Kuniharu Sakurada
email: kh.sakurada@keio.jp

Gowrishankar Ganesh

Wenwei Yu

Gowrishankar Ganesh
email: ganesh.gowrishankar@lirmm.fr

A novel tactile feedback system with on-line texture decoding and direct-texture-feedback

Tactile perception on our fingers is a key sensory feedback that enables us to perceive and explore our world using our hands as probes, and is essential for efficient gripping and manipulation of objects. A tactile feedback system can therefore greatly improve the quality of life of individuals with partial or complete sensory loss like during stroke, or with artificial limbs after an amputation. However, most existing tactile texture feedback technologies suffer from two constraints. First, texture decoding and texture feedback have been traditionally examined separately and not as parts of the same problem, and second, texture information has been popularly fed back using sensory modality other than tactile itself. In this study, we propose a prototype on-line direct-texture decoding and feedback system in which the texture touched by a user is decoded using an accelerometer attached to the finger. The feedback is realized by rubbing the user's skin instantaneously with the actual material that, and at the speed at which, the user swipes. The efficacy of the proposed system was tested in two user experiments with five test materials. The results and the corresponding hints for future improvements are discussed.

I. INTRODUCTION

Tactile sensory information is fundamental to our perception of our environment, and is a major determinant of our hand functions [START_REF] Carey | Sensory loss in stroke patients: Effective training of tactile and proprioceptive discrimination[END_REF], [START_REF] Ekstrand | Test-retest reliability of the Shape/Texture Identification testTM in people with chronic stroke[END_REF], including manipulation and grasping. Touch and tactile feedback are also crucial during our interaction with other humans individuals [START_REF] Takagi | Physically interacting individuals estimate the partner's goal to enhance their movements[END_REF], [START_REF] Ganesh | Two is better than one: Physical interactions improve motor performance in humans[END_REF]. Tactile feedback training and restoration are therefore important for individuals who have suffered from a partial or complete tactile feedback, such as stroke patients, and amputees [START_REF] Jiang | Using haptic feedback to improve grasp force control in multiple sclerosis patients[END_REF], [START_REF] Kim | Haptic feedback enhances grip force control of sEMG-controlled prosthetic hands in targeted reinnervation amputees[END_REF]. Upper limb amputees usually compensate the absence of tactile feedback with vision, but vision-dependent task execution causes excessive cognitive load on individuals [START_REF] Davoodi | Development of a physics-based target shooting game to train amputee users of multijoint upper limb prostheses[END_REF]. On the other hand, substituting stimuli for tactile feedback is known to help the recovery of original tactile functions that have been impaired or lost [START_REF] Kita | A pilot study of sensory feedback by transcutaneous electrical nerve stimulation to improve manipulation deficit caused by severe sensory loss after stroke[END_REF], [START_REF] Kim | An engineered tactile afferent modulation platform to elicit compound sensory nerve action potentials in response to force magnitude[END_REF].

Existing tactile texture feedback technologies, such as [START_REF] Lin | A natural user interface for realistic tactile perception of object surface texture[END_REF], suffer from several limitations. First, cost and applicability to human subjects remain the key hurdles for decoding Fig. 1.

Proposed tactile feedback system. The system consists of an accelerometer mounted on a fingertip. The features extracted from the accelerometer recordings were used to decode touched material using a linear sparse decoder. The decoded material was then presented to the user on the forearm of their left hand using the prototype direct-texture-feedback device.

texture information in scenarios of dynamic touch [START_REF] Rana | An Improved Soft Dielectric for a Highly Sensitive Capacitive Tactile Sensor[END_REF], [START_REF] Jimenez | Evaluation of force, vibration and thermal tactile feedback in prosthetic limbs[END_REF]. Second, most studies on texture decoding have been performed in 'passive' experiments and dynamical texture explorative motions have hardly been studied in regard to texture feedback [START_REF] Galambos | Vibrotactile force feedback for telemanipulation: Concept and applications[END_REF]. It is however known that passive external stimuli do not induce the natural tactile sensation [START_REF] Iwamura | Postcentral neurons in hand region of area 2: their possible role in the form discrimination of tactile objects[END_REF]. Finally, almost all previous decoding studies have studied decoding independently from the actual feedback stimulation system it drives.

Vibratory stimulation has been the most popular substitutive feedback utilized for texture feedback due to the convenience of attaching a vibrator onto a subject's skin [START_REF] Motamedi | The Use of Vibrotactile Feedback to Restore Texture Recognition Capabilities, and the Effect of Subject Training[END_REF], [START_REF] Lin | A natural user interface for realistic tactile perception of object surface texture[END_REF]. However, adapting to a vibratory stimulation requires an user to learn new associations between vibratory parameters, such as frequency or intensity, and texture patterns.

In this study, we propose an on-line direct-texturefeedback system [START_REF] Whitmire | Haptic revolver: Touch, shear, texture, and shape rendering on a reconfgurable virtual reality controller[END_REF] that addresses the above issues. In line with previous studies that used vibration sensors for texture recognition [START_REF] Natsume | Skin-propagated vibration for roughness and textures[END_REF], [START_REF] Ding | Tactile perception of roughness and hardness to discriminate materials by friction-induced vibration[END_REF], [START_REF] Fagiani | Tactile perception by friction induced vibrations[END_REF], [START_REF] Romano | Creating realistic virtual textures from contact acceleration data[END_REF], [START_REF] Bensmaia | The vibrations of texture[END_REF]), our system uses a accelerometer based texture decoding system, but crucially we integrate it with a new 'direct-texture-feedback' device. The system enables on-line decoding of a texture touched by a user's finger or prosthetic limb, and provide feedback by choosing and presenting the actual material decoded, on another limb. The feedback device ensures material feedback at the speed at which a user swipes his finger, a feature that is known to improve texture perception [START_REF] Löken | Coding of pleasant touch by unmyelinated afferents in humans[END_REF].

