery skills, since the creation of the object takes in case of the potter A only 60-75 sec and for potter B 120-140 sec. The fluidity and the speed of potters' gestures as well as the hand coordination are elements that constitute the basis of the rare know-how and gestural skills. All gestures have duration of 15-25 seconds. The centering and bottom opening 1 1 consists of fixing of the clay on the wheel, hands are pressing steadily on the material aiming at the opening of the bottom. Then, the potter's hands are picking up the clay, defining the height of the bowl through the second gesture, 1 2 the raise of the clay. Then the body posture is changing, slightly turning on the right or on the left side for the first configuration of the shape, for 1 3 . Precise finger gestures are specifying the basic form of the object. The fingers of the one hand are fixing the clay and of the other are forming the object. His hands are too close to each other, touching the inner and outer sides of the clay respectively. After this stage, the potter is making the final configuration of the shape 1 4 . His fingers are controlling and equalizing the bowl thickness and at the end the potter passes a very fine wire between the bowl and the wheel in order to take the bowl.

EXPERIMENTATIONS: CAPTURING THE POTTER'S GESTURES

After the definition of the above effective gesture the potters are asked to put the motion capture suit that can easily provide real-time access to motion information and permits the data acquisition (Figure 4). This suit contains 11 inertial sensors (gyroscopes and magnetometers) and it is covering for the upper part of potter's body, his wrists, his neck and his head. It provides an automatic filtering for the correction of magnetic disruption. It has been selected for the gesture capturing and the implementation of the methodology described below. It is occlusion-independent and it provides a high precision rotational representation of body segments.

The 11 sensors are integrated in the suit and after the calibration they provide and capture information related to the XYZ axis rotations with the use of integrated gyroscopes, accelerometers and magnetometers. The posture of the upper-part of the potter's body can be derived by the data obtained from the suit but it is not the case with his position in 3D space. These data are recorded following a hierarchical structure and more precisely the Bounding Volume Hierarchy (BVH).

Since magnetometers are used among other sensors in the suit with the inertial sensors, the quality of data captured can be influenced by magnetic disturbances. During the first day of data acquisition with the potter B these disturbances were very strong since he was using an old model of wheel, containing many metallic devices. Despite the fact that data are online corrected by the system if weak magnetic disturbances are identified, the data acquired at the first day were of a very bad quality. For this reason another data acquisition session has been realized with the use of a more modern wheel with less magnetic disturbances.

The following table I, lists the different parts of potter's body, which motions have been captured for the preservation of his gestural know-how. Some of them may play a more important role in the technical gesture depending on the type of handicraft, but all the following body articulations are involved in the performance of the gesture of the craftsman. We are also aware about the important role of fingers in wheel-throwing process. Finger tracking constitutes an important step and we are currently working on the creation of a skeletal model trained on the potter's finger gestures.

POTTER'S GESTURE MODELLING WITH THE ARTORASIS SYSTEM

After the gesture capturing using the inertial sensors, the data is normalized in [-1, 1] using Euler angles, Quaternions and Directors Cosine Matrix as described in the methodology.

A prerequisite for the creation and the application of our methodology was to design the ArtOrasis system and interface (Figure 5). This gesture recognition system is entirely implemented in MaxMSP, an interactive programming environment that uses the Jitter toolbox and it aims at the recognition of technical gestures. ArtOrasis can also be used for capturing, modeling, and recognition. It also provides functionalities for the visualization of the skeleton of the craftsman.

The machine learning engine of the ArtOrasis is based on a hybrid Hidden Markov Model and Dynamic Time Warping approach, which is implemented into the Gesture Follower (GF) [START_REF] Bevilacqua | Wireless sensor interface and gesture-follower for music pedagogy[END_REF]] patch for MaxMSP (developed by the IMTR research team of IRCAM). According to the model defined previously the user (researcher, potter, learner) of ArtOrasis can define and choose which are the most important parts of the body that participate in the execution of a technical gesture and train the system based on ones. This stage corresponds to the machine learning phase of the methodology. The training of the gesture recognition system is also based on the effective gesture separation defined in second methodological step. After the training of the system the last step is the gesture recognition that is evaluated below.

