Gilles Ma
email: martine.gilles@inrs.fr

Keywords: Intrinsic movement variability, motor control, workstation design Highlights

For several years, increasing numbers of studies have highlighted the existence of movement variability. Before that, it was neglected in movement analysis and it is still almost completely ignored in workstation design. This article reviews motor control theoriesand factors influencing movement execution,and indicateshowintrinsic movement variability is partof task completion. These background clarifications shouldhelp ergonomists and workstation designers to gain a better understanding of these concepts,which can then beusedto improve design tools. We also question whichtechniques -kinematics, kinetics or muscular activityand descriptors are most appropriate for describing intrinsic movement variability and for integrationinto design tools. By this way, simulations generated by designers for workstation design should be closer to the real movements performed by workers. This review emphasises the complexity of identifying, describing and processing intrinsic movement variability in occupational activities.

-MOVEMENT VARIABILITY AND MOTOR CONTROL

This paper relates to voluntary movementwhichdesignates a movement performed for the purpose of completing a specific task. Voluntary movement must be distinguished from reflex movement, which is a stereotyped motor response triggered by sensorystimulation.

In recent decades, a number of studies have developed theories taking the intrinsic variability of human movementinto account [START_REF] Kerlirzin | Le contrôle moteur : organisation et contrôle du mouvement -PUF éds[END_REF][START_REF] Stenard | Progress in motor control[END_REF].The bases of these theories, grouped as motor control theory,rely on several disciplines including biomechanics, movement physiology, behavioural neurosciences and cognitive sciences. Motor control is defined as the constant interaction between a subject, the environment in which they act and the task to be performed. Movement is therefore planned by the central nervous system (CNS) based on sensory information related to the environment in which the task is performedand the subject's capacity to interpret this informationwith the best possible yield. The more expert the subject, the better they will be able to achieve their objective, maximising the probability of success and minimising production and implementation costs [START_REF] Leplat | Les habiletés et leur analyse. Terrain et laboratoire[END_REF][START_REF] Leplat | L'acquisition des habiletés mentales : la place des techniques[END_REF]. This theory raises many questions with respect to its application. Some questions remain unanswered, for instance concerning the CNS's ability to process and control such a large amount of sensory information within a period as short as that of reaction time.

To answer these questions, [START_REF] Bernstein | The coordination and regulation of movement[END_REF] proposed an initial explanation,based on the notion of reducing the complexity of the "human" system. The association of complex kinematic chains with combinations of activation of joints and different muscles gives rise to an infinite number of possible configurations in which the same taskcould be performed. Muscular synergies and segmental strategies chosenthrough afferent information can then be used to decrease the number of degrees of freedom required to efficiently control the system. This control loop system makes it possible to performthe same task by exercising different muscles activities or joint amplitudes.

Motor control, to be efficient, must be able to select the appropriate input fromamong the huge amount ofsensory information to achieve some required output consistent with the environmentwhere the worker acts and the task to be performed. Depending on the information selected, the movement performed can be different.To select the appropriate input, a model of understandingwas proposed based on multiple paired forward and inverse models.

This model was considered an interesting way to achieve motor learning and control (Wolpert and Kawato, 1998;[START_REF] Harris | Signal-dependent noise determines motor planning[END_REF]Wolpert et al., 1998[START_REF] Blakemore | Spatio-temporal prediction modulates the perception of selfproduced stimuli[END_REF][START_REF] Wolpert | A unifying computational framework for motor control and social interaction[END_REF]. For [START_REF] Berthoz | Le sens du mouvement[END_REF], the brain continuously generates hypotheses on the movement to come, allowing it to formulate preparatory movements or postural adjustments. These hypotheses are based on the sensoryinformation provided based on the environment, linked to the memory of the movement acquired through experience. Along the same lines, for [START_REF] Rosenbaum | Remembered positions: stored locations or stored postures?[END_REF], movement is predictedas a function of the final posture that the forearmor hand must adopt at the end of the action. These authorshypothesised that each new posture encountered is stored in memory so that it can be re-used during a similar situation in the future. Furthermore, these plans of action are basedon procedural memory, i.e.,the personneeds to really perform the action rather than simply observing it or imagining doing it (Walsh and Rosenbaum, 2009). Thus, the more experience the personhas in performingatask, the better he/she will be able to select the relevant information from the environment and to readjust his/her movement, doing so as fast as possible while performing the task. The person will be able to better adapt to environmental conditions. This adaptation can be done in the short-term, during the movement itself, or in the medium to long-term, as a result of learning.