In the following sections we describe our prototype system in detail. This is followed by the description of the two user studies to evaluate the performance of the decoding system as well as the feedback device. Finally, we discuss the results, and what we learned for the improvement of future devices. We also discuss the reason why we think directtexture decoding and feedback may be better than decoding and feedback of general texture characteristics like hardness or roughness. II. SYSTEM SETUP Our tactile feedback system consists of two parts, an accelerometer based decoding system, and a direct-texturefeedback system (Fig. 1). We describe each part in detail below.

A. Decoding system

The decoding system is an updated version of the method proposed in our previous study [START_REF] Sakurada | Accurate Decoding of Material Textures Using a finger Mounted Accelerometer[END_REF] using a finger or tool (simulating a prosthetic finger) mounted accelorometer and uses a sparse linear classifier [START_REF] Yamamoto | Referral of tactile sensation to the tips of L-shaped sticks[END_REF] on the acceleromoter signals to decode/classify the the material the subject touches.

Followings are specific improvements incorporated in the current decoder, relative to the method from [START_REF] Sakurada | Accurate Decoding of Material Textures Using a finger Mounted Accelerometer[END_REF]:

a. Addition of a swiping motion detection for sampling only during explorative motions; b. Addition of a swipe speed estimator utilizing the the accelerometer signals; c. Utilization of new features in frequency domain, and estimated velocity and force of the active explorative motions, to improve decoding accuracy with fewer training samples, and over a larger range of swiping velocity. 1) Swiping motion detection: The data for decoding was extracted only for the period where we detected a 'finger swipe' (motion accompanied by touch). The swipe was detected using a threshold on the average acceleration change as:

m-1 ∑ i=1 |a n(i+1) -a n(i) |/m > T, (1)
where a n = a 2 x + a 2 y + a 2 z , m = 200 and threshold T was equal to 10 m/sec 2 . a x , a y , and a z are x, y , z component of the acceleration, respectively.

2) Swipe speed estimation: The swiping velocity v at any time instant t was estimated from the accelerometer as follows :

v(t) = l ∑ t-1 i=t-p a x (i) p ( 2
)
where l is the frame length in msec, p = 200/l, and the v is calculated with a length of 200 msec at a window shift of 50 msec.

3) Decoding features: Our previous work utilized only frequency based features [START_REF] Sakurada | Accurate Decoding of Material Textures Using a finger Mounted Accelerometer[END_REF]. In this study we were able to improve decoder performance by including frequency and time domain features, as well as the information about the swipe velocity and swipe force. To calculate the frequency features, we considered both, the accelerations in the three axis as well as the three principle components of the accelerations in the three axis. From this we calculated | Ãx ( f )|, | Ãy ( f )|, and | Ãz ( f )| are x, y, and z components of magnitude in frequency-domain after the Discrete Fourier Transform (DFT). This was used to calculate the 'vibration intensity' V and the 'center frequency f c ' [START_REF] Natsume | Skin-propagated vibration for roughness and textures[END_REF] :

V = 1000 ∑ f =100 log 10 | Ã( f )|, (3)
f c = 1000 ∑ f =100 f • | Ã( f )| 1000 ∑ f =100 | Ã( f )| . ( 4
)
Each of these frequency-domain feature vectors was extracted from between a 100 to 1000 Hz frequency band, that is believed to include important signals for texture decoding. We calculated the vibration intensity and center frequency for the three accelerations as well as the three principle components.

We also calculated the equivalent DFT321 time signal [START_REF] Romano | Creating realistic virtual textures from contact acceleration data[END_REF] by performing the inverse Fourier transform of the frequency-domain magnitude of DFT321, where the DFT321 parameters | Ãs ( f )| and phase θ ( f ) are defined as follows [START_REF] Romano | Creating realistic virtual textures from contact acceleration data[END_REF]:

| Ãs ( f )| = | Ãx ( f )| 2 + | Ãy ( f )| 2 + | Ãz ( f )| 2 , (5)
θ ( f ) = tan -1 Im(| Ãx ( f )| + | Ãy ( f )| + | Ãz ( f )|) Re(| Ãx ( f )| + | Ãy ( f )| + | Ãz ( f )|) , (6)
As the time domain features, we used the root mean square and the average of the first order difference of the total acceleration a n and the equivalent DFT321 time signal.

To summarize, for the decoding we utilized 18 features-

• (vibration intensity V and center frequency f c ) for the three components of acceleration and three principle components making 12 frequency features in total. • The RMS (Root Mean Square) and average values of the total acceleration and DFT321 time signal, making four time features in total. • the estimated swipe speed.

• the swipe force. Each of these features were collected in 'samples' consisting of periods of 200 msec collected over windows shifted by 50 msec. These features were ed through a sparse linear classifier to decode the swiped material [START_REF] Yamamoto | Referral of tactile sensation to the tips of L-shaped sticks[END_REF]. Our prototype device presents one of five material to the user. Each test material was fixed to the material belt with a tape. The material belts were attached to two pulleys and a DC motor was directly connected to the rotating shaft. A solenoid below each belt can be controlled to choose and lift one material to make contact with the user's skin. The speed and direction of the material contact was regulated by the DC motor to be equal to the swiping velocity of the user (calculated again from the accelerometer signals).

