Laurent Vigouroux
email: laurent.vigouroux@univ-amu.fr

Benjamin Goislard De Monsabert

Chris Hayot

Philippe Androuet

Éric Berton

Laurent Contact

Vigouroux

Assessment of the risk and biomechanical consequences of lateral epicondylalgia by estimating wrist and finger muscle capacities in tennis players

Previous studies suggested that a pronounced weakness of the extensor muscles relative to the flexor muscles could increase the risk of occurrence of lateral epicondylalgia. This study investigates this hypothesis by estimating the ratio of extensor to flexor muscle capacities among healthy non-players (n = 10), healthy tennis players (n = 20), symptomatic players (n = 6), and players who have recovered from lateral epicondylalgia (n = 6). Maximum net joint moments in flexion or extension were measured during seven tasks involving the voluntary contraction of wrist and fingers. Using these data, the muscle capacities of the main muscle groups of the hand (wrist flexors, wrist extensors, finger flexors, finger extensors, and intrinsic muscles) were estimated using a musculoskeletal model. These capacities were then used to compute the extensor/flexor capacity ratios about the wrist and the finger joints. Compared to healthy nonplayers, healthy players presented higher extensor muscle capacities and greater capacity ratios showing that playing tennis generates specific adaptations of muscle capacities. Interestingly, symptomatic players, similar to those of non-players, showed more imbalanced ratios than healthy players. These results confirm that the ratio of extensor/flexor muscle capacities seems to be associated with lateral epicondylalgia and can be further used to understand its incidence and consequences.

Introduction

Lateral epicondylalgia, also called 'tennis elbow' , is one of the most common and painful musculoskeletal disorders of the upper limb [START_REF] Coombes | Elbow flexor and extensor muscle weakness in lateral epicondylalgia[END_REF][START_REF] Morris | Electromyographic analysis of elbow function in tennis players[END_REF]. This pathology affects the tendons of the extensor carpi radialis brevis (ECRB) and longus (ECRL) as well as, in some cases, the extensor digitorum communis (EDC) [START_REF] Fairbank | The role of the extensor digitorum communis muscle in lateral epicondylitis[END_REF]. Epidemiological studies reported that 40-50% of tennis players suffer from lateral epicondylalgia during their career [START_REF] Carroll | Tennis elbow: Incidence in local league players[END_REF]Morris KEYWORDS hand; net joint moments; musculoskeletal modelling; muscle capacity; elbow tendinosis et al., 1989). Additionally, lateral epicondylalgia has also been associated with other activities such as building work and keyboard data entry [START_REF] Haahr | Physical and psychosocial risk factors for lateral epicondylitis: A population based case-referent study[END_REF]. This pathology was defined as an angiofibroblastic degeneration in response to partial lesions of the common tendon of wrist extensors [START_REF] Kraushaar | Tendinosis of the elbow (tennis elbow). Clinical features and findings of histological, immunohistochemical, and electron microscopy studies[END_REF] which are created by repeated microtraumas [START_REF] Nirschl | Tennis elbow[END_REF][START_REF] Nirschl | Elbow tendinosis/tennis elbow[END_REF]. In the most extreme cases, complete rupture of the tendon may occur and surgical intervention is required [START_REF] Baker | Arthroscopic classification and treatment of lateral epicondylitis: Two-year clinical results[END_REF].

Lateral epicondylalgia is a complex pathology which results from the combination of multiple risk factors [START_REF] Gruchow | An epidemiologic study of tennis elbow. Incidence, recurrence, and effectiveness of prevention strategies[END_REF][START_REF] Nirschl | Tennis elbow[END_REF]. In the context of tennis playing, the vibrational wave propagation following ball impact has been proposed as a primary risk factor [START_REF] Chard | Racquet sports-patterns of injury presenting to a sports injury clinic[END_REF][START_REF] Hatze | Forces and duration of impact, and grip tightness during the tennis stroke[END_REF][START_REF] Hennig | Transfer of tennis racket vibrations onto the human forearm[END_REF]. However, since the pathology has also been associated with activities which do not involve any vibration phenomenon [START_REF] Nirschl | Tennis elbow[END_REF], this factor should rather be considered as secondary. Nevertheless, the shock wave propagation factor has been clearly associated with modifications of forearm muscle activities [START_REF] Wei | Comparison of shock transmission and forearm electromyography between experienced and recreational tennis players during backhand strokes[END_REF] and might therefore participate in the pathology development. Backhand strokes have also been assumed to be a primary factor in the pathology, mainly because they induce an eccentric stretching of wrist extensors [START_REF] De Smedt | Lateral epicondylitis in tennis: Update on aetiology, biomechanics and treatment[END_REF][START_REF] Knudson | Upper extremity angular kinematics of the one-handed backhand drive in tennis players with and without tennis elbow[END_REF][START_REF] Knudson | Biomechanical studies on the mechanism of tennis elbow[END_REF]. However, as with vibration phenomenon, this factor can be considered as secondary only since lateral epicondylalgia is caused by other activities which do not involve any eccentric actions of extensor muscles, e.g. farmers and office workers [START_REF] Haahr | Physical and psychosocial risk factors for lateral epicondylitis: A population based case-referent study[END_REF]. Moreover, even if some studies suggest that the manner in which backhand strokes are performed, e.g. using both hands or only one, could increase the risk of lateral epicondylalgia [START_REF] Renstrom | Elbow injuries in tennis[END_REF][START_REF] Roetert | The biomechanics of tennis elbow. An integrated approach[END_REF], no epidemiological evidence has yet demonstrated the benefit or the risk of preferring one technique over the other.

Alternatively, there is a more generic explanation for lateral epicondylalgia following the overuse of wrist extensor muscles related to repetitive and intensive grip force exertions during racket or other object manipulation [START_REF] Snijders | Provocation of epicondylalgia lateralis (tennis elbow) by power grip or pinching[END_REF][START_REF] Rossi | Potential effects of racket grip size on lateral epicondilalgy risks[END_REF]. The assumption of this explanation is initially supported by the significant electromyographic (EMG) activity measured for wrist extensor muscles (30 to 80% of the maximum EMG values) during both backhand and forehand strokes [START_REF] Giangarra | Electromyographic and cinematographic analysis of elbow function in tennis players using single-and double-handed backhand strokes[END_REF][START_REF] Morris | Electromyographic analysis of elbow function in tennis players[END_REF]. While the activation of extensors during backhand strokes is expected due to their agonistic role in this motion, their involvement during forehand strokes, which is mostly a flexion movement, is more surprising. This phenomenon is due to the complexity of the hand musculoskeletal system. Because the tendons of the finger flexor muscles cross the wrist joint, the contractions of those muscles necessary for exerting grip forces concurrently generate a flexion moment at the wrist [START_REF] Snijders | Provocation of epicondylalgia lateralis (tennis elbow) by power grip or pinching[END_REF]. As the wrist balance is key in stabilising the hand and ensuring the success of the grip task, intensive actions of extensor muscles are necessary to counterbalance the undesirable wrist flexion moment created by finger flexors [START_REF] Hoozemans | Prediction of handgrip forces using surface EMG of forearm muscles[END_REF][START_REF] Mogk | The effects of posture on forearm muscle loading during gripping[END_REF]. Musculoskeletal modelling of the hand showed that this phenomenon requires that wrist extensor muscles (EDC, ECRB, ECRL, and the extensor carpi ulnaris; ECU) produce forces close to their maximum force capacity during tennis forehand strokes [START_REF] Rossi | Potential effects of racket grip size on lateral epicondilalgy risks[END_REF] and, more generally, during prehension tasks (Goislard de Monsabert, [START_REF] Goislard De Monsabert | Quantification of hand and forearm muscle forces during a maximal power grip task[END_REF][START_REF] Goislard De Monsabert | Quantification of finger joint loadings using musculoskeletal modelling clarifies mechanical risk factors of hand osteoarthritis[END_REF].