EVALUATION

One of the final goals of our research is the design of a real-time pedagogical tool that can help transmission, and thus preservation of gestural know-how. To attain this goal, we need to compare gesture realization by an apprentice with the recorded and modeled gestures by experts. A pre-requisite before estimating such similarity, is the automated recognition by the system of what particular step the apprentice is trying to perform. For this reason we are convinced that online technical gesture recognition is essential for the comparison of handicraft skills between apprentices and expert. Furthermore, the segmentation of the data captured into a set of specific gestures, and the training of models, provides the data with a semantic dimension.

In order to validate our approach, and evaluate the recognition accuracy of the system for all the 1 , it has been asked to each of the expert potters to create five bowls. All the gestures * from the potter A and . from the potter B that are involved in all the four Capture, modeling and recognition of expert technical gestures in wheel-throwing art. • 9: 11 ACM Journal on Computing and Cultural Heritage, Special issue "Interacting with the past" phases 1 have been recorded in real conditions (co-articulated gestures and without rest). It has to be mentioned that very often, expert craftsmen are not available to create many copies of exactly the same object since this procedure is considered as a creative art process or because of ageing. Nevertheless, in case of the potter A the repeatability of his gestures can be considered as being of a high level, since he was very concentrated and careful in the way he performed the gestures. In case of the potter B the repeatability is of a medium level since he is easily disturbed in his everyday work by external elements (neighbors visiting his atelier, etc.)

The gesture recognition rates have been evaluated based on the « jackknife » method [START_REF] Abdi | Jackknife[END_REF]. In our case, jackknifing means estimation of the recognition accuracies for manually segmented gestures (isolated gestures) by using subsets of the available gestural data. The basic idea behind the jackknife variance estimator lies in systematically recomputing the statistic estimate leaving out one or more observations at a time from the sample set.

Practically, a dataset contains observations of all the * and . . In total, five observations for each gesture have been recorded and distinct databases for learning and test have been used in five iterations. For each iteration, one dataset is left out to be used as the learning database and train one model 6 per gesture until all the data sets are used once and the four remaining datasets are used as a database for testing. Two metrics have been used to evaluate the system: 1789:;:< = # <> ?7@8_B89<C :D:< ; # <> ?7@8_B89<C :D:< ; + # <> FGH;8_B89<C :D:< ; 1 B89GHH = # <> ?7@8_B89<C :D:< ; # <> ?7@8_B89<C :D:< ; + # <> 6:;;8I_B89<C :D:< ; 2

So, for the potter A, the first evaluation phase has been done using Euler angles after normalization for training the HMMs 6 * . For each of the eleven body segments, one Euler angle per axis has been computed. The table II shows the results of the five iterations of the jackknifing as well as the Precision and Recall per gesture of the potter A. Twelve queries for recognition per * have been asked to the 6 * . Both Precision and Recall were at 100%. The Quaternions and the Direction Cosine Matrix have also been used for the training of the M K models. For both Quaternions and Direction Cosine Matrix, all the observations : from + * and , * that have been given as query to ArtOrasis gave true recognized (Recall) but there are some cases where 6 + * and 6 , * gave maximum likelihood for false recognitions (Precision). In table III we can see that all three ways of the motion representation give excellent results for the recognition of all the effective gestures.

This first experimental case shows that, at least for the creation of simple objects in wheel throwing-art, online gesture recognition based on machine-learning can be successfully applied, and can therefore be used as a first step for "capturing of gestural skills" related to pottery. More precisely, the Direction Cosine Matrix needs the most computational power since a 3x3 matrix has to be calculated and sent as an input to the HMMs, which may be a very important constraint for real-time applications. Taking into consideration the fact that only the upper part of the potter's body contributes in a direct way to the creation of the object while he/she is seating on a chair in front of the wheel, we can conclude that the degrees of freedom of his/her body are really reduced. Additionally, DCM are widely used on animation but not for analysis, recognition or modeling of rotations. Quaternions different in terms of means and this fact contributes to the very high recognition rates. The results of the One-Way-ANOVA for the potter B are very similar to those of the potter A.