However, taking all this information on board to generate a movement comes at a considerable cost for the CNS.

Five main models are admitted as possible ways to reduce this cost. The minimum jerk model focuses on minimising variations in effector acceleration [START_REF] Flash | The coordination of arm movements: an experimentally confirmed mathematical model[END_REF][START_REF] Hogan | An organizing principle for a class of voluntary movements[END_REF]. In the minimal effort model, neuromuscular behaviour is compared to a spring for which the equilibrium point depends on the simultaneous activation of agonistic and antagonistic muscles and joint stiffness [START_REF] Hasan | Optimized movement trajectories and joint stiffness in unperturbed, inertially loaded movements[END_REF]. The minimum torque change model is based on the value of the torque or external forces applied to the system [START_REF] Uno | Formation and control of optimal trajectory in human multijoint arm movement[END_REF]. In a fourth model, the cost of a movement is estimated by the CNS based on the minimal variance between the final position of the forearmand that of the eye during movement of the upper limb [START_REF] Harris | Signal-dependent noise determines motor planning[END_REF]. Finally, the optimal feedback control model takesobserved movement variability into account only if it is likely to jeopardise the movement's final goal. According to this model, variability is an integral part of the movement and of itssuccess [START_REF] Todorov | Optimality principles in sensorimotor control (review)[END_REF][START_REF] Todorov | Optimal feedback control as a theory of motor coordination[END_REF]. All these models are based on kinetic or kinematic information. In addition to these models, aneurophysiological approach based on joint coordinates has also beenproposed asa possible means to reduce the cost of motor control. Withthis approach, control is achieved through proprioceptive muscle feedback by determining an equilibrium position for the limb and stiffness around the joint [START_REF] Feldman | Functional tuning of the nervous system with control of movement or maintenance of a steady posture. III. Mechanographic analysis of the execution by man of the simplest motor tasks[END_REF][START_REF] Bizzi | Does the nervous system use equilibrium-point control to guide single and multiple joint movements?[END_REF].

Even if motor control theories are still being discussed, intrinsic movement variability seems to be a natural feature of human movement. The theories developed thus far show that variability could be present at muscle and jointlevels [START_REF] Côté | Effects of fatigue on intermuscular coordination during repetitive hammering[END_REF][START_REF] Madeleine | Time to task failure in shoulder elevation is associated to increase in amplitude and to spatial heterogeneity of upper trapezius mechanomyographic signals[END_REF][START_REF] Holtermann | The use of EMG biofeedback for learning of selective activation of intra-muscular parts within the serratus anterior muscle A novel approach for rehabilitation of scapular muscle imbalance[END_REF]. These theories can also serve as the bases for control algorithms used to simulate human motion by workstation design engineers. For instance, De [START_REF] De Magistris | Dynamic control of DHM for ergonomic assessments[END_REF][START_REF] De Magistris | Dynamic digital human models for ergonomic analysis based on humanoid robotics techniques[END_REF] used Fitt's law, Todorov's speed profiles model, minimum jerk optimisation, and minimum exertion to model human movement in an industrial assembly task.

More recently, an additional approach was introduced. This approachconsiders motor redundancy through the principle of abundance derived from [START_REF] Bernstein | The coordination and regulation of movement[END_REF] and states thatredundancy should no longer be considered as a problem to be solved, but as a benefit. Indeed, redundancymakes it possibleto adapt movements to the main constraints, i.e., the objective of the task,and to the secondary environmental constraints,which include perturbationsor parallel tasks cropping up as the movement is performed [START_REF] Latash | The bliss (not the problem) of motor abundance (not redundancy)[END_REF]. This approach differentiates state variables characterising the system, such as joint angle or muscle activation, and performance variables that describe performance of the task, such as an exerted force or a trajectory of the final effector [START_REF] Park | Optimality versus variability: an example of multi-finger redundant tasks[END_REF][START_REF] Park | Optimality versus variability: effect of fatigue in multifinger redundant tasks[END_REF].Some of the state variables significantly affect task performance and should be controlled, while othershave little effect and can be more loosely constrained. During a perturbation or while performing a secondary task, the CNS can stabilise the main state variables for the principle task by bringing other variables into play. Thus,a task does not have a single, optimal solution, but a family of equivalent solutions, providing the system with "goal equivalent" variability.