B. Direct-texture-feedback device

The second key contribution of this work is the development of a prototype low cost feedback device. This device was designed with the aim of presenting to the users, the actual materials that they touch with their pathological finger, or prosthetic finger (and which we recognize using the decoder). This direct-texture-feedback is presented on a different healthy part of the user's body.

In this first prototype, we developed a 3d printed feedback device that is able to present one of five materials to the user by 'rubbing' them against the users skin on their forearm. The device uses a single motor and can adjust the speed of swipe on-line so as to match the velocity of their finger swiping the actual material (as recognized by our speed estimation), providing a sense of agency to the feedback.

The feedback device is shown in Fig. 2. The current prototype has medium dimensions (height×width×depth, 81.4×139.5×147.0 mm) and is designed for feedback on the forearm of the user. The device consists of main rotating shaft with pulleys (φ 22 mm). The main shaft is not actuated.

A second rotating shaft is actuated by a DC motor (TG-87A-GU-85.1-KA, TSUKASA ELECTRIC CO.,LTD.). Five belts made of different materials can be mounted side by side across the shafts, on the pulleys as shown in Fig. 2. Each belt measure 10 mm in width. Our device consists of five solenoid switches (CBS08300480, TAKAHA KIKO Co., Ltd.) placed between the shaft and the pulley, one each below a belt. The solenoids can be activated independently in order to push a belt up and rub against the skin of the subject. The solenoid is able to push the belt against the skin with a normal force of about 1.5N. The rated rotation velocity of the DC motor is 135.8 rpm, and is able to rotate the material belt at velocities between 1 to 10 cm/sec, velocities that have been shown to elicit comfortable tactile perception in a previous study [START_REF] Löken | Coding of pleasant touch by unmyelinated afferents in humans[END_REF].

III. VERIFICATION EXPERIMENTS

We conducted two experiments with participants to evaluate our device. The first experiment verified the scope of decoding performance of our updated decoder, to see what is the best performance one may expect from it. For this, we analyzed the decoder in a controlled training and testing scenario that enabled us to control, as well as measure, the speed and load of swipes. We tested the decoder for both the cases, when the swipe was made with the user's finger, and when the finger was artificial (and swiped by a user holding it).

Next, in the second experiment, we tested the tactile feedback system as a whole in the realistic user scenario in which a user swipes a material at his/her own comfortable speed, the material is decoded on-line during the touch, and the decoded results are fed back using our direct-texturefeedback device.

A. Experiment 1: Verification of decoding performance a) Participants: Four healthy subjects (three males, one female, 22.8±1.0 years old) participated in this experiment. The participants received explanation about all tasks before the experiments.

b) Experiment setup: The participants sat on a chair in front of a table. They were presented with one of five test materials (cork, denim, cotton, leather and paper) which they touched with their index finger (Fig. 3). A load sensor placed below the material allowed us to evaluate the force applied by the finger on the drum, that was read in through an Arduino microcomputer and then transmitting it to the PC through serial communication. A six-axis accelerometer (MPM6-06/2000C, Micro-Stone Corporation) was mounted on the finger tip of a participant. The accelerometer signals were read in using a multi-function DAQ device (USB-6218, National Instrument). We also did sessions where the accelerometer was mounted on a 3d-printed plastic finger, that a participant held in his hand and touched the test material-this simulated the prosthetic finger. The photo shows the material plate (with a load cell below it) that the participants swiped either with their finger or while holding an artificial finger (see insets). They were presented with their swipe load feedback on a computer screen placed on the table in front of them.

c) Experiment procedure: The test materials were presented one at a time and the participant was asked to rhythmically swipe in a distal to proximal direction guided by rhythmic audio cues that were presented to them. The load applied to the test material was visually fed back to the participant on the monitor and they were asked to maintain the load near the target load displayed on the same monitor.

All measurements were done over a total of 30 conditions consisting of three target trial velocities (10, 20, 30 cm/sec), two target loads (0.49, 0.98 N), and five test materials. The accelerometer and load cell sampling rates were set at 4 kHz and 20 Hz, respectively.

The 30 conditions were repeated for the case when the same participant swiped the materials with the artificial finger.

d) Data pre-processing and decoder training: The accelerometer signal was Butterworth high pass filtered over 100 Hz before the features were evaluated. The swipe speed was Butterworth band passed between 0.125 Hz and 5 Hz.

For each touch trial of a particular load and swipe speed, we collected 150 'training samples', that consisted of features (described in Sec.II-A.3) extracted from 200 msec windows shifted by 50 msec. In total, in a total of 30 conditions (five materials x three swipe speeds x two swipe loads), we therefore recorded 4,500 samples. A decoder was constructed for each participant and trained with a total of 1500 samples, including 50 samples collected randomly from each swipe condition.

e) Decoder test results: The decoder for each participant was tested on 50 samples (that were independent of the training samples) collected randomly from all the conditions. We constructed five one-vs.-rest decoders (ex. cork-vs.-rest) from the data and the classification results of the decoder with the highest accuracy among the five provided us with the material decoding. This methodology was shown to be efficient in [START_REF] Sakurada | Accurate Decoding of Material Textures Using a finger Mounted Accelerometer[END_REF]. We performed this procedure 20 times to get the average performance for every participant. The across participant mean and standard deviation is shown as a confusion matrix in The same results as a) when the swipe load features were omitted. We ound that the swipe load features do not influence the decoding accuracy significantly test material (columns) was classified as (rows). Fig. 4a also shows the confusion matrix in the conditions when the swipe was done using the artificial finger. It was possible for the decoders to achieve decoding accuracies of more than 90% both, when the materials were touched with a participant's finger, and with an artificial finger.