From these previous findings, it can be hypothesised that part of the risk of lateral epicondylalgia might be related to an inability for the extensors to generate the appropriate muscle force intensities to counteract the flexor muscle actions naturally induced while gripping and manipulating a tennis racket [START_REF] Kaplan | Arteritis involving the arteries of the hand[END_REF][START_REF] Rossi | Potential effects of racket grip size on lateral epicondilalgy risks[END_REF][START_REF] Strizak | Hand and forearm strength and its relation to tennis[END_REF]. Assessing the relative force-generating capacity between extensor and flexor muscles, referred to as 'muscle capacity ratio' in this article, therefore appears pertinent in investigating lateral epicondylalgia risks and muscular adaptations resulting from playing tennis. Previous assessments of upper limb muscle capacity ratio were based only on maximum isometric net joint moments [START_REF] Alizadehkhaiyat | Upper limb muscle imbalance in tennis elbow: A functional and electromyographic assessment[END_REF], 2009;[START_REF] Coombes | Elbow flexor and extensor muscle weakness in lateral epicondylalgia[END_REF] or EMG activity [START_REF] Bazzucchi | Tennis players show a lower coactivation of the elbow antagonist muscles during isokinetic exercises[END_REF]. However, these previous studies suffered from several limitations in their assessment of muscle capacity. The EMG approach is questionable because the relationship between EMG and muscle force remains limited, mainly because of the uncertain nature of the signal as well as the issues related to its normalisation [START_REF] Rao | Influence of additional load on the moments of the agonist and antagonist muscle groups at the knee joint during closed chain exercise[END_REF] and distortion [START_REF] De Luca | Surface myoelectric signal cross-talk among muscles of the leg[END_REF]. Similarly, approaches using only net joint moment to assess muscle capacities can result in significant inaccuracies. Because co-contraction is inevitable, maximum isometric net joint moments always include the conjoint actions of both agonist and antagonist muscle groups, and might therefore inadequately represent agonist muscle capacity [START_REF] Amarantini | A method to combine numerical optimization and EMG data for the estimation of joint moments under dynamic conditions[END_REF]. In order to improve the assessment of muscle capacity, methods have been developed to estimate the individual contributions of agonist and antagonist muscle groups using a combination of net joint moments and EMG signals as inputs for musculoskeletal models [START_REF] Amarantini | A method to combine numerical optimization and EMG data for the estimation of joint moments under dynamic conditions[END_REF][START_REF] Olney | Predictions of knee and ankle moments of force in walking from EMG and kinematic data[END_REF][START_REF] Rao | Influence of additional load on the moments of the agonist and antagonist muscle groups at the knee joint during closed chain exercise[END_REF][START_REF] Vigouroux | Estimation of hand and wrist muscle capacities in rock-climbers[END_REF]. The use of such methods might therefore be more appropriate for investigating muscle capacity ratio with regard to the occurrence of lateral epicondylalgia.

This current study investigated the muscle capacities and the extensor/flexor muscle capacity ratios about the wrist and finger joints in one group of healthy non-players and three groups of tennis players comprising healthy players, players currently injured by lateral epicondylalgia and players who had recovered from lateral epicondylalgia. The objective of this study was to determine the adaptations of hand muscle capacities that could result from several years of playing tennis and to determine in what proportion the muscle capacity ratio could be associated with the occurrence of lateral epicondylalgia. A musculoskeletal model of the hand was used conjointly with a set of measurements of maximum net joint moments to estimate the muscle force capacities and the muscle capacity ratios at the wrist and metacarpophalangeal (MCP) joints. Given the predominant action of extensors when manipulating a racket, we hypothesised that the muscle capacity ratios are significantly modified by tennis experience and modified according to the pathology history of the players.

Methods

Participants

Forty-two males, including 32 tennis players and 10 non-players, volunteered to participate in this study. The 'Healthy non-players' group comprised ten male participants (age: 26.8 ± 3.6 years; height: 178.0 ± 5.8 cm; body mass: 69.2 ± 6.0 kg; hand length: 18.8 ± 1.0 cm) not involved in any activity requiring intensive use of the finger and wrist muscles. Tennis players were divided into three groups: 20 healthy tennis players with no history of lateral epicondylalgia ('Healthy tennis players' , age: 22.3 ± 9.5 years; height: 177.5 ± 6.7 cm; body mass: 67.5 ± 9.0 kg; hand length: 19.4 ± 1.1 cm), six tennis players currently suffering from chronic lateral epicondylalgia ('Symptomatic players' , age: 27.5 ± 8.4 years; height: 180.7 ± 7.6 cm; body mass: 72.5 ± 8.5 kg; hand length: 19.2 ± 0.7 cm), and six tennis players who had recovered from lateral epicondylalgia ('Recovered players' , age: 21.2 ± 4.2 years; height: 186.2 ± 11.5 cm; body mass: 79.0 ± 11.9 kg; hand length: 20.0 ± 1.6 cm). All tennis players trained at least four hours a week for three years, and were classified as intermediate or advanced players according to the International Tennis Number (ITN between 7 and 3). The player skill-levels were similar in the three groups. The criterion for inclusion in the Symptomatic group was that the participant was currently experiencing at least one of the following symptoms: laterally localised pain at the elbow, tenderness at the lateral epicondyle and/or pain with extension of the wrist or the fingers. The inclusion criterion in the Recovered group was that the lateral epicondylalgia should have been diagnosed and clinically treated during the last 3 years before the experiment. The ethics committee of Aix-Marseille University approved the testing protocol and a written informed consent was obtained from each participant before testing.

Measurement of maximum net joint moments

A method previously used to investigate muscle capacities in rock-climbers [START_REF] Vigouroux | Estimation of hand and wrist muscle capacities in rock-climbers[END_REF] has been adapted for this study. A hand-wrist ergometer (Bio2 M, Compiegne, France) measured the net isometric flexion and extension moments about the wrist and the MCP joints at 2000 Hz (Figure 1A). During all trials, the participant's position was such that the wrist was at 0° of both flexion and abduction with the forearm in neutral pronation; the elbow was at 90° of flexion and the shoulder was at 0°-15° of abduction and neutral flexion and rotation. The ergometer was fixed onto a wall and positioned according to the participant's height to ensure a horizontal position of the right forearm while conforming to the posture described above. Participants were asked to exert a maximum isometric joint moment about the MCP joints or about the wrist for six seconds. A resting period of one minute separated consecutive trials to limit the effects of fatigue.