All these theories aim to understand how movement is planned and performed. However, none of them provides a fully satisfactory response for all situations, andall of the models proposed have advantages and disadvantages.

As [START_REF] Miall | Motor Control, Biological and Theoretical[END_REF] suggested, it is likely that motor control is achieved through mechanisms compatible with several control theories depending on the situation.According to all these considerations, there is no unique solution whenexecuting a given task. If we extend this idea, in line withour interest in the field of occupational activities, we can hypothesise that workers have the choice betweena large panel of possible movements depending on the various solutions. This range of options can lead to intrinsic movement variability.

-INTRINSIC MOVEMENT VARIABILITY AND FACTORS INFLUENCING IT

The theories of motor control cited in the previous section can explain why intrinsic movement variability is present during task performance. This section reviews various experiments highlighting the presence of intrinsic movement variability, particularly during upper limb movements,based on various factors related either to the characteristics of the person, or of the task.

For the person's characteristics, [START_REF] Svendsen | Amount and structure of force variability during short, ramp and sustained contractions in males and females[END_REF] studied variabilityduring an elbow flexion endurance task in asymptomatic men and women. Theyobserved that forces produced by women were less variable than those produced by men. In her review, [START_REF] Côté | A critical review on physical factors and functional characteristics that may explain a sex/gender difference in work-related neck/shoulder disorders[END_REF] suggestedthatthe reducedmotor variability in women compared to men could partly explain why women are at higher risk of developing musculoskeletal symptoms, especially inthe neck / shoulder region.

The observations describedby [START_REF] Gaudart | Conditions for maintaining ageing operators at work--a case study conducted at an automobile manufacturing plant[END_REF] showed that younger workers have a more variable work cycle time than older workers. Younger workers alternatedperiods of relaxation with periods of accelerationmore than older workers. In contrast, Madeleine et al. (2008) demonstrated that experienced operators presented greater variability inwork cycle time even if the overall timewas shorter for novices. These authorshighlighted a greater kinematic variability, but lower variability in terms of muscular activity for more experienced workers. They explained that this reduction in variability of muscular activity could be the result ofthe implementation of specific motor programs. Moreover, with age, the characteristics of motor adaptation change, mainly due to alterations tomuscular capacities [START_REF] Zwart | Physical workload and the ageing worker: a review of the literature[END_REF][START_REF] Enoka | Muscle fatigue: what, why and how it influences muscle function[END_REF]. These capacities are generally diminished by progressive loss of the muscles'viscoelastic properties and the increase in recovery time following an effort. Movement variability can either increase due to accumulated fatigue, or decrease due to the reduction of adaptation capacities. In all cases, with age, the increase infatigue comes in addition tocumulativeexposure duration. Moreover, asreported by [START_REF] Gaudart | Conditions for maintaining ageing operators at work--a case study conducted at an automobile manufacturing plant[END_REF],operators are aware of their,more or less declining,physical and cognitive capacities. They develop their strategies based ontheir experience and their capacities.