Considering realistic scenarios (like the one we will test in Experiment 2), we also analyzed the decoding accuracy in the absence of a load sensor, hence omitting the swipe load as a feature (Fig. 4b). We observed that even omitting the swipe load features, the decoder was able to achieve accuracies close to that with the load features. We therefore chose not use the load features in our next experiment.

B. Experiment2: Verification of the decoder-direct-texture-Feedback system

A texture identification experiment was conducted to show the efficacy of our direct-texture-feedback system. Specifically, considering a realistic application of the device for amputees, we tested, first, the effectiveness of the on-line implementation of our decoder, and second, the ability of users to identify the materials presented by our direct-texturefeedback device. In order for the participants to not identify the presented material directly from their swiping finger, we confined this experiment to swipes with the prosthetic finger (held by the participants in their hand). A foam padding on the prosthetic finger further ensured that the participants were b) Experiment setup: During the experiment, participants held the prosthetic finger in their right hand, and sat in a chair in front of the monitor. They operated a 2-button button pad with their left hand. The subject was asked to swipe the tip of the artificial finger onto the test material surface (200×150 mm) set in front of the right hand. The texture feedback device was placed below the left forearm of the participant (Fig. 5). The subject could adjust the position of their forearm during the experiment so as to optimize the perception of direct-texture-feedback.

Both the test material and the right hand were occluded from the participant's vision by a partition board. Furthermore, the subjects were blind folded with a piece of cloth. To avoid that the subjects identify the touched material from audio cues, they were asked to wear headphones that played white noise. c) Experiment procedure & On-line strategy: The subject was asked to swipe the test material rhythmically in a distal to proximal direction with the tip of the artificial finger at their 'preferred speed'. The feature vectors were extracted during the swipes in the same way as Experiment 1. The decoders were trained with 100 extracted samples.

The participants were presented with one randomly selected test material at a time. They swiped the material using the same sponge covered artificial finger. The online decoding was conducted by features extracted during the swipes, and the decoded material was presented to the participants using the direct-texture-feedback device. The participants were then presented with the name of materials one at a time on a computer screen in front to them. They used the button pad to answer if the presented material under their forearm and the screen were the same, or not. The materials were fed back by the direct-texture-feedback device in one of three speeds in random order-at the speed of the swipe ('normal'), at 67% of the speed of their swipe ('slow') and at 150 % of the swipe speed ('fast').

d) Data analysis and results: The across participant means and standard deviations of the accuracy of the online decoder is shown in Fig. 6. Fig. 7 plots the material identification results at the different presentation speeds. Note that the identification results are calculated relative to the material presented to the participants, irrespective of the material actually touched by the participants.

We also analyzed the identification results in a two-way ANOVA, which evaluated two factors: the test materials (five materials, d f = 4) and the speed of swipe (Slow vs. Normal vs. Fast, d f = 2) and their interaction. We found that the materials did not affect the decoding accuracy (F(4,72) = 0.55, p = 0.69), but the accuracies changed depending on the speed of presentation (F(2,72) = 4.72, p = 0.02) and there was no interaction (F(8,72) = 2.04, p = 0.06). Across materials, post-hoc analysis showed that a significant difference was only observed between the fast and normal presentation (T(9) = 2.32, p = 0.045), but it did not survive multiple correction. These results suggest that a faster material presentation using the direct-texture-feedback device was better for identification, but further studies are required to verify this point.

IV. DISCUSSION

A. Summary

In this study we developed a prototype texture feedback system including a on-line texture decoder and a directtexture-feedback device. The texture decoding in this system is achieved using a finger mounted accelerometer. We considered two cases-when the texture is swiped by a human finger (i.e when the device us used by a individual with sensory deficits), and when the texture is swiped by a artifical finger (i.e when the device is used by a amputee). A machine learning algorithm was trained to recognize the texture from the vibration signals recorded by the accelerometer when the fingers swiped a surface.

B. Improving the on-line decoder

We tested the decoder with five common materials and observed that the decoder could give excellent decoding accuracies of around 95% correct recognition in an off-line decoding experiment (Experiment 1). In the on-line decoding (Experiment-2) however, this accuracy decreased to around 77 % (Fig. 6). This is a significant decrease relative to the Experiment 1. We analyzed the reason for this and found that it was due to our flawed training methodology in Experiment 2. Unlike Experiment 1, in Experiment 2 we did not require the participants to swipe at different speeds during training, but let them swipe at their 'preferred speed'. We later found that, in the presence additional texture identification task, the variations in the participant speed was much higher, and they also consistently used higher swipe speeds during identification (Training: 4.39±3.09 cm/sec, Identification: 13.0±8.86 cm/sec). In other words, their preferred speeds was very different in the decoder training and testing (identification task) phase, leading to attenuated decoder performance. We expect that training the decoder over a range of speeds (like in Experiment-1 should solve this problem and bring the recognition rates to the values achieved in Experiment 1.

C. Lessons for direct-texture-feedback

Once the texture being touched is decoded, the next step is to feedback the decoded texture to the user. Texture feedback is a non-trivial problem because the tactile perception of a texture is determined by not just the friction and the resulting vibrations induced by it, but also the modulated by the temperature of the material and the surface area of contact (for example a touched metallic surface makes contact only at the base of finger, while a fur coat will we make contact with not just the base but also the sides of a finger [START_REF] Fagiani | Tactile perception by friction induced vibrations[END_REF]). Hence many studies have opted to feedback just vibrations, assuming the user would learn the mapping between these and the material over time [START_REF] Motamedi | The Use of Vibrotactile Feedback to Restore Texture Recognition Capabilities, and the Effect of Subject Training[END_REF], [START_REF] Lin | A natural user interface for realistic tactile perception of object surface texture[END_REF]. Here we therefore opted for presenting directly the touched material (that we decode) as the feedback. In fact as the reader would have noticed, even our decoder is designed for a direct-texture-feedback device-we decode for specific material rather than some general property (like hardness, roughness, stickiness etc) of materials.