The ergometer was used with four different modules which tested specific hand and finger muscle groups during maximum flexion or extension isometric moment exertion. Participants exerted isometric actions on the modules using either the palmar side of the hand/fingers for flexion moments or the dorsal side for extension moments. Participants were instructed to involve only their hand and fingers and to avoid any participation of the elbow, shoulder, trunk or legs. Although actions of synergist and antagonist muscles could not be excluded, the four modules were designed to provide maximum involvement of the targeted muscle group(s). The testing of multiple joints and tasks results in a strength profile that was further used by the model to assess the individual capacity of each muscle group. The modules were used as follows:

(i) The first module completely encased the hand palm between the MCP joint of the thumb and those of the four long fingers (Figure 1B). This module was used during the 'Wrist' tasks which consisted in exerting either flexion (W flex ) or extension (W ext ) moments about the wrist and aimed to involve only those muscles acting about the wrist. The module was designed such that the fingers were not in contact with the module, thereby minimising the actions of extrinsic finger muscles. (ii) The second module encased the hand and fingers from the palm of the hand to the tips of the fingers (Figure 1D). It was used to measure the combined actions of extrinsic finger muscles and wrist-specific muscles. The 'Combined' tasks corresponded to the exertion of flexion (C flex ) and extension (C ext ) moments about the wrist. (iii) The third module encased the four long fingers from the MCP joint line to the fingertips (Figure 1C). It was used during the 'Finger' tasks which consisted in exerting either flexion (F flex ) or extension (F ext ) moments about the MCP joints and aimed at maximally targeting the finger extrinsic muscles as well as the intrinsic muscles. (iv) The last module was placed under the proximal phalanx palmar side of the long fingers, distally to the MCP joints, such that DIP and PIP joints were free. It was used during the 'Intrinsic' task (F int ) which consisted in exerting a flexion moment about the MCP joints. The aim of this module was to involve the intrinsic muscles (Figure 1E).

EMG recordings

The levels of EMG activity of the ECRL, EDC, flexor carpi radialis (FCR), and flexor digitorum superficialis (FDS) muscles were recorded using a Biopac MP150 system (Biopac Systems, Inc. Santa Barbara, CA, USA) sampling at 2000 Hz. Surface electrodes (9-mm diameter) were positioned over the muscle bellies after skin abrasion and cleaning with alcohol. Electrodes were placed as in [START_REF] Vigouroux | Estimation of hand and wrist muscle capacities in rock-climbers[END_REF] and their placement was checked by asking participants to execute a series of contractions targeting specific flexor or extensor muscle groups about either the wrist or the MCP joints. EMG signals were filtered using a Butterworth filter (bandpass, 4th order, zero phase lag, bandwidth 20-400 Hz). Figure 2 shows an example of typical recordings for one participant during the wrist flexion task (left panels), the finger flexion task (middle panels), and the wrist extension task (right panels). In addition to the ergometer tasks, two trials were performed with zero net joint moments to measure the ratio of coactivation between forearm extensor and flexor muscles [START_REF] Falconer | Quantitative assessment of co-contraction at the ankle joint in walking[END_REF]. During this 'Co-contraction' task (Co), the participants were asked to maximally and statically co-contract all hand and forearm muscles with a similar arm, forearm and hand posture to the one used during ergometer tasks.

Data analysis and estimation of muscle capacities

For each condition, only the trial presenting the highest measured joint moment was considered for the analysis. The maximum isometric joint moments were evaluated using a 750 ms window centred on the peak moment for each task [START_REF] Valero-Cuevas | Large index-fingertip forces are produced by subject-independent patterns of muscle excitation[END_REF]. The samples over this time interval were used to compute the mean joint moment as well as the EMG root mean square (RMS) value. For each muscle, the muscle activation was assessed by normalising the RMS value during each task by the highest RMS value recorded among all trials and all time samples. The initial maximum capacities (M init max|g ) of the five g muscle groups (Wrist Flexors, WF; Wrist Extensors, WE; Finger Flexors, FF; Finger Extensors, FE; Intrinsic, FI) were evaluated using a musculoskeletal model of the hand including 42 m muscles (Table 1 and Equation ( 1)). To this end, the muscle-moment generating capacities of each muscle (M init max|g ) were initially evaluated according to Equation (2) as was classically done for a generic musculoskeletal model of the hand (Sancho-Bru, Perez-Gonzalez, [START_REF] Sancho-Bru | A 3D biomechanical model of the hand for power grip[END_REF][START_REF] Valero-Cuevas | Large index-fingertip forces are produced by subject-independent patterns of muscle excitation[END_REF]:

where the maximum muscle stress value (σ max ) is a constant, usually set at 35.4 N/cm 2 for hand muscles [START_REF] Valero-Cuevas | Large index-fingertip forces are produced by subject-independent patterns of muscle excitation[END_REF]. r m and PCSA m are the flexion/extension moment arm about either the wrist or MCP joint and the physiological cross-sectional area of the m muscle. PCSA data was taken from [START_REF] Chao | Biomechanics of the hand[END_REF] for the fingers, and from [START_REF] Ramsay | Muscle moment arm and normalized moment contributions as reference data for musculoskeletal elbow and wrist joint models[END_REF] for the wrist. Depending on the posture adopted during this experiment, muscle moment arms were computed assuming a neutral position (0° in flexion-extension, abduction-adduction, and pronation-supination) for all joints using the data from [START_REF] Chao | Biomechanics of the hand[END_REF] for the fingers, and from [START_REF] Lemay | A dynamic model for simulating movements of the elbow, forearm, and wrist[END_REF] for the wrist. Moment arms about the finger joints were scaled for each participant using their hand length [START_REF] Chao | Biomechanics of the hand[END_REF].

To assess personalised muscle capacities for each of the tested participants, the PCSA data and the σ max value of Equation (2) were adjusted using an optimisation procedure. The procedure consisted in determining for each participant a new σ max value and five PCSA coefficients (c g ). Each PCSA coefficient was used to multiply the PCSA of the muscles from one of the five muscle groups used in the procedure (WF, WE, FF, FE, and FI) such that:

The adj max and c g values were adjusted for each participant such that the model was able to reproduce the net moments effectively measured with the ergometer. The net moment measured during one of the eight tasks is denoted M ergo (task) with task = W flex ;W ext C flex ;C ext ;F flex ;F ext ;F int ;Co while its estimate using the musculoskeletal model is denoted M ergo (task) and calculated using the equation ( 1) (5)

