Micaela Schmid
email: micaela.schmid@fsm.it

Alessandra Bottaro

Stefania Sozzi

Marco Schieppati

Professor Peter J Beek

Adaptation to continuous perturbation of balance: Progressive reduction of postural muscle activity with invariant or increasing oscillations of the center of mass depending on perturbation frequency and vision conditions

Keywords: Motor processes, Adaptation, Vision, Dynamic postural control, EMG

We investigated the adaptation of balancing behavior during a continuous, predictable perturbation of stance consisting of 3-min backward and forward horizontal sinusoidal oscillations of the support base. Two visual conditions (eyes-open, EO; eyes-closed, EC) and two oscillation frequencies (LF, 0.2 Hz; HF, 0.6 Hz) were used. Center of Mass (CoM) and Center of Pressure (CoP) oscillations and EMG of Soleus (Sol) and Tibialis Anterior (TA) were recorded. The time course of each variable was estimated through an exponential model. An adaptation index allowed comparison of the degree of adaptation of different variables. Muscle activity pattern was initially prominent under the more challenging conditions (HF, EC and EO; LF, EC) and diminished progressively to reach a steady state. At HF, the behavior of CoM and CoP was almost invariant. The time-constant of EMG adaptation was shorter for TA than for Sol. With EC, the adaptation index showed a larger decay in the TA than Sol activity at the end of the balancing trial, pointing to a different role of the two muscles in the adaptation process. At LF, CoM and CoP oscillations increased during the balancing trial to match the platform translations. This occurred regardless of the different EMG patterns under EO and EC. Contrary to CoM and CoP, the adaptation of the muscle activities had a similar time-course at both HF and LF, in spite of the two frequencies implying a different number of oscillation cycles. During adaptation, under critical balancing conditions (HF), postural muscle activity is tuned to that sufficient for keeping CoM within narrow limits. On the contrary, at LF, when vision permits, a similar decreasing pattern of muscle activity parallels a progressive increase in CoM oscillation amplitude and the adaptive balancing behavior shifts from the initially reactive behavior to one of passive riding the platform. Adaptive balance control would rely on on-line computation of risk of falling and sensory inflow, while minimizing balance challenge and muscle effort. The results from this study contribute to the understanding of plasticity of the balance control mechanisms under posture-challenging conditions.

Introduction

Contrary to common belief, bipedal stance is remarkably stable. Falls never occur during quiet standing, in the absence of autonomous failure [START_REF] Gupta | Neurogenic orthostatic hypotension: Chasing "the fall[END_REF]. Likewise, falls rarely if ever occur in able-bodied subjects during everyday activities, even when these imply complex movements. Of course balance can be overthrown by slips or postural perturbations. But, within limits, the body usually reacts with a variety of stabilizing responses that render upright stance a robust attractor [START_REF] Stirling | Stability and the maintenance of balance following a perturbation from quiet stance[END_REF]. As a result, balance is preserved under many challenging conditions. The way the body learns to stand up and walk in the first year or life [START_REF] Hadders-Algra | Development of postural control during the first 18 months of life[END_REF] and the capacity of the nervous system for compensating the consequences of lesions [START_REF] Geurts | A review of standing balance recovery from stroke[END_REF][START_REF] Lacour | Restoration of vestibular function: Basic aspects and practical advances for rehabilitation[END_REF] are the extremes of our day-to-day capacity of coping with new environmental situations, by learning the most efficient ways to adapt to postural challenges and by attaining the goal to minimize effort [START_REF] Orrell | Implicit motor learning of a balancing task[END_REF]). The nervous system has the capacity to compensate for failure of some of its central or peripheral components in allowing efficient (albeit not optimal) posture and movement.

Ageing has a profound influence on postural adjustments to stance perturbation, especially in the presence of sensory conflicts [START_REF] Bugnariu | Aging and selective sensorimotor strategies in the regulation of upright balance[END_REF]. Under appropriate conditions, however, subjects are able to recalibrate and adapt to the changes, and improve balance capacity.