This option ofcourse ensures that the touch perception of the material (friction, temperature, contact area etc) are maintained during feedback, but on the other hand this means that we need to have all the material that a user may touch, 'in hand' and available for feedback. This seems like a impossible problem at first glance, but in fact the common material that we encounter in our daily life are not as many as one would imagine, especially if we group them into some general categories like metals, wood, cloth as so on.

In fact the relatively low identification rate in Experiment 2 (Fig. 7) showed that such catogorization may be in fact benefit texture feedback. We observed in Experiment 2 that users had difficulty distinguishing the materials presented to them, even when they knew that it was one of the five they had been shown before. This difficulty was not due to our choice of the feedback location. While in Experiment 2 we presented the feedback on their forearm, in preliminary studies, we found that users found identification difficult even when the feedback was provided near their shoulder, thigh or calf,or even the finger of their other hand (though performance in this case was improved a bit). This is maybe due to the fact that visual feedback is a crucial contributor for the identification of materials [START_REF] Lederman | Texture perception: studies of intersensory organization using a discrepancy paradigm, and visual versus tactual psychophysics[END_REF], [START_REF] Kitahara | Sensory properties in fusion of visual/haptic stimuli using mixed reality[END_REF]. However, in Fig. 7, if we see the results of the Fast feedback (third panel) during which identification performance was best, we do see that users tend to mistake between certain material pairs more often. For example,denim is often mistaken as cork and vice-versa, and paper and leather are often mistaken as one another. The reason why these materials are perceived similar is unclear and needs further studies. However, these results suggest that feedback identification can be increased if the feedback is not of every material, but rather of material categories defined by the identification process. Such identification based categorization may also further help improve material decoder. We plan to test these issues in the next version of the device.

We conclude by noting that, in agreement with previous studies [START_REF] Löken | Coding of pleasant touch by unmyelinated afferents in humans[END_REF], we observed that the speed of feedback critically affected texture identification. First, the users reported that the correspondence between the material swipes and the feedback (enabled by our on-line velocity estimation) provided a feel of 'agency', making the feedback feel natural. Second, we found that the identification accuracy changed with the speed of feedback (Fig. 7). Interestingly however, we found that the best accuracies were observed with a feedback that was 1.5 times faster than the user swipes. Further studies are required to understand the reason behind this and consequently, optimize the feedback speed.

Fig. 2 .

 2 Fig.2. The direct-texture-feedback device. Our prototype device presents one of five material to the user. Each test material was fixed to the material belt with a tape. The material belts were attached to two pulleys and a DC motor was directly connected to the rotating shaft. A solenoid below each belt can be controlled to choose and lift one material to make contact with the user's skin. The speed and direction of the material contact was regulated by the DC motor to be equal to the swiping velocity of the user (calculated again from the accelerometer signals).

Fig. 3 .

 3 Fig.3. Setup of Experiment 1. The photo shows the material plate (with a load cell below it) that the participants swiped either with their finger or while holding an artificial finger (see insets). They were presented with their swipe load feedback on a computer screen placed on the table in front of them.

Fig. 4 .

 4 Fig. 4. Experiment 1, decoding accuracy. (a) The 'confusion matrix' presents the % mean and standard deviation decoding accuracy across participants-that is, what each presented test material (columns) was classified as (rows). The high diagonal values shows that the materials were decoded correctly for most participants. (b) The same results as a) when the swipe load features were omitted. We ound that the swipe load features do not influence the decoding accuracy significantly

Fig. 5 .

 5 Fig. 5. Setup of Experiment 2. The test material was presented to the right hand of the participants while the feedback device was placed under the left forearm. The participants swiped the presented material with the artificial finger held in their right hand. A sponge surrounding the artificial finger prevented the participants from feeling the vibrations generated by the swipes. A board prevented the participants from seeing their right hand, while a piece of cloth covered the feedback device.

Fig. 6 .

 6 Fig. 6. Experiment 2, accuracy of the on-line decoder. The confusion matrix of the across participant % mean and standard deviation of the decoding accuracy for each presented material. The actual materials are shown along the columns, while the decoder classified material are shown along the rows.

Fig. 7 .

 7 Fig.7. Experiment 2, feedback perception by participants. The participants were presented with each test material in a random order which they swiped at their own preferred speed. The direct-texture-feedback device then presented the decoded material in one of three speeds-normal: at the speed of their swipe, slow: 67% of the swipe speed, and fast: 150% of the swipe speed. The confusion matrices show the (across participant) % mean and standard deviation of the materials identified by the participants (rows) corresponding to the materials presented to them (columns).

ACKNOWLEDGMENT KS and GG were partially supported by the JST ERATO Grant Number JPMJER1701, Japan.

* This work was supported by JSPS KAKENHI Grant Number 17H02129 to Wenwei YU.