M init max|g = ∑ m M init max|m with g = {WF;WE;FF;FE;FI} (2) M init max|m = r m ⋅ PCSA m ⋅ max (3) M adj max|g = ∑ m r m ⋅ c g ⋅ PCSA m ⋅ adj max (4) M ergo adj max , c g , a mech (task) = ∑ g a mech|g (task) ⋅ M adj max|g
f adj max , c g , a mech = ∑ task M ergo (task) -M ergo adj max , c g , a mech (task) 2 (6) ⎧ ⎪ ⎨ ⎪ ⎩ 15 < 𝜎 adj max < 60 0.5 < c g < 8 0 < a mech < 1 (7a) 0.7 M ergo C ext M ergo C flex ≤ M max|WE + M max|FE M max|WF + M max|FF ≤ 1.3 M ergo C ext M ergo C flex (7b) 0.7 M ergo (F ext ) M ergo (F flex ) ≤ M max|FE M max|FF + M max|FI ≤ 1.3 M ergo (F ext ) M ergo (F flex ) (7c) 0.7 M ergo (W flex ) M ergo (C flex ) ≤ M max|WF M max|WF + M max|FF ≤ 1.3 M ergo (W flex ) M ergo (C flex ) (7d) 0.7 M ergo (W ext ) M ergo (C ext ) ≤ M max|WE M max|WE + M max|FE ≤ 1.3 M ergo (W ext ) M ergo (C ext )
The initial values used in the optimisation for adj max and c g were 35.4 N/cm 2 and 1, respectively. Each a mech|g (task) initial value was equal to the EMG activity of the associated muscle during the task. All the initial values were carefully chosen to obtain the most physiologically realistic results. The constraints of Equation ( 7) were added to avoid unrealistic distribution of muscle capacities. They consisted in constraining ratios of estimated muscle capacities, e.g. flexor/extensor or flexor/flexor, with ratios of measured net moment values. These equations prevented large inconsistencies between the distribution of estimated muscle capacities and the profile of measured net moments. To assess the validity of the obtained results, the absolute differences between the estimated mechanical activations (a mech|g (task)) and the measured EMG activities were calculated (referred to as Diff EMG ). For each muscle group, this value was averaged among all tasks and all participants. No Diff EMG was computed for the Intrinsic muscle group because no EMG was collected for these muscles.

Using the estimated muscle capacities, the muscle capacity ratio between extensor and flexor muscles about the wrist (Mratio wrist ) and the MCP joints (Mratio mcp ) was computed as follows:

Statistical analysis

Mean and standard deviation (±) were computed for each group of participants. The results were checked for normality and homogeneity of variance. ANOVA (Statistica, Statsoft, Tulsa, USA) were used to identify significant differences among participant groups (Healthy non-tennis players, Healthy tennis players, Symptomatic players, Recovered players) for the maximum joint moment of the seven tasks (W flex , W ext, C flex , C ext, F flex , F ext, F int ), the estimated moment-generating capacities of the muscle groups at the wrist (WF, WE; FF, FE) and the MCP joints (FF, FE, FI) and the muscle capacity ratios at the wrist and MCP joints. The significance level was set at p < 0.05 and a Holm-Bonferroni's adjustment was made. Newman-Keuls was used post hoc to identify the differences between the samples when ANOVA showed significant effects.

Results

Measured net joint moments

Measured net joint moments are shown in Figure 3. A significant effect of participant groups was found on isometric flexion strength during the Wrist and the Combined tasks (F(3, 38) = 6.63; p = 1.0 × 10 -3 and F(3, 38) = 3.82; p = 1.7 × 10 -2 , respectively). In both these tasks, Symptomatic players exerted higher net joint moments compared with the other participant groups. During the Wrist flexion task, Symptomatic players exerted net joint moment amounting to 17.9 ± 3.9 Nm while Healthy non-tennis players, Healthy tennis players, (8a)

Mratio wrist = M max|WE + M max|FE M max|WF + M max|FF (8b) Mratio mcp = M max|FE M max|FF + M max|FI
and Recovered players exerted 15.4 ± 2.1 Nm, 13.6 ± 1.9 Nm, 15.9 ± 1.1 Nm, respectively.

During the Combined flexion task, Symptomatic players exerted on average 31.6 ± 7.6 Nm while other participants exerted 26.3 ± 5.9 Nm, 24.0 ± 4.1 Nm, 27.6 ± 2.2 Nm for Healthy non-players, Healthy tennis players, and Recovered players, respectively. Concerning extension net moments, a significant effect of participant groups was only found for the Wrist task (F(3,38) = 8.03; p = 2.9 × 10 -4 ). Healthy non-players exerted -8.1 ± 2.2 Nm while Healthy players, Recovered players, and Symptomatic players exerted -11.5 ± 1.7 Nm, -12.2 ± 1.1 Nm, -10.6 ± 2.9 Nm, respectively. No significant effect was found for the Combined extension task, for both Finger tasks as well as for the Intrinsic task (p > 0.05).

Estimated muscle moment-generating capacities

The estimated moment-generating capacities of muscle groups about the wrist and MCP joints are presented in Figure 4. For wrist muscles, a significant effect of participant groups was found in the capacities of both flexor (F(3,38) = 5.12; p = 4.5 × 10 -3 ) and extensor muscle groups (F(3,38) = 12.29; p = 9.0 × 10 -6 ). High wrist flexor capacities were observed in both Symptomatic and Recovered players (34.3 ± 3.8 Nm and 37.2 ± 7.3 Nm, respectively) compared with Healthy tennis players and Healthy non-players (29.7 ± 4.2 Nm and 29.7 ± 4.2 Nm, respectively). The wrist extensor capacity was higher (in absolute value) among Recovered players (-21.5 ± 2.8 Nm) and Healthy tennis players (-19.2 ± 2.2 Nm) than for Healthy non-players (-14.6 ± 2.5 Nm) and Symptomatic players (-16.7 ± 3.1 Nm).

Concerning the finger muscles, participant groups showed no significant effect on the capacities of finger flexors (19.3 ± 6.6 Nm at wrist joint), finger extensors (-7.8 ± 2.8 Nm at wrist joint), or intrinsic muscles (6.2 ± 2.6 Nm at MCP joints). 1. the healthy non-players are represented by the clear grey blocks, the healthy tennis players are in black, the recovered players are in white, and the Symptomatic players are represented by dark grey blocks. Significant differences between the indicated participant groups were noted (**: p < 0.01; *: p < 0.05; ‡: p < 0.1). Flexion and extension are positive and negative values, respectively. no muscle capacities were presented for the intrinsic muscle group at the wrist joint and for the wrist muscle groups at the mcp joints since these muscles do not cross these respective joints.

Table 2 provides the mean estimated parameters (c g and adj max ) that were used to compute the muscle capacities given above. As expected, the changes in c g coefficients and σ max values among the tested participant groups lead to similar conclusions as when comparing muscle capacities. These variables are indeed strongly related through Equation ( 5). The average differences between mechanical activations and EMG recordings during each task ranged from 0.11 to 0.30. The highest values were observed for the finger extensor muscle group with mean values ranging from 0.25 to 0.30. The lowest difference between mechanical activations and EMG recordings was observed for the finger and wrist flexors (values ranging from 0.11 to 0.16).

Muscle ratios of extensor/flexor capacities

Muscle capacity ratios at wrist and MCP joints are shown in Figure 5. A ratio less than 1 indicates that the joint presents an imbalanced capacity ratio with stronger capacities for flexor than for extensor muscles. The ANOVA showed a significant effect of participant groups on the wrist muscle capacity ratio (F(3, 38) = 5.08; p = 4.7 × 10 -3 ). All wrist capacity ratios were less than 1, meaning that wrist extensors were weaker than wrist flexors in all tested participant groups. Healthy tennis players showed the highest ratios amounting to 0.57 ± 0.10. Recovered players and Non-players showed intermediate ratios amounting to 0.50 ± 0.04 and 0.47 ± 0.07. Interestingly, the muscle capacity ratios of the Symptomatic players were the lowest (0.43 ± 0.07) and were on average 23.2 % lower than those of Healthy tennis players. At MCP joints, no significant effect within participant groups on muscle capacity ratio was found. Muscle capacity ratios at MCP joints were less than 1 in all tested participants (0.27 ± 0.07 on average for all participants).