Coordinated control of body segments is a complex aspect of motor behavior, owing to the multitude of degrees of freedom of the controlled system [START_REF] Johansson | Human postural dynamics[END_REF][START_REF] Johansson | Optimal coordination and control of posture and movements[END_REF]. This is particularly evident during maintenance of equilibrium, where different coordination modes might be employed depending on posture dynamics and sensory feedback. When the implicit requirement is to keep the body and its center of mass within the boundaries of the support surface [START_REF] Schieppati | The limits of equilibrium in young and elderly normal subjects and in parkinsonians[END_REF], subjects are able to select their postural behavior from a repertoire of strategies [START_REF] Gorgy | Coordination of upper and lower body during balance recovery following a support translation[END_REF][START_REF] Torres-Oviedo | Muscle synergies characterizing human postural responses[END_REF], innate or learned trough experience. This adaptive selection may imply a time-consuming process based on the internal representation of the body dynamics, taking into account the constraint of the new demand and the relevant sensory inflow.

Motor adaptation to balance-challenging perturbations occurs with the repetition of successive, separate balancing trials [START_REF] Bhatt | Adaptive control of gait stability in reducing slip-related backward loss of balance[END_REF][START_REF] Hu | Multisensory training of standing balance in older adults: I. Postural stability and one-leg stance balance[END_REF][START_REF] Keshner | Postural coactivation and adaptation in the sway stabilizing responses of normals and patients with bilateral vestibular deficit[END_REF]Ledin et al., 1990Ledin et al., -1991;;[START_REF] Mummel | Postural responses to changing task conditions in patients with cerebellar lesions[END_REF][START_REF] Perrin | Training improves the adaptation to changing visual conditions in maintaining human posture control in a test of sinusoidal oscillation of the support[END_REF] and can be partly transferred to new perturbation conditions [START_REF] Bhatt | Generalization of gait adaptation for fall prevention: From moveable platform to slippery floor[END_REF]. Adaptation of the balancing strategies also occur during a sequence of discrete, repeated trials of support-surface translations, with manipulations of oscillation frequency, amplitude and vision [START_REF] Buchanan | Emergence of postural patterns as a function of vision and translation frequency[END_REF][START_REF] Fujiwara | Postural control adaptability to floor oscillation in the elderly[END_REF][START_REF] Van Ooteghem | Compensatory postural adaptations during continuous, variable amplitude perturbations reveal generalized rather than sequence-specific learning[END_REF]van Ooteghem, Frank, & Horak, 2010). [START_REF] Buchanan | Emergence of postural patterns as a function of vision and translation frequency[END_REF] briefly pointed to adaptation in the oscillation of the CoP, which decreased in amplitude with repeated exposures to separate, successive trials at frequencies greater than 0.5 Hz, and observed no difference in the adaptation to such a pattern for different vision conditions. [START_REF] Fujiwara | Postural control adaptability to floor oscillation in the elderly[END_REF] showed a similar decrease on CoP velocity of repeated oscillation trials of one minute each, separated by short breaks. By using variableamplitude perturbations of the support base, [START_REF] Van Ooteghem | Compensatory postural adaptations during continuous, variable amplitude perturbations reveal generalized rather than sequence-specific learning[END_REF][START_REF] Van Ooteghem | Practice-related improvements in posture control differ between young and older adults exposed to continuous, variable amplitude oscillations of the support surface[END_REF] showed improvement of balance with practice, by shifting from an ankle strategy towards a multi-segmental control strategy, allowing balancing with greater energy efficiency. Under different conditions featuring repetitive linear trunk and head accelerations of seated subjects, [START_REF] Blouin | Attenuation of human neck muscle activity following repeated imposed trunk-forward linear acceleration[END_REF] found that the neck muscle activity progressively diminished across trials. [START_REF] Dietz | Human stance on a sinusoidally translating platform: Balance control by feedforward and feedback mechanisms[END_REF] described an adaptation process, observed during the administration to standing subjects of variable body accelerations produced by changing the frequency of oscillation of a supporting treadmill. These investigations pointed to the operation of adaptation processes, which occurred regardless of the various trials being randomized or segments of perturbations being repeated within a series of variable cycles.