Claude Feldman

Pambou Nziengui1

Rostand Moutou Pitti1

Joseph Gril1

Éric Fournely1

Analytical Assessment Of Creep Behaviour Of European Species In Outdoor Conditions

Keywords: Creep test, Notched beam, Moisture content effects, Beam theory, Timber

WF

Abies Alba Mil, White Fir DFi (WFi) Beam number i of species DF (WF) 𝑘

CONTEXT AND PROBLEMATIC

Wood is defined as a material whose the mechanical behavior in structure depending on the fluctuation of its internal moisture content [START_REF] Dubois | Modeling of the Viscoelastic Mechano-Sorptive Behavior in Wood[END_REF][START_REF] Angellier | Influence of Hygrothermal Effects in the Fracture Process in Wood under Creep Loading[END_REF]. It is also known that the internal changes of humidity, coupling with long term loading bring in many times, the initiation and the propagation of cracks, which are directly an impact on the total deflection measured on the wood until its collapse [START_REF] Tran | Reliability Assessment and Updating of Notched Timber Components Subjected to Environmental and Mechanical Loading[END_REF][START_REF] Nziengui | Notched-Beam Creep of Douglas Fir and White Fir in Outdoor Conditions: Experimental Study[END_REF]. These cracks are amplified by the geometric singularities available on the wood and are at the origin of the total collapse of the structure. In this case, a specific experimental device has been built to assess the effects of moisture content in the mechanical behavior of wood [START_REF] Nziengui | Notched-Beam Creep of Douglas Fir and White Fir in Outdoor Conditions: Experimental Study[END_REF]. The device takes, also into account, the effects of long term loading and the crack propagations. In the base of the results obtained, an analytical model of the evolution of the creep behavior of wood in outdoor conditions are proposed herein.

ANALYTICAL MODELING OF NOTCHED BEAM DEFLECTION IN 4-POINTS BENDING TEST

Modeling Of The Compliance Of Notched Beam

The experimental campaign of creep loading is composed of two types of European species (Pseudotsuga Menziesii and Abies Alba Mil) tested as presented by [START_REF] Nziengui | Notched-Beam Creep of Douglas Fir and White Fir in Outdoor Conditions: Experimental Study[END_REF]. During the experimental campaign the deflection of the notched beam, measured by LVDT sensor placed under the center of the beam, is considered as the principal parameter followed (Figure 1a). In this case we can assume that the evolution of the deflection takes into account the effects of loading, crack opening and crack initiations of the notched beam. Figure 1 shows the experimental setup of the creep loading. From this experimental setup (Figure 1), an analytical model of the evolution of the beam deflection is proposed. we can assume, by taking into account the general assumptions of the beam theory especially the symmetry of the system, that the study can be restricted only on the half notched beam (HNB). Then, by considered the flexural moment MZ, the moment of inertia IZ, the modulus of elasticity of the notched beam EN, the crack propagation (appeared at the corner of the beam) and based on the strength of materials calculus the deflection of measured by the LVDT sensor can takes the following expression:

𝑦 𝐺 = ( 23 36 ) × ( 𝑎𝐿 2 𝑒𝐻 3 ) × (1 + 189 23 𝛼 3 ) × [1 + 108 115 . ( 𝐻 𝐿 ) 2 . (𝑘). 1 + ( 3 2 )𝛼 1 + ( 189 23 )𝛼 3 ] × ( 𝐹 0 𝐸 𝑁 ) (1)
Where yG= yM+yV represents the general deflection measured by the LVDT sensor for a loading F0 applied on the notched beam during the creep test (yM deflection brought by the flexural moment, yG deflection briought by the shear force V). k represents the constant ratio of shear with k=(E/G)=17 (E and G, respectively, the module of elasticity and the shear modulus of the notched beam gives by the predictive law of [START_REF] Guitard | Mécanique Du Matériau Bois et Com-Posites[END_REF]. 𝛼 represents the crack level propagation appeared at the corner of notched beam. By taking into account the expression shows by (1) the compliance in the time of the notched beam can take the simplified following expression:

𝐽 𝑁 (𝛼, 𝑡) = 𝑦 𝐺 (𝑡) 𝐴 × 𝐹 0 × 𝑓(𝛼, 𝑘) (2)
Where 𝐽 𝑁 representing the compliance of NB at the time 𝑡 for a crack appeared at the corner of the beam. This expression of the compliance 𝐽 𝑁 take into account, only, the effects of mechanical behavior of beam.

Taking into account of the moisture content (w) effect

In outdoor conditions wood is submitting to the effects of moisture content, in this case and by taking into account this hypothesis, it seems necessary to bring a correction on the compliance given by equation [START_REF] Angellier | Influence of Hygrothermal Effects in the Fracture Process in Wood under Creep Loading[END_REF]. By starting from the Fick law, for an unidirectional diffusion, a correction is done on the expression of moisture content w of the NB tested. Then, the hydric displacement (Uh) brings by the hydric phenomenon takes the following expression:

𝑈 ℎ = 𝛽 • ( 𝐻 2 ) • [𝑤(%) -𝑤 𝑖 (%)] (3)
Where, 𝛽 is the shrinkage-swelling coefficient along the considered axis.

Figure 2 presents a comparison done between the evolutions of the hydric displacement measured and the hydric displacement calculated. The results show that there is a good fit between the data taken from the experimental campaign and those taken from the predictive model proposed. On the base of this result and by taking into account the superposition theorem, the compliance of the NB can take definitively the following expression:

𝐽 𝑁 (𝛼, 𝑡) = 𝑦 𝐺 (𝑡) -𝑈 ℎ (𝑡) 𝐴 • 𝐹 0 • 𝑓(𝛼, 𝑘) (4)