Other studies show that the kinematics of movement can be modified with fatigue, for example during repetitive throwing [START_REF] Forestier | The effects of muscular fatigue on the coordination of a multi-joint movement in human[END_REF][START_REF] Huffenus | Effects of distal and proximal arm muscles fatigue on multi-joint movement[END_REF], hammering [START_REF] Côté | Differences in multi-joint kinematic patterns of repetitive hammering in healthy, fatigued and shoulder-injured individuals[END_REF][START_REF] Côté | Effects of fatigue on intermuscular coordination during repetitive hammering[END_REF] sawing [START_REF] Côté | Movement reorganization to compensate for fatigue during sawing[END_REF][START_REF] Gates | The effects of neuromuscular fatigue on task performance during repetitive goal-directed movements[END_REF] and during reaching movements [START_REF] Fuller | Posture-movement changes following repetitive motioninduced shoulder muscle fatigue[END_REF]Lomond and Côté, 2010). During repeated sawing movements,elbow angle amplitude is reduced and offset by a more advanced and lower position of the shoulder as well as an increase inshoulder, wrist and torso movement amplitudes [START_REF] Côté | Movement reorganization to compensate for fatigue during sawing[END_REF]. Furthermore, [START_REF] Gates | The effects of neuromuscular fatigue on task performance during repetitive goal-directed movements[END_REF] showed that these movement patterns were modified without affecting the production rate. Various studies have shown that the presence of temporal and/or spatio-temporal variability in muscular activity and posture can have a preventive effect on the onsetof fatigue [START_REF] Bosch | The effect of work pace on workload, motor variability and fatigue during simulated light assembly work[END_REF][START_REF] Côté | Differences in multi-joint kinematic patterns of repetitive hammering in healthy, fatigued and shoulder-injured individuals[END_REF][START_REF] Farina | The change in spatial distribution of upper trapezius muscle activity is correlated to contraction duration[END_REF][START_REF] Fuller | Posture-movement changes following repetitive motioninduced shoulder muscle fatigue[END_REF][START_REF] Van Dieën | Low-level activity of the trunk extensor muscles causes electromyographic manifestations of fatigue in absence of decreased oxygenation[END_REF]. [START_REF] Van Dieën | Low-level activity of the trunk extensor muscles causes electromyographic manifestations of fatigue in absence of decreased oxygenation[END_REF] showed that increased variability in the activity of the posterior extensor muscles reduced the development of fatigue. These authors suggested that persons who can alternate muscular activity between the different portions of the posterior extensor muscles will havegreater endurance. Along theselines, Palmerudet al.

(1998)suggested that the redistributionof load between the synergetic muscles of the shoulder could avoid or delay the onsetof muscular fatigue. Thus, people having a greaterability of motor variability can slow down or prevent the development of fatigue and are less sensitive to the risk of developingmusculoskeletal disorders [START_REF] Kilbom | Repetitive work of the upper extremity Part II: the scientific basis (knowledge base) for the guide[END_REF]Madeleine et al., 2008;[START_REF] Mathiassen | Diversity and variation in biomechanical exposure: what is it, and why would we like to know?[END_REF][START_REF] Mathiassen | Motor control and cardiovascular responses during isoelectric contractions of the upper trapezius muscle: evidence for individual adaptation strategies[END_REF]. Muscle fatigue and musculoskeletal discomfort are reported to be involved in initiating work-related musculoskeletal disorders (Madeleine, 2010). For workers performing a deboning activity, those with neck-shoulder discomfort showed smaller work cycle duration and extentof variability of the head-shoulder displacementthan those without discomfort.A greater extentof variability of the elbow-hip displacement was also noted for the former group. However,studies using surface electromyography have shown that musculoskeletal discomfort may or may not lead to changes in muscle activation (Ostensvik et al., 2009;[START_REF] Szeto | A comparison of symptomatic and asymptomatic office workers performing monotonous keyboard work-1: neck and shoulder muscle recruitment patterns[END_REF]Vasseljen andWestgaard, 1996, Westgaard et al., 2001). Madeleine et al. (2008) also highlighted that variability could be modified by the presence of pain. Acute pain generatesgreater motor variability than chronic pain, suggesting motor reorganisation.

These cross-sectional studies do not give consistent evidence for a positive effect of movement variability on musculoskeletal complaints. Prospective longitudinal studies involving large worker cohorts will be necessary to establish a cause-effect relationship between them.The existence of an optimal level of variability could also be questioned. For [START_REF] Stergiou | Optimal movement variability: a new theoretical perspective for neurologic physical therapy[END_REF], optimal variability is located between two limits. Above the upper limit, the system is too unstable and sensitive to perturbations. Below the lower limit, the system is too stereotyped and thus less able to adapt to perturbations. To go further, determining the limits of intrinsic movement variability for healthy workers could reveal the transition towards discomfort and pain, as suggested by Madeleine (2010) Work pace is usually determined by designer's recommendations or in accordance with production constraints.

These authors showed that increasing the work pace led to an increase in the variability of wrist speed and acceleration. This result is consistent with other studies showing that kinematic variability increases as the speed at which movements are performed increases [START_REF] Harris | Signal-dependent noise determines motor planning[END_REF]. In contrast, Srinivasan et al. (2015d) showed that motor variability in the shoulder and elbow decreased when the pace of repetitive work was increased. The apparent contradiction between the results presented by Srinivasan et al. (2015d), and[START_REF] Bosch | The effect of work pace on workload, motor variability and fatigue during simulated light assembly work[END_REF] could be due to different pace increases and errors in performance. Furthermore, Srinivasan et al. (2015b) showed that decreasing accuracy while simultaneously increasing pace in short-cycle repetitive work led to decreased motor variability in arm movements.