The present investigation was based on the assumption that a prolonged postural disturbance, challenging the balance control mechanisms by continuous and predictable oscillations of the support surface, would be an appropriate tool for investigating the characteristics of the timevarying continuous adaptation process of balancing behavior. This type of postural disturbance produces, under steady-state conditions, different balancing strategies depending on vision and frequency of oscillation [START_REF] Buchanan | Emergence of postural patterns as a function of vision and translation frequency[END_REF][START_REF] Cappa | Effect of changing visual condition and frequency of horizontal oscillations on postural balance of standing healthy subjects[END_REF][START_REF] Corna | Standing on a continuously moving platform: Is body inertia counteracted or exploited[END_REF], which are largely controlled by anticipatory mechanisms (Dietz et 6 al., 1993;[START_REF] Fujiwara | Development of adaptability of postural control during floor vibration[END_REF][START_REF] Fujiwara | Postural control adaptability to floor oscillation in the elderly[END_REF][START_REF] Horak | Central programming of postural movements: Adaptation to altered support-surface configurations[END_REF][START_REF] Schieppati | Variability in a dynamic postural task attests ample flexibility in balance control mechanisms[END_REF].

Clearly, the steady-state must be reached after some time from the onset of the cyclic perturbation.

However, the within-trial trend in the kinematic balancing pattern associated with the continuous effort of equilibrium maintenance has never been systematically examined. By the same token, no information exists as to the pattern of EMG activity throughout a prolonged perturbation. This would have relevance for understanding the time constraints necessary for optimizing the balancing behavior and counteracting the risk of falling. It would also give a template against which to evaluate possible sensori-motors disorders in postural steadiness and in the compliance with balance-perturbing conditions (e.g., [START_REF] Morton | Cerebellar control of balance and locomotion[END_REF].

In order to gain further insight into the adaptation to repeated postural perturbations, we examined in detail the features and the time-course of the changes in dynamic, kinematic and EMG variables during balancing on a continuously backwards and forwards translating platform. We hypothesized that the adaptation strategy would imply keeping the body's center of mass within safe limits by appropriately modifying both body kinematics and postural muscle effort, and that a relation existed between these changes [START_REF] Peterka | Sensorimotor integration in human postural control[END_REF][START_REF] Schieppati | Variability in a dynamic postural task attests ample flexibility in balance control mechanisms[END_REF][START_REF] Scholz | Motor equivalent control of the center of mass in response to support surface perturbations[END_REF]. We also hypothesized that adaptation was affected by the visual condition and by the frequency of platform translation.

Whether vision modulates postural adaptation is still an open issue. Under dissimilar balancing conditions (quiet standing), forward shift of center of foot pressure and reduction of body sway are the counterparts of the increased balance capacity as a consequence of learning [START_REF] Tarantola | Human stance stability improves with the repetition of the task: Effect of foot position and visual condition[END_REF]. Notably, however, a decrease in body oscillation with trial repetition only occurred when vision was not available [START_REF] Tarantola | Human stance stability improves with the repetition of the task: Effect of foot position and visual condition[END_REF], suggesting that vision selects the optimal balancing strategy, hardly requiring further adjustments with trial repetition. Others have shown that the stance-perturbing effect of a non-predictable pseudorandom vibration of the calf muscles was larger without than with vision, as was the rate of attenuation of these effects (Patel, Gomez, Lush, & Fransson, 2009). Vision seems instead not to affect the development of adaptation of successive stabilizing responses to cyclic tilts of the support surface [START_REF] Keshner | Postural coactivation and adaptation in the sway stabilizing responses of normals and patients with bilateral vestibular deficit[END_REF]. Possibly, vision calibrates the fine postural adjustments occurring during quiet stance [START_REF] Schieppati | The limits of equilibrium in young and elderly normal subjects and in parkinsonians[END_REF], while abrupt head displacements during stance perturbations make vision unreliable for postural adjustments. Therefore, we also addressed the effect of vision on adaptation: contrary to the above proposition, we would expect visual information to be able to speed up and improve the adaptation process. Availability or suppression of vision can and does in fact readily change the pattern of balancing on a continuously oscillating support surface even when the change in visual condition occurs at the time steady-state behavior is attained (De Nunzio, Nardone, Schieppati, 2007;De Nunzio & Schieppati, 2007).