RESULTS AND DISCUSSIONS

Figure 3 shows the relationship between the compliance of the NB of the specimens DF9 and WF9, loaded in 4point bending test in an uncontrolled environment with the variation of the internal moisture content (w) measured on the matched beams. The literature [START_REF] Grossman | Use of Leicester's 'Rheological Model for Mechano-Sorptive Deflections of Beams[END_REF][START_REF] Hunt | The Mechano-Sorptive Creep Susceptibility of Two Softwoods and Its Relation to Some Other Materials Properties[END_REF][START_REF] Montero | Infl Uence of Hygromechanical History on the Longitudinal Mechanosorptive Creep of Wood[END_REF] shows that there are three distinct observable behaviors: (i) the first relates to increasing compliance for any reduction of the moisture content of the tested wood. In present study for example and despite the fact the studies are done in the uncontrolled environment, the same conclusions can be highlighted. Indeed, on the paths JN (DF9) (Figure 3) the drying phases (-, drying) are followed by an increase in compliance (section bc for example in path JN (DF9)). (ii) the second behavior shows that during a humidification (++) greater than the first (see path JN (DF9)), there is a consequent increase in compliance. (iii) the last behavior (the most complex to observe) is that for humidification below a level already reached, compliance decreases (see section cd Figure 3).

CONCLUSIONS

In this paper, by seeing the results obtained, especially with the comparison of the evolution of the experimental data and the model, we can remark that the proposed model describes satisfactorily the evolutions of the parameters arrested. The first limitation of the model is that it is based on one-dimensional approach of notched beam. This approach tends to minimize the 3D effects of the structure and to amplify, to a lesser extent, the effects of changes in internal moisture of the wood, hence the presence of abnormal peaks appearing on the path of the model during periods of high humidification. A second limit remains the consideration of the tip of the crack at the expense of the crack front, but also the heterogeneity of the material, the 3D discontinuity of moisture, etc. The modeling of the MOE of a notched beam allowed us to report the behavior of the compliance trajectory of a NB in 4-points bending test, in uncontrolled environment. The general tendency observed is the highlight of the "++" effect of the internal humidity causing an increase in compliance and the existence of a limit of compliance.

  , 𝑦 𝐺 Deflection, deflection, Global deflection 𝑦 𝑀 , 𝑦 𝑉 Deflection induced by the bending moment, deflection dues to the shear force 𝛾 Lateral displacement dues to the shear force 𝑉 Shear force 𝐹 0 Intensity of total load applied on the beam

Figure 1 .

 1 Figure 1. (a) Half Notched Beam (HNB) in 4-points bending test. (b) Diagram of shear force 𝑽.

Figure 2 .

 2 Figure 2. Relationship between the experimental and analytical evolutions of the hydric displacement of the height of the notched tested.

Figure 3 .

 3 Figure 3. Typical trajectory JN of the notched beams DF9 and WF9 in function of the evolution of corrected moisture content (w).

ACKNOWLEDGEMENTS

The authors would like to acknowledge the valuable contributions of the National Research Agency (ANR) for their financial support of this work through the CLIMBOIS ANR-13-JS09-0003-01 project, as well as the labeling awarded by France's ViaMéca cluster and the laboratory of physical meteorology (Lamp) of University Clermont Auvergne for the regular sending of meteorological data. The acknowledge also the CNRS for the financial support through the project "Ingéniérie verte" RUMO.


Fery Léo

Gwozdziewicz Piotr

Moutou Pitti Rostand

Influence Of Fracture And Delayed Effects On Steel-Concrete Composite Structures

Keywords: Steel-concrete composite structure, Creep, Shrinkage, Fracture, Preflex beam

This work investigates the fracture behavior of Preflex beams submitted to time dependent effects and long term loadings. However, it is necessary to consider delayed effects such as shrinkage and creep on these structures because their influence is not negligible. The different materials used in mixed construction are presented. The flowchart applied to compute creep and relaxation effects versus the time is also described. It is interesting to note that the analytical results obtained for a Preflex beam are very similar to those obtained using finite element modelling on the LUSAS software. It is also observable that the cracking of the concrete that encases the lower fibre, due to creep, causes an increase of stresses in the steel profile that can lead to the break of the beam. The use of reinforcement on the preflex beam decrease the crack opening after 100 years loadings.

INTRODUCTION

Mixed construction is a method which knows a quick development and takes advantage of two materials: steel and concrete [START_REF] Aribert | Construction mixte acier-béton : Calcul des poutres mixtes[END_REF][START_REF] Aribert | Analyse et formulation pratique de l'influence de la nuance de l'acier du profilé sur le degré minimum de connexion partielle d'une poutre mixte[END_REF]. They are linked together by connectors, so that concrete works in compression and steel in tension. This results in a composite beam with improved mechanical characteristics because both concretes are supposed to work in compression. However, it is necessary to consider the impact of delayed effects such as shrinkage and creep on the fracture processes of these structures because their influence is not negligible.

The literature background show that some authors discussed about the behavior and the manufacturing process of preflex beam [START_REF] Bae | Behavior of preflex beam in manufacturing process[END_REF] [START_REF] Foort | Connecteurs dans les poutres Preflex : comportements dus à l'effort normal et à l'effort de rupture -Mémoire de fin d'étude[END_REF]. Foort [START_REF] Ghali | Concrete structures : stresses and deformations[END_REF] studied the behavior of connectors in preflex beams due to normal and rupture force. Jerome [START_REF] Jerome | Étude de la fissuration des poutres Preflex sollicitées. Comparaions numérique et analytique[END_REF] investigated Study the cracking of stressed preflex beams by comparing numerical and analytical results given by Ghali and Fabre method [START_REF] Zhang | Experimental Study on Static and Fatigue Behavior of Steel-Concrete Preflex Prestressed Composite Beam[END_REF]. But the impact of delayed effects as creep and relaxation of crack process have not investigated in this structure.