In their study, [START_REF] Bosch | The effect of work pace on workload, motor variability and fatigue during simulated light assembly work[END_REF] used kinematics and muscular activity measurements to characterisemovementvariability. Their results showed that variability in muscle activity did not differ as a function of work pace. Thus, depending on the techniques used, the presence of movementvariability is highlighted to a greater or lesser degree. However, visual observation or kinetics can also be used to analyse movement variability at work [START_REF] Gaudart | Conditions for maintaining ageing operators at work--a case study conducted at an automobile manufacturing plant[END_REF][START_REF] Gaudart | La baisse de la polyvalence avec l'âge : question de vieillissement, d'expérience, de génération?[END_REF][START_REF] Svendsen | Muscle coordination and force variability during static and dynamic tracking tasks[END_REF]. All these techniques provide various levels of information about the movement performed by the worker. Muscular activity measurements could inform about motor units within a muscle, a muscle subdivision or the whole muscle. Kinetics gives informationon the force exerted by the worker, resulting from the activity of several muscles. Kinematics and visual observation inform onhow each body segment is involved in amovement. These techniques provide sometimes convergent and sometimes divergent information [START_REF] Bosch | The effect of work pace on workload, motor variability and fatigue during simulated light assembly work[END_REF][START_REF] Gaudez | A better way of fitting clips? A comparative study with respect to physical workload[END_REF]. Thus, defining the appropriate technique or combination of techniques to characterise movement variability at work is a major issue before starting any investigation. The technique should be chosen in line with the information sought and the desired level of accuracy. Movement variability studies are also affected by anysignal processing performed and the descriptorsselected. Statistical descriptive magnitudes such as mean, standard deviation and coefficient of variance have often been used to analyse movement in work situations [START_REF] Winter | Biomechanics and motor control of human gait: normal, elderly and pathological[END_REF]. Fractal and entropy analyses, deterministic chaos and wavelet transform approaches resulting from the theories of non-linear systems, are other tools which may facilitate our understanding of the structure and origin of variability [START_REF] Cashaback | Muscle fatigue and contraction intensity modulates the complexity of surface electromyography[END_REF]Chau, 2001aChau, , 2001b;;[START_REF] Enders | Task-Oriented Control of Muscle Coordination during Cycling[END_REF][START_REF] Madeleine | Characterization of postural control deficit in whiplash patients by means of linear and nonlinear analyses -A pilot study[END_REF][START_REF] Madeleine | Level of self-reported neck/shoulder pain and biomechanical workload in cleaners[END_REF][START_REF] Samani | Advanced biofeedback from surface electromyography signals using fuzzy system[END_REF][START_REF] Stergiou | Human movement variability, nonlinear dynamics, and pathology: Is there a connection?[END_REF][START_REF] Svendsen | Muscle coordination and force variability during static and dynamic tracking tasks[END_REF]. For Madeleine (2010), methods developed in non-linear dynamics are of particular interest to assess the complexity and dimensionality of motor control. Finally, mathematical tools such as Dynamic Time Warping [START_REF] Muscillo | Classification of motor activities through derivative dynamic time warping applied on accelerometer data[END_REF], geometric invariants [START_REF] Schutter | Invariant Description of Rigid Body Motion Trajectories[END_REF][START_REF] Schutter | Recognition of 6 DOF Rigid Body Motion Trajectories using a Coordinate-Free Representation[END_REF] and other pattern recognition tools can be used to measure similarity between several repetitions of a movement and to categorise them [START_REF] Granata | Human activity understanding: a multilayer approach combining body movements and contextual descriptors analysis[END_REF]. A wide range of studies ofmotor variability have been conducted for different purposes, yet there is currently no consensus on the most relevant descriptors to use in one situation or another (Srinivasan et al., 2015b;Srinivasan et al., 2015c).

Indeed, movement variabilitymay be difficult to comprehend since in a given condition some descriptors can indicate an increase in movement variability while others indicate a decrease (Srinivasan et al., 2015b).