We also compared the spatio-temporal features of the presumed adaptation process between 'demanding' and 'easy' conditions, in the assumption that spatio-temporal features of adaptation would be modulated by task difficulty. Two frequencies of platform translations were used, as low (LF) and high (HF) platform translation frequencies produce a different balancing behavior under steady-state condition. At HF, when the perturbations are more challenging, CoM oscillations are narrow. At LF, CoM oscillates like the platform and CoP back and forth oscillations are smaller than at HF, featuring a 'passive' riding of the platform by the body [START_REF] Buchanan | Emergence of postural patterns as a function of vision and translation frequency[END_REF][START_REF] Corna | Standing on a continuously moving platform: Is body inertia counteracted or exploited[END_REF]. Is riding 'passive' from the beginning at LF, and are the CoM oscillations small from the beginning at HF, or are these features the outcome of an adaptation process? Further, the same duration of the balancing trial at the two frequencies (therefore a different number of oscillation cycles within the same time-period), would allow to conjecture about the potential contribution to adaptation of the amount of repeated perturbing events or of the time elapsed from the beginning of the postural challenge.

Methods

Subjects

Ten healthy subjects volunteered to participate in the study (8 females and 2 males, mean age 27 ± 5.4 SD). All subjects gave written informed consent according to the Declaration of Helsinki and the guidelines of the local ethics committee. All subjects were naïve to the experimental procedures.

Procedure

Subjects rode a mobile platform (Lomazzi & Co., Italy), periodically translating in the anteroposterior (A-P) direction on the horizontal plane in a sinusoidal fashion, along the subjects' sagittal plane. Two different visual conditions (eyes closed, EC; eyes open, EO) and translation frequencies were imposed (high frequency, HF, 0.6 Hz; low frequency, LF, 0.2 Hz) by an ad-hoc software (e-TT, Italy). The amplitude of platform translation was 10 cm for all conditions. Subjects stood on the platform in a natural position (feet spaced about 10 cm), with the arms by their side, and kept their head in the primary position. After one short-lasting tryout for familiarizing with the platform, the experiment was started. Subjects were instructed to rest with both feet on the platform and not to make steps in reaction to the perturbations unless necessary to avoid falls. The protocol consisted of 4 trials for each subject, presented in the same experimental session: two trials at HF and two at LF oscillation frequency, each frequency comprising one EC and one EO trial. The trials were randomized across subjects. No more trials per condition per participant were included, in order to prevent specific memories of previous adaptations. Each trial lasted 5 min. It was made of three consecutive parts: subjects stood quietly on the still platform for 1 min; then, the platform translation started and subjects balanced on the platform for 3 min; finally, the platform stopped and subject returned to quiet stance and stood on the still platform for another 1 min. The same visual condition was maintained throughout each trial. The trial did not start until the subjects indicated they were ready to begin. Subjects were aware of the onset of the acquisition, but no warning was given as to the onset or termination of the platform translation cycles within the acquisition epoch. The four trials with different vision and frequency conditions were separated by a time-interval from 1 to several minutes during which subjects were free to move.

Data recording

Kinematic data were acquired by means an optoeletronic device (BTS, Italy). Twenty reflective markers were placed bilaterally on the following body landmarks for CoM computation: vertex and lateral head, forehead, acromion, L5, anterior superior iliac spine, greater trochanter, lateral condyle, lateral malleolus, metatarsal distal phalanx, calcaneum. The markers' position in space was recorded by eight cameras at a sampling frequency of 100 Hz and sent to a PC for off line analysis. Kinetic data were acquired by means a pair of sensorized insoles (Pedar Mobile system, Novel, Germany) that measured the plantar pressure distribution and the center of pressure position (CoP) of each foot. These data were sampled at 100 Hz frequency and transmitted to the PC by using Bluetooth technology. Muscle activity was recorded by using a telemetric system (TelEMG, BTS, Italy). The surface electrodes were placed on the belly of the Soleus (Sol) and Tibialis Anterior (TA) muscle of the right side of the body. EMG signals were preamplified (5000x), band-pass filtered (10-600 Hz) and sampled at 1000 Hz.