The different materials used in mixed construction are presented. In the same time, the conception step of the preflex beam and the numerical flowchart to obtain crack opening is described. It is also observable that the cracking of the concrete that encases the lower fibre, due to creep, causes an increase of stresses in the steel profile that can lead to the total collapse of the beam.

MATERIAL AND METHODS

Materials : Preflex beam

The Preflex beam is studied in the rest of this study. An isostatic beam with a span of 17 m was considered subject to a variable operating load, Figure 1 (a). The section consists of a HEM 700, first phase concrete B1 (in red) and second phase concrete B2 (in blue), as can be seen in Figure 1 (b). All dimensions are given here in meters . For numerical computation, a load of 7 kN / m² applied to the entire upper surface of concrete B2 is applied. The relative humidity of the environment has been assumed to be 70%.

Modelling steps

The different steps of the building are posted in Table 2. Concrete B1 is poured on day 1 and concrete B2 on day 31. In fact, we wait 28 days after pouring B1 before releasing the pre-bending forces and 28 days after pouring B2 before commission the beam. The pre-bending force is composed of two point forces: one located at the quarter of the beam and another located at the three-quarters. It is necessary that the profile does not exceed its elastic limit during the pre-bending step.

In order to calculate the rupture load of the Preflex beam in both cases (with and without the delayed effects), we will increase the operating load until the rupture stress of the profile is reached. The finite element software LUSAS is used in order to implement the numerical model. An important assumption which consists in supposing that the concrete B1 does not work anymore and can be removed when it is completely cracked. It should be noted that the concrete B1 cracks because the loading is important. In general, this concrete is used in Preflex beams for its toughness in compression. When the complete cracking appears we denote two load cases: one before the total cracking and one after. For the second load case (after cracking), we remove the concrete B1 and add a linear force representing the self-weight of B1 before restarting the calculation. Thus, for each load case (the load is gradually increased), we proceeded as follows detailed in flowchart of Figure 4 as follow:

• Step 1. First, we list the lower and upper fiber stresses in concrete B1 at 100 years.

• Step 2. It is then checked whether these values exceed the admissible stress.

• Step 3. If this is the case, we are looking for the date on which concrete B1 is completely cracked. If this is not the case, we go directly to step 6.

• Step 4. Two load cases are created: one before cracking and one after the removed of concrete B1 by adding its own weight.

• Step 5. We relaunch the calculations and we list the stresses suffered by concrete B2 at 100 years because removing concrete B1 increases the stresses in the profile and in concrete B2.

• Step 6. We check that the concrete B2 does not reach its admissible limit and we proceed in the same way as for B1 if this is the case. Finally, we list the maximum stress in the steel profile and check that it remains acceptable.

Stapes

RESULTS AND DISCUSSION

Crack opening with first reinforcement

The fracture due to creep and relaxation effects have been discussed in this section. The applying force is 22 kN/m² (breaking load of the beam when we do not use reinforcing steel taking up traction in concrete) at the serviceability limit state.

Stresses to profile

Step 1

Step 2

Step 3

Step 4

Step 5

Step 6

Crack opening (mm) The consideration of cracking of concrete under LUSAS, is computed by using steel reinforcements. We therefore started by applying a very weak reinforcement, composed of 6 mm bars every 30 cm, in the longitudinal and transverse direction of the beam, on the upper face and on the lower face.

Figure 3 presents the relationship between the crack opening versus time for the creep and the shrinkage effects. It observed that crack opening due to creep is significantly greater than that due to shrinkage. Indeed, at 100 years, creep represents 77.6% of cracking after loading and shrinkage only 22.4%. On the graph, we can notice that even at 100 years, the cracking induced by the shrinkage continues to increase very slightly whereas it is completely stopped for creep.

Crack opening with second reinforcement

In order to decrease the impact of delay deformations on crack opening, the reinforcement have been increased. In this case, bars of 6 mm every 15 cm were placed on the upper side and bars of 12 mm every 15 cm on the lower side. Figure 4 presents the relationship between the crack opening versus time for the creep and the shrinkage effects. This reinforcement of steel allows to remain below the admissible limit of cracking, since when we take into account the cracking due to loading and that due to delayed effects, we arrive at 0.08 mm at 100 years. Once again, we realize that the cracking due to creep is much greater than that due to shrinkage. The proportion remains the same since 77.6% of the cracking due to delayed effects comes from creep.

CONCLUSION

In this work, the behavior of preflex beam is investigated. The different materials used in the design of this structure is presented. The design step and the numerical implementation of crack opening with LUSAS software is detailed. The crack processes due to creep and shrinkage effects are investigated. It is observed that the use of reinforcement decrease significantly the crack opening even if the creep is responsible of more than 77 % of the crack opening process. From these results, it can be concluded that the consideration of delayed effects leads to a lower failure load of the beam. Therefore, it is necessary to consider the delayed effects in the dimensioning of a Preflex beam in order to avoid the total collapse of the structure.

Figure 1 :

 1 Figure 1: (a) Static model of the beam. (b) Section of beam studied According to table 1 (a), a shade of 275 MPa has been chosen. The elastic moment of the profile is 2299 kN.m. Concrete B1 and B2 are respectively C50 / 60 and C25 / 30 as posted in Table 1 (b).

Table 1 :

 1 (a) Characteristic of the profile HEM 700. (b) Characteristics of the concrete in B1/B2 phases

Figure 2 :

 2 Figure 2: Flowchart for computing the rupture stress

Figure 3 :

 3 Figure 3: Crack opening due to creep / relaxation effects with first reinforcements

Figure 4 :

 4 Figure 4: Crack opening due to creep / relaxation with second reinforcements

Table 2 :

 2 Preflex beam manufacturing steps