Table 2. mean (±) estimated parameters for the musculoskeletal model. cg represents the pcSa coefficients adapted to each participant for each muscle group. adj max represents the maximum muscle stress coefficient adapted for each participant and used for all muscle groups. Diff emG represents the mean (among all tasks and all participants of a participant group) of the differences between the estimated mechanical activation of each muscle group and the recorded emG activity. no emG was recorded for the intrinsic muscle group.

Discussion and implications

The understanding of hand biomechanics during object or handle manipulation has significantly improved over the last decade, mostly through numerous ergonomic and clinical studies [START_REF] Kong | Optimal cylindrical handle diameter for grip force tasks[END_REF][START_REF] Sancho-Bru | Grasp modelling with a biomechanical model of the hand[END_REF][START_REF] Wu | Modeling the finger joint moments in a hand at the maximal isometric grip: The effects of friction[END_REF]. In particular, the estimation of muscle forces using musculoskeletal models confirmed that wrist extensors are highly involved when grasping handle (Goislard de [START_REF] Goislard De Monsabert | Quantification of hand and forearm muscle forces during a maximal power grip task[END_REF] and when performing tennis strokes [START_REF] Rossi | Potential effects of racket grip size on lateral epicondilalgy risks[END_REF], supporting the idea that a weakness of extensor muscles relative to flexor muscles could increase the risk of occurrence of lateral epicondylalgia. This study investigated the extensor/flexor capacity ratios about both the wrist and the MCP joints in participants with different levels of tennis expertise (advanced players vs. non-players) and various stages of lateral epicondylalgia (healthy, symptomatic, and recovered).

The first key result of this study shows that players have different hand muscle capacities compared with non-players. In accordance with the study of [START_REF] Rogowski | Asymmetry in volume between dominant and nondominant upper limbs in young tennis players[END_REF], who have characterised the increased forearm muscle volume in tennis players, our findings suggest that the experience of several years of playing gives rise to specific muscular adaptations. More precisely, our results indicate that these adaptations are not uniform among hand and forearm muscles since, compared with Non-players, Healthy tennis players showed a 30% increase of their wrist extensor capacity but no particular modification for other muscle groups. This result is quite surprising since one would expect that tennis players would need to enhance the capacity of wrist flexors to generate higher wrist moments and achieve more powerful forehand, serve or smash strokes [START_REF] Giangarra | Electromyographic and cinematographic analysis of elbow function in tennis players using single-and double-handed backhand strokes[END_REF]. In contradiction with this assumption, tennis practice and expertise might influence the capacity and coordination of other muscles such as those of the elbow and shoulder [START_REF] Rogowski | Scapulothoracic kinematics during tennis forehand drive[END_REF][START_REF] Genevois | Performance factors related to the different tennis backhand groundstrokes: a review[END_REF]. The particular strengthening of wrist extensors observed in our study probably result from their agonistic role in backhand stroke movements [START_REF] Knudson | Biomechanical studies on the mechanism of tennis elbow[END_REF] as well as the necessity to ensure equilibrium about the wrist joint while gripping and manipulating the racket [START_REF] Rossi | Potential effects of racket grip size on lateral epicondilalgy risks[END_REF]. This specific muscular adaptation also probably results from adaptations in patterns of muscle activation. [START_REF] Bazzucchi | Tennis players show a lower coactivation of the elbow antagonist muscles during isokinetic exercises[END_REF] indeed observed an increased level of wrist extensor activation which, after numerous repetitions, was able to improve the extensor capacities and contributes in explaining our observations.

From a clinical point of view, two interesting results were found regarding the hypothesis that muscle capacities are linked to the occurrence of lateral epicondylalgia. Firstly, it was observed that Symptomatic and Recovered tennis players showed a 20% higher wrist flexor capacity compared with Healthy tennis players and Non-players. Secondly, the Symptomatic players showed lower muscle capacity ratios about the wrist (i.e. around 0.43 on average) than both Healthy tennis players (around 0.55) and Recovered players (i.e. around 0.50). These two results indicate that there might be a link between extensor/flexor capacity ratios and the stage of lateral epicondylalgia which therefore partially validates our hypothesis. As the participants were not followed through a longitudinal study, it is difficult to determine whether the observed differences have initiated or are resulting from lateral epicondylalgia. Nevertheless, Symptomatic players exhibited both high wrist flexor capacities and a low capacity ratio which suggests that the wrist extensors are highly engaged in order to counteract the wrist moment created by the stronger flexors. From this reasoning, tennis players exhibiting such marked imbalance are probably more exposed to overuse of the extensor tendons and therefore to the occurrence of lateral epicondylalgia. The combination of both characteristics (strong wrist flexor and strongly imbalanced ratio) could thus be considered as a risk factor and/or as part of the early diagnosis of lateral epicondylalgia. The results obtained for Healthy tennis players and Recovered players tend to confirm this idea since their more balanced extensors/flexors ratio could explain why they were effectively free of the disease. The muscle capacity ratios observed for these two groups could thus be considered as an appropriate adaptation of the muscular capacities to prevent or recover from lateral epicondylalgia. On the contrary, Symptomatic players and Non-players exhibited low muscle capacity ratios which tend to indicate they are more at risk of the occurrence of lateral epicondylalgia and that their extensor capacities should be corrected to protect them for overuse and injuries. Nevertheless, although the results presented here are promising, longitudinal studies would be necessary to fully validate this hypothesis.

Our findings partially contradicted those of [START_REF] Alizadehkhaiyat | Upper limb muscle imbalance in tennis elbow: A functional and electromyographic assessment[END_REF][START_REF] Alizadehkhaiyat | Assessment of functional recovery in tennis elbow[END_REF]. These authors noted an overall weakness of wrist flexion and an increase in ECRB muscle activation in Recovered players. It was concluded that, despite attenuation of pain, an overall upper limb weakness indicated an incomplete functional recovery. In contrast, our results indicated that Recovered players show both higher capacity of wrist flexor muscles and higher wrist capacity ratio thus demonstrating a complete functional recovery and an adaptation of muscle force capacities. A first explanation of these important differences might be that the Recovered players tested in this study had already returned to playing tennis for more than one year, as against 6 months in [START_REF] Alizadehkhaiyat | Assessment of functional recovery in tennis elbow[END_REF]. Another explanation is that the measurement approach used by these authors differed substantially from the one used in this study. [START_REF] Alizadehkhaiyat | Upper limb muscle imbalance in tennis elbow: A functional and electromyographic assessment[END_REF][START_REF] Alizadehkhaiyat | Assessment of functional recovery in tennis elbow[END_REF] directly estimated muscle force capacities and ratios using the measured net wrist moments. However, as already explained in the introduction, net joint moments are not representative of muscle force capacities since these measurements corresponded to the resultant action of all the muscles involved, including both synergistic and antagonistic actions. By using a musculoskeletal model of the hand, this study took into account the influence of muscle co-contraction and therefore provided a more accurate estimate of the capacities. However, it should be noted that the use of such musculoskeletal models is subject to inherent limitations, such as assuming rigid segments and frictionless joints. Unfortunately, it is not possible to fully validate estimates of muscle capacities since the direct measurement of all forearm muscle forces is technically difficult and ethically unreasonable. Nonetheless, the differences between estimated and measured activations were in agreement with results of studies comparing estimates from EMG and musculoskeletal models [START_REF] Valero-Cuevas | Large index-fingertip forces are produced by subject-independent patterns of muscle excitation[END_REF][START_REF] Vigouroux | Using EMG data to constrain optimization procedure improves finger tendon tension estimations during static fingertip force production[END_REF]. This suggests that the estimated co-contraction levels were realistic and therefore that the levels of muscle capacities were also physiologically realistic. Therefore, although full validation is not possible, these points increase the confidence in our estimation of specific muscle force capacities.