As it has been discussed before, the recognition is useful for the system in order to distinguish the gestures between them. But when the gesture is correctly recognized then it is important to have information about its temporal evolution (time index) compared to its model. This can be used not only to measure the different performances of the same potter, but also as a way to measure the distance between the performances between expert and learner in real-time. To do this, a temporal rescaling or time warping of the gesture can be directly obtained from our hybrid DTW-HMM approach. Time alignment experiments have been done for the right palms of the two potters. In figure 7, the time alignment of ) . for the potter B is shown. Normalized rotations of the first bowl for the right palm have been given to ArtOrasis as a learning sequence. Then, the normalized rotations of the second bowl have been given both as recognition and time alignment sequence. The results presented above show that for the ) . the aligned sequence fits well with the one that has been used for the learning of the HMMs for all three axis. Finally, from this alignment, it is possible to examine precisely where the main differences occur between the different sequences. . As previously mentioned, such difference could be displayed in real-time and offered thus feedback to the potter.

CONCLUSION

Conscious of the need for transmission of the ICH and for its preservation, we proposed a methodology for wheel-throwing gestural know-how preservation through capturing, modeling, online recognition and time alignment of different performances for the most fundamental gestures. In order to validate this methodology, the technological prototype ArtOrasis has been developed. It is able to capture rotations of body segments using inertial sensors and recognize the expert postures and gestures based on machine learning techniques. Both methodology and system have been evaluated on basic expert gestures for the creation of a bowl with the help of different gesture descriptors. Through experiments conducted on two different case studies of rare expertise, we show that gesture recognition with machine-learning can be successfully applied to the creation of simple objects in wheel-throwing pottery. This illustrates that technical gesture recognition can be used for modeling of gestural skills, which is a first required step for "capturing of ICH" in a form facilitating its transmission by interactive pedagogical tools. The high level of recall and precision of gestures recognition is justified by the fact that there are no equalities of variance but also by the expertise of the potters, who repeated the gestures in a very precise way.

The long-term goal of this research is the development of the appropriate methodology and technology for collecting, recording, classifying and modeling of hand gestures that constitute a rare know-how in various types of handicrafts. Since the role of finger touching on the material is very important, a future goal of this research is to extend the current methodology by combining it with computer vision in order to capture finger movements as well. To propose a completed methodology it would be also interesting to combine kinematic data (upper part of the body and fingers) with kinetic information about the pressure brought on the clay. But also the object detection and scene segmentation would give precious information about the evolution of the creation process and also about the progression of the gesture. The results of this study will not only contribute to the preservation of this gestural know-how but also to the development of a system aiming at the transmission. These results could be also used for renewal of ICH by proposing gestural metaphors for the creation of augmented musical performances based on handicraft gestures.

Fig. 1 .

 1 Fig. 1. Gesture recognition pipeline based on the Animazoo suit of wireless motion sensors for the upper-part of the potter's body.

  and recognition of expert technical gestures in wheel-throwing art. • 9: 5 ACM Journal on Computing and Cultural Heritage, Special issue "Interacting with the past"

  , A. Glushkova, F. Bevilacqua, F. Moutarde ACM Journal on Computing and Cultural Heritage, Special issue "Interacting with the past"

Fig 3 .

 3 Fig 3. Basic phases and gestures per potter for the creation of a bowl

  , A. Glushkova, F. Bevilacqua, F. Moutarde ACM Journal on Computing and Cultural Heritage, Special issue "Interacting with the past"

  Fig 4. (a) The potter A, Theodoros Galigalidis, pottery teacher at the "Therapy Center for Depending Individuals" in Thessaloniki, is performing the , (first configuration). (b) Skeleton reconstruction. (c) The potter is wearing the suit with the sensors for preliminary analysis.

  Fig 5. Screenshot of the interface of the ArtOrasis system presenting the learning and recognition phases In case of wheel throwing pottery 11 segments of the human skeleton listed in the table below have been selected and used for the training of ArtOrasis system. Concerning the different gestures separation, the training has been based on the 4 effective gestures identified during the second stage.According to the model defined previously the user (researcher, potter, learner) of ArtOrasis can define and choose which are the most important parts of the body that participate in the execution of a technical gesture and train the system based on ones. This stage corresponds to the machine learning phase of the methodology. The training of the gesture recognition system is also based on the effective gesture separation defined in second methodological step. After the training of the system the last step is the gesture recognition that is evaluated below.

  , A. Glushkova, F. Bevilacqua, F. Moutarde ACM Journal on Computing and Cultural Heritage, Special issue "Interacting with the past"

Fig. 7

 7 Fig.7 Example of aligned data from the right palm of the potter B for ) . (Opening the Bottom)

Table III .