Moreover, when it comes to integrating movement variability intoworkstation design, the descriptors chosen must be usable in design tools.

All the studies cited in this sectionshow that many factors contribute to intrinsic variability. These factors can be linked to the person's physiological state (gender, age,presence of pain, fatigue or discomfort, or prevention of their onset), to their expertise and to the characteristics of the task to be performed. Unlike personal factors, factors related to task characteristics can be manipulated by designers. However, all factorsare interconnected, superimposed and difficult to distinguish during work activities. Therefore, identifying the various factors influencing movement variability is a prerequisitefor any research into intrinsic movement variability. Indeed, depending on the characteristics of the populationstudied, these personal factors may be different for each subjectin thepopulation.On top of this, the working environment could also add somesources of variability.

-INTRINSIC MOVEMENT VARIABILITY AND DESIGN ENGINEERING

The previous sections of this review show that intrinsic movement variability is a natural characteristic of human movements and activities. This is particularly important in the field of workstation design, since inadequate design choices may have harmful consequences on future operators' health and safety. For instance, operators working at an unsuitable workstation may suffer from muscle fatigue and musculoskeletal discomfort, potentially leading to work-related musculoskeletal disorders (Madeleine, 2010). Hence, an efficient occupational risk prevention approach must account for operators' real activity, and this approach should be applied as early as possible in the design process, as stated in the general principles for design (CEN 12100, 2011). In Europe, workstation designers must comply with the Directive on Machinery 2006/42/CE (European Union, 2006) and its associated standards (CEN 1005(CEN -4, 2009;;CEN 1005CEN -5, 2007)). These regulations raise a majorquestion as to how well intrinsic movement variability is known or taken into consideration in the field of workstation design today.

Over the last twenty years, the design process has changed radically to better take into account new industrial constraints such as reduced product life-cycles, rapidly evolving markets and customers'demands for more personalised products. In this context, simulation software tools todesign and plan products and industrial processeshave led to the concept of the "digital factory" (Arndt, 2006),and have progressively become predominant in manufacturing industrydue to advances made in computer technology [START_REF] Claudon | Prise en compte de l'ergonomie lors de l'utilisation de systèmes de temps prédéterminés[END_REF].Among these tools, virtual reality (VR) and digital manikins,also known as digital human models (DHM), have made significant progress. VRhas been used for years in manufacturing industries, and numerous application cases can be found in the literature. This toolallows designers to immerse human operators in virtual environments simulating future workstations in order to validate their design and assess their ergonomics [START_REF] Pontonnier | Ergonomics and Virtual Reality: VISIONAIR Project examples[END_REF]. DHMs allow designers, engineering offices [START_REF] Haesen | Betterlift -Introducing a semi-automatic exhaust manipulator to reduce a high absenteeism rate[END_REF] and consultants (Urbatic, 2007) to represent and place virtual operators in situations with given characteristics,to assessthe future constraints of a work situation (anthropometry, reaching zones, physical performances, time analysis). By using DHM, a workstation designer can define and assessthe movements of the future operator. Even before the production of physical prototypes of the future workstation, VR and DHM can contribute from the design phase to the application of the safety principles set out by the abovementioned Directive on Machinery and its associated standards. These tools can also be used as a means of communication between the different stakeholders in a project,including designers, engineers, health and safety prevention officers, users and decision-makers.Thus,these tools can helpto design more ergonomic workstations [START_REF] Falck | What are the obstacles and needs of proactive ergonomics measures at early product development stages? -An interview study in five Swedish companies[END_REF].