Conclusion

In conclusion, our study showed that the muscle moment capacities and flexor/extensor capacity ratios can be useful for evaluating the risk of the occurrence of lateral epicondylalgia and quantifying the adaptation necessary to recover from or prevent the pathology. Apart from muscle capacities, it should however be noted that lateral epicondylalgia is a pathology resulting from a combination of multiple factors, some of them associated with the players' history, anthropometry, and/or amount of training. Muscle force capacity ratio should therefore be considered as an additional etiological factor in lateral epicondylalgia and/or an additional way in which to diagnosis lateral epicondylalgia. Our method could be further used to characterise individual muscle capacity profiles for training programmes and learning processes.

Figure 1 .

 1 Figure 1. experimental set-up. these pictures show the specially-designed ergometer (a) as well as the modules and associated postures (B, c, D, and e) used to realise the isometric maximal voluntary contractions.

Figure 2 .

 2 Figure 2. representative net joint moment data recorded by the ergometer (top panels) and associated emG recordings of the four representative muscles (Fcr, FDS, ecrL, eDc) during the wrist flexion task (left panels), finger flexion task (middle panels), and wrist extension task (right panels). the emG recordings clearly indicate that the activation levels of synergist and antagonist muscles are non-negligible and therefore justify the need for a musculoskeletal model to estimate the capacities of specific muscle groups instead of only using the resultant net joint moments.

Figure 3 .

 3 Figure 3. results of measured net joint moments. the clear grey blocks represent the healthy non-players, the black blocks represent the healthy tennis players, the white blocks represent the recovered players, and the dark grey blocks represent the Symptomatic players. positive values represent flexion moments, negative values represent extension moments. Significant differences between the indicated participant groups were noted (**: p < 0.01; *: p < 0.05; ‡: p < 0.1).

Figure 4 .

 4 Figure 4. estimated muscle moment-generating capacities at the wrist joint level (upper panel) and mcp joints level (lower panel). muscles included in the different muscle groups are detailed in table1. the healthy non-players are represented by the clear grey blocks, the healthy tennis players are in black, the recovered players are in white, and the Symptomatic players are represented by dark grey blocks. Significant differences between the indicated participant groups were noted (**: p < 0.01; *: p < 0.05; ‡: p < 0.1). Flexion and extension are positive and negative values, respectively. no muscle capacities were presented for the intrinsic muscle group at the wrist joint and for the wrist muscle groups at the mcp joints since these muscles do not cross these respective joints.

Figure 5 .

 5 Figure5. extensor/flexor muscle capacity ratios at the finger (mcp) and wrist joints. healthy non-players, healthy tennis players, recovered players, and Symptomatic players are represented by the clear grey, black, white, and dark grey blocks, respectively. a ratio close to 1 means that the extensors and the flexors present similar moment-generating capacities. Significant differences between the indicated participant groups were noted (*: p < 0.05; ‡: p < 0.1).

Table 1 .

 1 muscle groups used in the optimisation process. We Wrist extensors extensor carpi radialis brevis, extensor carpi radialis longus, extensor carpi ulnaris FF Finger Flexors flexor pollicis longus; flexor digitorum profundus of index, middle, ring, and Little fingers; flexor digitorum superficialis of index, middle, ring, and Little fingers Fe Finger extensors extensor pollicis longus and brevis; adductor pollicis longus; extensor digitorum communis of index, middle, ring, and Little fingers, extensor indicis proprius, extensor digiti quinti a mech|g (task) is the mechanical activation of the g muscle group with g = {WF;WE;FF;FE;FI}. The mechanical activation a mech|g (task) is a value between 0 and 1 which indicates at what percentage of its maximum capacity (M

	Muscle Group	Included Muscles
	WF Wrist Flexors	flexor carpi radialis; flexor carpi ulnaris; palmaris longus
	Fi intrinsic	1st, 2nd, 3rd, 4th dorsal interossei; 1st, 2nd, 3rd palmar interossei; 1st, 2nd, 3rd, 4th lumbrical, flexor
		digiti quinti, abductor digiti quinti

where a mech = a mech|WF (task);a mech|WE (task);a mech|FF (task);a mech|FE (task);a mech|FI (task)

Acknowledgements

This work forms part of a collaboration between the Decathlon-Oxylane group and Aix-Marseille University through the contract supporting the 'corps au sport' chair.

Disclosure statement

No potential conflict of interest was reported by the authors.


Langlois David
email: david.langlois@loria.fr

Smaïli Kamel
email: kamel.smaili@loria.fr

LORIA System for the WMT13 Quality Estimation Shared Task

In this paper we present the system we submitted to the WMT13 shared task on Quality Estimation. We participated to the Task 1.1. Each translated sentence is given a score between 0 and 1. The score is obtained by using several numerical or boolean features calculated according to the source and target sentences. We perform a linear regression of the feature space against scores in the range [0..1], to this end, we use a Support Vector Machine with 66 features. In this paper, we propose to increase the size of the training corpus. For that, we decide to use the post-edited and reference corpora in the training step after assigning a score to each sentence of these corpora. Then, we tune these scores on a development corpus. This leads to an improvement of 10.5% on the development corpus, in terms of Mean Average Error, but achieves only a sligth improvement on the test corpus.

Introduction

In the scope of Machine Translation, Quality Estimation (QE) is the task consisting to evaluate the translation quality of a sentence or a document. This process may be useful for post-editors to decide or not to revise a sentence produced by a machine translation [START_REF] Specia | Exploiting objective annotations for measuring translation post-editing effort[END_REF][START_REF] Specia | Predicting machine translation adequacy[END_REF]. Moreover, it can be useful to decide if a translated document can be broadcasted or not [START_REF] Soricut | Trustrank: Inducing trust in automatic translations via ranking[END_REF]. The most obvious way to give a score to a translated sentence consists in using a machine learning approach. This approach is supervised: experts are asked to score translated sentences and with the obtained material, one learns a prediction model of scores. The main drawback of the machine learning approach is that it is supervised and requires huge data. To score a sentence is time-consuming. Moreau et al. in [START_REF] Moreau | Quality estimation: an experimental study using unsupervised similarity measures[END_REF] dealt with this issue by proposing unsupervised similarity measures. In fact, the score of a translated sentence is defined by a measure giving the distance between it and the contents of an external corpus. The authors improve the results of the supervised approach but this method can be used only in the ranking task. Raybaud et al. [START_REF] Raybaud | Detecting errors in machinetranslated sentences[END_REF] proposed a method to add errors in reference sentences (deletion, substitution, insertion). By this way, they build additional corpus in which each word can be associated with a label correct/not correct. But, it is not possible to predict the translation quality of sentences including these erroneous words.