 III Comparative table for Precision and Recall of Euler, Quaternions and Direction Cosine Matrix (DCM) representations

		Euler		Quaternions	DCM
		Precision	Recall	Precision	Recall	Precision	Recall
	) *	100%	100%	80%	94%	100%	85%
	+ *	100%	100%	100%	90%	87%	100%
	, *	100%	100%	100%	87%	95%	100%
	-*	100%	100%	85%	100%	100%	95%
	Total	100%	100%	91%	92%	96%	95%

Table II .

 II Precision and Recall per gesture from the potter A based on five iterations of jackknifing using Euler angles.

	Capture, modeling and recognition of expert technical gestures in wheel-throwing art. •	9: 15
			6 ) *	Maximum likelihoods ( LG ) 6 + * 6 , * 6 -*	Recall
		) *	20	-	-	-	100%
	Observa tions ( : *	* + , *	--	20 -	-20	--	100% 100%
		-*	-	-	-	20	100%
	Precision		100%	100%	100%	100%
		ACM Journal on Computing and Cultural Heritage, Special issue "Interacting with the past"

ACKNOWLEDGEMENTS

The research project is implemented within the framework of the Action «Supporting Postdoctoral Researchers» of the Operational Program "Education and Lifelong Learning" (Action's Beneficiary: General Secretariat for Research and Technology), and is co-financed by the European Social Fund (ESF) and the Greek State.

9: 13

ACM Journal on Computing and Cultural Heritage, Special issue "Interacting with the past" impose the non-Euclidean space, which cannot be easily interpreted by humans and consequently they cannot be meaningful for learning purposes.

With regards to the potter B, we have also evaluated the recognition accuracy based on jackknife method. In the table IV, the precision and recall for his . are presented. During this test we use Euler angles since they have been previously identified as the most relevant descriptor. Like in the previous example 20 queries for recognition per K have been asked to the 6 . . The precision and recall are perfect for )

. to - . . For / . , there is one false recognition since - . and / . are very similar. The difference between - . and / . is that, in / . the potter B defines the shape with a tool and a sponge but in - . he defines the shape without any tool, just with his hands. Gesture G 0 has the lowest recognition rate because the potter was very disturbed. The repeatability of this gesture is low and it has a direct impact on its recognition rate. Even if the number of . is increased compared to * , the Precision and Recall are still excellent.

The possibility to do cross-potters evaluation has been rejected since it is not meaningful to compare discrete gestural skills of a rare expertise, which are strongly related with the local identity of each expert.

The recognition rate is very high but is effective after a latency of 80-120 frames (1.5 to 2 seconds). This latency corresponds to the time that is needed to separate the different HMM models based on their associated maximum likelihood. Co-articulation has an impact to the computation of the instant likelihood and this effect usually becomes more important during the transitions between gestures. It lasts short time periods and the gestures are not clearly recognized during these periods. More precisely, co-articulation can be defined as the fusion of distinct actions into larger and holistically perceived chunks [START_REF] Hardcastle | Coarticulation: theory, data and techniques[END_REF].

On the left of figure 6 for example, + * is given to ArtOrasis. At the beginning, there is a maximum likelihood alternation effect between the 6 * . After the first 1.5 seconds,

Obser vatio ns

) .

20

-----100%

+ .

-20 ----100%

, .

--20 ---100%

-.

---20 --100%

/ .

- 6 + * has the maximum likelihood until the end of the observations. As we can see, the likelihood of 6 + * is very high comparing to other models and this can be justified by the fact that the expert repeats the gestures in a very way. On the first figure a) of the figure 6, there is still a latency of about 1.5 second before 6 , * becomes the maximum likelihood. Then, this effect is normalized and almost instantly the 6 , gives the maximum likelihood for about 1040 frames. Just after the frame 1121, the maximum likelihood starts to alternate between 6 + * and 6 , * due to coarticulation. In + * (the raise), the potter needs to clean his hand before to take a small tool and starts the first configuration of the shape. During the co-articulation phase, the right hand goes under the left and vice-versa, which is a common phase with the gesture + * also.

In parallel, we applied a Levene's test in order to detect the equality of variances between the Euler angles for the potter A. According to this test, the variances of the four gestures in three axis are not equal. By applying the One-Way-ANOVA test, we observe that the mean values for all the four gestures are not equal on the axis X by comparing pairs of gestures. Also, equalities are extracted between