However,these tools do not yetallow designersto take movement variability into account. As VR relies upon human operators, ittakes movement variability into account to a certain extent. Nevertheless, ergonomic studies based on VR are not fully reliable because interaction devices modify postures and motions [START_REF] Pontonnier | Assessing the ability of a VR-based assembly task simulation to evaluate physical risk factors[END_REF], and force-feedback devices cannotrender real exertions,which is an issue for ergonomics studies. Digital manikins, in contrast, incorporate ergonomic indicators based on the analysis of static postures, andthus cannot fully take the overall movement performed by anoperatorinto account,although doing sowould improve the precision of the ergonomic evaluation (Andreoni et al., 2009;Andreoni et al., 2011). DHM are also incompatible withthe incorporation ofpotentialvariants in movement, although it has been shown that the presence of obstacles impeding the subject's trajectories modifiesmovement variability [START_REF] Jacquier-Bret | Analyse biomécanique du mouvement de préhension contraint et altéré : indices quantitatifs de la gestion de la redondance motrice[END_REF]. Moreover, users and publishers of this type of software are not very aware of this aspect of occupational activity.Indeed, they usually hypothesise that there is one "best way" to achieve tasks, which will be the same for all workers, regardless of gender, age, experience or state of fatigue. As the main objective of current forms of production organisation in industrial companies is to ensure productivity and quality, workstation designers attempt to define a single succession of postures and movements to be performed by the operator to optimise production and/or safety criteria, without offering any alternative. This results in highly prescriptive operating procedures with respect to the order of operations, how they are to be performed, and the time allocatedto each step of the task. The expected movement is thus standardised.

In spite of this standardised prescription, differences still exist between the movement prescribed by designers and the movements actuallyexecuted by operators [START_REF] Authier | Handling Techniques: The Influence of Weight and Height for Experts and Novices[END_REF][START_REF] Pw | The nature of work-related neck and upper limb musculoskeletal disorders[END_REF][START_REF] Chassaing | Vers une compréhension de la construction de la gestuelle des tôliers d'une entreprise automobile[END_REF][START_REF] Kilbom A Persson | Work technique and its consequences for musculoskeletal disorders[END_REF][START_REF] Mathiassen | Diversity and variation in biomechanical exposure: what is it, and why would we like to know?[END_REF][START_REF] Ohlsson | Disorders of the neck and upper limbs in women in the fish processing industry[END_REF][START_REF] Putz-Anderson | Cumulative Trauma Disorders, a manual for the musculoskeletal diseases of the upper limb[END_REF][START_REF] Roquelaure | Occupational and personal risk factors for carpal tunnel syndrome in industrial workers[END_REF][START_REF] Silverstein | Hand wrist cumulative trauma disorders in industry[END_REF][START_REF] Sluiter | Criteria document for evaluating the work-relatedness of upper-extremity musculoskeletal disorders[END_REF].Indeed, movement variability is ever-present in occupational life [START_REF] Madeleine | Changes in the amount and structure of motor variability during a deboning process are associated with work experience and neck-shoulder discomfort[END_REF][START_REF] Mathiassen | Diversity and variation in biomechanical exposure: what is it, and why would we like to know?[END_REF][START_REF] Mathiassen | Variability in mechanical exposure within and between individuals performing a highly constrained industrial work task[END_REF][START_REF] Van Dieën | Low-level activity of the trunk extensor muscles causes electromyographic manifestations of fatigue in absence of decreased oxygenation[END_REF]. In the area of sport, [START_REF] Bartlett | Is movement variability important for sports biomechanics[END_REF] showed that there was probably no single movement that optimises the performance of a given task. Therefore, they felt it was logical to allow athletes to explore a number of possible solutions, rather than to limit the movement to the oneindicated by the coach. Similarly, it appears relevant to propose a number of possible movements in occupational situations. Today, the design of work situations consists in a single operating procedure that operators must follow, whereas it appears preferable to provide them with several possible working procedures.