In this paper, we propose to increase the size of the training corpus. For that, we use the score given by experts to evaluate additional sentences from the post-edited and reference corpora. Practically, we extract from source and target sentences numerical vectors (features) and we learn a prediction model of the scores. Then, we apply this model to predict the scores of the post-edited and the reference sentences. And finally, we tune the predicted scores on a development corpus.

The article is structured as follows. In Section 2, we give an overview of our machine learning approach. We give a short description of the features we use in Section 3. Then, in Sections 4 and 5 we describe the corpora and how we increase the size of the training corpus by a partly-unsupervised approach. In section 6, we give results about this method and we end by a conclusion and perspectives.

Overview of our quality estimation submission

We submit a system for the task 1.1: one have to evaluate each translated sentence with a score between 0 and 1. This score is read as the HTER be-tween the translated sentence and its post-edited version. Each translated sentence is assigned a score between 0 and 1. The score is calculated using several numerical or boolean features extracted according to the source and target sentences. We perform a regression of the feature space against [0..1]. To this end, we use the Support Vector Machine algorithm (LibSVM toolkit [START_REF] Chang | LIBSVM: A library for support vector machines[END_REF]). We experimented only the linear kernel because our experience from last year [START_REF] Langlois | Loria system for the WMT12 quality estimation shared task[END_REF] showed that its performance are yet good while no parameters have to be tuned on a development corpus.

The features

This year, we prefered to investigate the underlying knowledge on the post-edited and reference corpora. Therefore, we use the same features as last year [START_REF] Langlois | Loria system for the WMT12 quality estimation shared task[END_REF], including the 17 baseline features provided by the quality estimation shared task organizers and our own features [START_REF] Raybaud | Detecting errors in machinetranslated sentences[END_REF][START_REF] Langlois | Loria system for the WMT12 quality estimation shared task[END_REF]. Some of our features describe source sentences only, ones use target sentences only, and another describe both sides. We extract information by the way of language model information (perplexity, level of back-off, intra-lingual triggers), translation table (IBM1 table, inter-lingual triggers). For language models, we use models trained on corpus read from left to right (classical way), and from right to left (the first word is the last in sentences).

The features at sentence level are computed by averaging over each word in the sentence. Because the confidence on a word depends on its neighbours, we add features which are average of word scores in a window centered or not on the word. The union of the both sets baseline+loria improved sligthly the baseline system on the test set provided by the Quality estimation Shared Task 2012 [START_REF] Callison-Burch | Findings of the 2012 workshop on statistical machine translation[END_REF]. The reader will find more details in [START_REF] Langlois | Loria system for the WMT12 quality estimation shared task[END_REF]. In the following (Section 6), we give results for BASE-LINE or LORIA features alone, and for BASE-LINE+LORIA features.

Corpora

The organizers provide a set of files for training and development. We list below the ones we used:

• source.eng: 2,254 source sentences taken from three WMT datasets (English): news-test2009, news-test2010, and news-test2012.

In the following, this file is named src • target system.spa: translations for the source sentences (Spanish) generated by a PB-SMT system built using Moses. In the following, this file is named syst • target system.HTER official-score: HTER scores between MT and post-edited version, to be used as the official score in the shared task. In the following, this file is named hteroff • target reference.spa: reference translation (Spanish) for source sentences as originally given by WMT; In the following, this file is named ref • target postedited.spa: human post-edited version (Spanish) of the machine translations in target system.spa. In the following, this file is named post

We split these files into two parts: a training part made up of the 1,832 first sentences, and a development part made up of the 442 remaining sentences. This choice is motivated by the fact that in the previous evaluation campaign we had exactly the same experimental conditions. For each given file f, we use therefore a part for training f.train and a part for the development f.dev.

Training Algorithm

This section describes the approach we propose to increase the size of the training corpus.

We have to train the prediction model of scores from the source and target sentences.

The common way to train such a prediction model consists in extracting a features vector for each couple (source,target) from the (src.train,syst.train) corpus. For each vector, the score associated by experts to the corresponding sentence is assigned. Then, we use a machine learning approach to learn the regression between the vectors and the scores. And finally, we use the triplet (src.dev,syst.dev,hteroff.dev) to tune parameters.

On machine learning approach, the number of examples is crucial for a relevant training, but unfortunately in this approach, we use a training corpus of only 1,832 examples.

To increase the training corpus, we propose to use the ref and post files. But for that, we have to associate a score to these new target sentences. One way could be to calcule the HTER score between each sentence and its corresponding sentence in the post edited file. But this leads to a drawback: all the couples (src,post) would have a score equal to 0, and then there is a risk of overtraining on the 0 value. To prevent this problem, we prefered to learn a prediction model from the (src.train,syst.train,hteroff.train)

triplet. Then we apply this prediction model to the (src.train,post.train) and to the (src.train,ref.train). Then, we get a training corpus made up of 1, 832 × 3 = 3, 696 examples with their scores. Consequently, it is possible to learn a prediction model from this new training corpus. These scores are not optimal because the features cannot describe all the information from sentences, and a machine learning approach is limited if data are not sufficiently huge. Therefore, we propose an any-time randomized algorithm to tune the reference and post-edited scores on the development corpus. We give below the algorithm we propose. To evaluate a model, we use it to predict the scores on the development corpus. Then we compare the predicted scores to the expert scores and we compute the Mean Average Error (MAE) given by the formula M AE(s, r) = n i=1 |s i -r i | n × 100 where s and r are two sets of n scores.

Results

We used the data provided by the shared task on Quality Estimation, without additional corpus. This data is composed of a parallel English-Spanish training corpus. This corpus is made of the concatenation of europarl-v5 and news-commentary10 corpora (from WMT-2010), followed by tokenization, cleaning (sentences with more than 80 tokens removed) and truecasing. It has been used for baseline models provided in the baseline package by the shared task organizers. We used the same training corpus to train additional language models (forward and backward 5gram with kneyser-ney discounting, obtained with the SRILM toolkit) and triggers required for our features. For feature extraction, we used the files provided by the organizers: 2,254 source english sentences, their translations by the baseline translation system, and the score of these translations.

This score is the HTER between the proposed translation and the post-edited sentence. We used the train part to perform the regression between the features and the scores. Therefore, the system we propose in this campaign is the same as the one we presented for the previous campaign in terms of features. But, we only use a SVM with a linear kernel and we do not use any feature selection. The added value of the new system is the fact that we increase the size of the training corpus.

To evaluate the different configurations, we used the MAE measure. The performance of our system with only the classical train set (src.train,syst.train) are given in Table 1. In this table, BASELINE+LORIA use both features BASELINE and LORIA (Section 3). We remark that, contrary to last year, the BASE-LINE+LORIA do not improve the performance of the BASELINE features on the development set. Now, we increase the training corpus with the method described in previous section.