Up to now, the only way to take intra-and inter-operator motor variability into account in workstation design was to increasethe number of simulations.With DHM, this option is difficult to apply due to the additional workload it implies. Indeed, using digital manikins is complex and very time-consuming. DHM simulations are generally made subjectively by the designer, using a computer keyboard and mouse. Various paths have been explored to facilitate this animation task:using optimisation algorithms [START_REF] Chaffin | Development of Computerized Human Static Strength Simulation Model for Job Design[END_REF][START_REF] Zhang | Using artificial neural networks for human body posture prediction[END_REF],experimental data from movement analyses [START_REF] Fritzsche | Introducing EMA (Editor for Manual work Activities) -A New Tool for Enhancing Accuracy and Efficiency of Human Simulations[END_REF][START_REF] Wang | Trapezius muscle activity as a risk indicator for shoulder and neck pain in female service workers with low biomechanical exposure[END_REF],more intuitive computer interfaces [START_REF] Yoshizaki | An actuated physical puppet as an input device for controlling a digital manikin[END_REF],and the automated translation of a codified operating procedure into postures and movements [START_REF] Claudon | Prise en compte de l'ergonomie lors de l'utilisation de systèmes de temps prédéterminés[END_REF][START_REF] Kuo | Motion generation from MTM semantics[END_REF].More recently, works in the area of humanoid robotics have used command laws based on some characteristics of human movementand motor control toanimate digital manikins that also comply with mechanical laws [START_REF] Collette | Commande dynamique d'humains virtuels : équilibre robuste et gestion des tâches[END_REF][START_REF] De Magistris | Dynamic virtual manikin control design for the assessment of the workstation ergonomy[END_REF][START_REF] Mansour | A computational approach for push recovery in case of multiple noncoplanar contacts[END_REF].For example, as mentioned earlier, De Magistris' DHM controller [START_REF] De Magistris | Dynamic control of DHM for ergonomic assessments[END_REF]relies on Fitt's law, Todorov's speed profiles model, minimum jerk optimisation and minimum exertion. However, these developments can only be usedto simulate a unique movement to perform the task and none of them hasyet been integrated into digital manikin software applications available to designers.Indeed, movement variability remains an element which is given little attention in workstation design and is completelyignored in DHM.

-A BETTER UNDERSTANDING OF MOVEMENT VARIABILITYFOR IMPROVED INTEGRATION INTO WORKSTATION DESIGN

The first three parts of this review defined and described the concept of intrinsic movement variability. As shown, this concept mainly depends on personal and task characteristics.BecauseEuropean designers have an obligation to minimise risks at the workplace, intrinsic movement variability should be considered both reliably and early in design tools to be closer to real activities/tasks performed by an operator in a work situation.

However, as presented in the previous section, designers are not sufficiently aware of movement variability, and current design tools lack appropriate features. Hence, a major challenge will be to enrich design tools so that they can simulate different ways in which a task can be performed by taking intrinsic movement variability into account. This will be no easy feat, and understanding and integrating movement variability into the workstation design process will likely be a long and ambitious task. Indeed, in most recent studies, movement variability was only approachedthrough the comparison of two states, e.g.women vs.men [START_REF] Svendsen | Amount and structure of force variability during short, ramp and sustained contractions in males and females[END_REF] or novices vs.experts (Madeleine et al. 2008). These studies set the background for movement variability analysis. Their results showed the advantagesof focusing on what was long considered irrelevant noise. They allowed great stridesin particular in research into the factors influencing this variability and the most relevant descriptors and signal processing to characterisevariability.

However, we need a complementary approach based on the same literature.Intrinsic movement variability should be considered holistically, integratingas wide a range as possible ofpersonal factorsthat could be encounteredwhenperformingatask. Moreover, some characteristics and measures of intrinsic movement variability of a task should be studied. This approach could be related to what was suggested by [START_REF] Luger | The influence of task variation on manifestation of fatigue is ambiguousa literature review[END_REF] regarding "natural movement variation" and workload exposure. They stated that "one should be aware of the amount of natural variation already present in tasks as these generally do not solely consist of one single movement" [START_REF] Luger | The influence of task variation on manifestation of fatigue is ambiguousa literature review[END_REF]. In the same way, one should be aware of the existence of intrinsic movement variability. This knowledge could ultimately be valuable for design tools.

Thus, it appears necessary to characterise intrinsic movement variability in several workers as they perform a range of repetitive tasks. This characterisation woulddefine all the possible movements that can be performed to completeatask without distinguishing personal features. Then, the goal wouldbe to simulate this envelopeof movementsin design tools, which will requirethe development of models and algorithms. An important contribution to this goalwill be to identify relevant and coherent descriptors to be used both for movement variability analysis and for simulation of future occupational situations.

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  ,and could help to identify early warning signs indicating the development of work-related musculoskeletal disorders. So, workercharacteristics -such as gender, age, novice/expert status, presence of fatigue, discomfort or paininfluence intrinsic movement variability.When designingworking equipment, designers should ensure that any operator should be able to work at his/her workstation, whatever his/her characteristics. In terms of task characteristics, Srinivasan et al. (2015a) studied the effects of additional concurrent cognitive demands on repetitive and precision arm movements. They found no changes in shoulder and elbow movement variability between thephysical condition and the condition with additional cognitive demands.Bosch et al. (2011) compared movementvariability during simulated light assembly work performed at different work paces.

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