First, we use the system trained on (src.train,syst.train) to predict scores for the sentences in post.train and ref.train. We know that these scores should represent the HTER score, then a well translated sentence should be assigned a higher score. Therefore, we can make the hypothesis that sentences from post.train and ref.train are better than the ones in syst.train. We check this hypothesis by comparing the distributions of HTER scores in the three files (true HTER scores in syst.train, and predicted scores in the two other files). We present in Table 2 the minimum, maximum, mean and standard deviation of this score for the three corpora. We remark that the scores are not well predicted because some of them are negative while all scores in syst.train are between 0 and 1. This is due to the fact that the constraint of HTER in terms of limit values are not explicitly taken into account by SVM. We give more details about these scores out of [0..1] in Table 3. For post.train, 2 scores are under 0 with a mean value equal to -0.123, and no scores are higher than 1. For ref.train, 4 scores are under 0 with a mean value equal to -3.023, and 26 scores are higher than 1 with a mean equal to 1.126. Comparing to the 1,832 sentences in the training corpus, we can conclude that the 'outliars' are very rare. In Table 2 Mean and Standard deviation are computed only for scores predicted between 0 and 1. The obtained mean values are quite similar, but the standard deviation are very low for predicted scores.

This configuration leads to a performance equal to 13.88 on the development corpus, which is slightly worse than the BASELINE system but slightly better than the BASELINE+LORIA system. Because, SVM achieves results which do not represent exactly HTER and because the model is learnt on a relatively small corpus (1,832 sentences), we decided to modify randomly some scores. This operation is called in the following a tuning process.

For the tuning process, after several tests, we fixed to 0.1 the probability pdisturb to modify the score of a sentence. Then, the score is modified by shifting it in [-0.01... + 0.01]. We start with the initial predicted scores (MAE = 13.88). Then we randomly modify a subset of scores and keep a new configuration if its MAE is improved. The process is stopped when MAE converges. Figure 1 presents the evolution of MAE on the development corpus.

The process stopped after 22, 248 iterations, only 274 (1.2%) iterations led to an improvement. We present the results of this approach on the development corpus and on the official test set of the campaign (500 sentences). We group in Table 4 the results on development and test corpus for the BASELINE features and the BASELINE+LORIA features with and wihtout using the postedited and reference sentences. Finally, we achieve a MAE of 12.05 on the development set. This constitutes an improvement of 10.5% in comparison to the BASELINE system. But we improve only sligthly the performance of the baseline system on the test set. We conclude that there is an overtraining on the development corpus. In order to prevent from this problem, we could use a leaving-one-out approach on the training and development corpora.

With the tuned values of scores, we calculated the same statistics as in Tables 2 and3. We present these statistics in Tables 5 and6. As we can see, the tuning process leads to an increasing of the mean value of the scores. Moreover, the number of scores out of range increases. This analysis reinforces our conclusion about overtraining: predicted scores may be strongly modified to obtain a good performance on the development corpus. To conclude the experiments, we try to fix the problem of scores predicted out of range. For that, we set to 0 the scores lower than 0 and to 1 the ones greater than 1. Then we learn a new SVM model using these new scores. This leads to a MAE equal to 12.18 on the development corpus and 14.83 on the test corpus, which is worse than the performance without correction. This is for us a drawback of the machine learning approach. For this approach, the scores have no semantic. SVM do not "know" the scores are HTER between 0 and 1. Then, if tuning leads to no reasonable values, this is not a problem if it increases the performance. Moreover, maybe the features do not extract from all sentences information representative of their quality, and this quality is over-estimated: then the tuning system has to lower strongly the corresponding scores to counteract this problem.

Set of features

Conclusion and perpespectives

In this paper we propose a method to increase the size of the training corpus for Quality Estimation in the scope of Task 1.1. We add to the initial training corpus (sentences translated by a machine translation system) the post-edited and the reference sentences. We associate to these sentences scores predicted by using a model learnt on the system sentences. Then we tune the predicted scores on the development corpus. This method leads to an improvement of 10.5% on the development corpus in terms of MAE, but achieves only a sligth improvement on the test corpus. A statistical study shows that tuning scores leads to out of range values. This surprising behaviour have to be investigated. In addition, we will test another machine learning tools (neural networks for example). Another point is that, contrary to last year, the whole set of features leads to worse performance than baseline features. This could be explained by the fact that no selecting algorithm has been used to choose the best features. In fact, we prefered, this year to investigate the underlying knowledge on the post-edited and reference corpora. Last, we conclude that the good improve-ment on the development corpus is not reproduced on the test corpus. In order to prevent from this problem, we will use a leaving-one-out approach on the training.

  train,syst.train) and HTER target scores from experts 2. Predict initial scores for postedited and reference sentences (a) Use this model to predict the scores associated to the features from (src.train,post.train) and (src.train,ref.train). The predicted scores for (src.train,post.train) are called post best and the ones for (src.train,ref.train) are called ref best 3. Learn initial prediction model using the 3 trains (system part, post-edited part and reference part) (a) Learn the prediction model using features from the three sets of features and the scores associated to these sets (experts scores, post best and ref best) (b) Evaluate this model. This leads to a performance equal to best 4. Tune scores for postedited and reference sentences (a) Repeat the following steps until stop (b) Build a new set of scores named post new (resp. ref new) by disturbing each score of post best (resp. ref best) with a probability equal to pdisturb. A modified score is shifted by a value randomly chosen in [-disturb,+disturb] (c) Learn the prediction model using features from the three sets of features and the new scores associated to these sets (experts scores for system set, post new and ref new for the postedited and reference sets) (d) Evaluate this model. This leads to a performance equal to perf (e) If perf<best then replace best by perf, post best by post new and ref best by ref new.

Table 1 :

 1 Performance in terms of MAE without increasing the training corpus

				Dev
		BASELINE	13.46
		LORIA		14.04
		BASELINE+LORIA 13.88
	Set of sentences Min	Max Mean Standard deviation
	syst.train	0	1	0.317 0.169
	post.train	-0.147	0.708 0.315 0.083
	ref.train	-11.314 0.746 0.329 0.081

Table 2 :

 2 Statistics on HTER for the three set of sentences used in the training corpus

			lower than 0	higher than 1
	Set of sentences Number Mean Number Mean
	syst.train	0	-	0	-
	post.train	2	-0.123 0	-
	ref.train	4	-3.023 26	1.126

Table 3 :

 3 Statistics on HTER for the three sets of sentences used in the training corpus Figure 1: Evaluation of the MAE on the development corpus

	Set of features	Dev	Test
	BASELINE	13.46 14.81
	BASELINE+LORIA 13.88 nc
	+ postedited + ref	13.78 nc
	+ tuning	12.05 14.79

Table 4 :

 4 Performance in terms of MAE of the features with and without increasing the training corpus

	Set of sentences Min	Max Mean Standard deviation
	post.train	-0.811	1.322 0.407 0.235
	ref.train	-10.485 1.320 0.409 0.242

Table 5 :

 5 Statistics on HTER for the three set of sentences used in the training corpus, after tuning

		lower than 0	higher than 1
	Set of sentences Number Mean Number Mean
	post.train	318	-0.164 29	1.118
	ref.train	282	-0.205 28	1.123

Table 6 :

 6 Statistics on HTER for the three set of sentences used in the training corpus, after tuning