Data analysis

Standing before the beginning of the platform translation

The anthropometric data of each subject were collected and the position in space of the body's center of mass (CoM) computed from the marker traces by means of a custom-made software. For each trial, the mean A-P position of CoM during the first quiet-stance period was computed. In order to average the data from different subjects, the position of the CoM was referred to the position of the malleolus' marker (CoM A-P position minus malleolus A-P position). The standard deviation (SD) of the CoM position was an estimate of the oscillation amplitude around its mean position during the same period. To compute the net A-P CoP instantaneous position from the insole data, home-made software was implemented, based on the formula below (where F, vertical force; l, left foot; r, right foot). Then, CoP position and oscillation were quantified by the same procedure used for CoM. Each pre-processed EMG trace of TA and Sol muscles, for every subject and trial, was rectified and filtered with a low pass Butterworth filter with a cut-off frequency of 50 Hz to obtain the EMG envelope. The data collected during quiet stance allowed to verify that the initial body postural conditions (body oscillation and orientation in space) of each trial were the same, in order to legitimate the comparison between the data collected during the balancing trials performed under different frequency and vision conditions.

Pattern of adaptation during the oscillation period

The peak-to-peak (P-P) CoM and CoP A-P oscillations were computed for each platform translation cycle. This was the difference between the relative minimum and the subsequent relative maximum value within each cycle. CoM and CoP peak-to-peak oscillations and the EMG activity have been interpolated with an exponential function (y = A + B e -t/τ

), which has often been considered appropriate for descriptive assessment of learning or adaptation over time [START_REF] Franklin | Adaptation to stable and unstable dynamics achieved by combined impedance control and inverse dynamics model[END_REF][START_REF] Heathcote | The power law repealed: The case for an exponential law of practice[END_REF][START_REF] Mazur | Learning as accumulation: A re-examination of the learning curve[END_REF][START_REF] Newell | Adaptation and learning: Characteristic time scales of performance dynamics[END_REF]. The A, B and τ parameters were computed by using the minimum sum squared algorithm. This was achieved with the iterative conjugate gradient method of the Excel Solver utility (see [START_REF] Sozzi | Sensorimotor integration during stance: Time-adaptation of control mechanisms on adding or removing vision[END_REF]. These parameters gave an estimation of the variable values at the beginning of the trial (the intercept value with the ordinate at time zero, equal to A+B) and at the steady state (the asymptotic value, A), and of the time-decay of the function (the time constant, τ). Since the first oscillation cycle contains a large EMG response connected to the complex reflex response elicited by the abrupt, unexpected onset of the platform displacement (see Allum, Tang, Carpenter, Oude Nijhuis, & Bloem, 2011;[START_REF] Oude Nijhuis | First trial postural reactions to unexpected balance disturbances: A comparison with the acoustic startle reaction[END_REF], and since such response is of a different nature than the following EMG activity (reflex vs. anticipatory) (see [START_REF] Siegmund | Does startle explain the exaggerated first response to a transient perturbation?[END_REF], for all variables the interpolation procedure has been performed on the entire duration of the oscillation period except for the time interval corresponding to the duration of the first cycle. This procedure is exemplified in Fig. 1 for the EMG envelope. A possible limitation of this analysis depends on our fitting of the variable profiles with a single best-fit exponential curve, instead of using a set of exponential curves, each characterized by its time constant [START_REF] Franklin | Adaptation to stable and unstable dynamics achieved by combined impedance control and inverse dynamics model[END_REF], which would allow determining if the postural adaptation process is a set of co-existing distinct processes that evolve with different time scales [START_REF] Newell | Adaptation and learning: Characteristic time scales of performance dynamics[END_REF].

The adaptation index

For the P-P of CoM and CoP oscillations and for the EMG envelope, an arbitrary adaptation index was also computed in order to allow comparison in the adaptation behavior between variables of different nature. This index highlights the difference between the initial value of the variable and the value attained at the end of the perturbation period (EMG activity or oscillation amplitudes of kinetic and kinematic measures), in order to give an estimation of the entity of the change due to the continuous repetition of the perturbing stimulus. This was defined as the value of the fitted exponential curve at time t = 180 s, divided by the value at time zero (intercept value, A+B). An index < 1 indicated that the variable value was smaller at the end than at the onset of the balancing trial; vice versa for an index > 1.

Statistical analysis

Standing before the beginning of the platform translation

The TA and Sol EMG data were analysed separately with 2-way ANOVA (within frequency and vision condition). The CoM and CoP mean positions were analysed with 3-way ANOVA (within variable, frequency and vision condition). The frequency condition was added to the ANOVA design, even if these data were collected before platform translation, in order to check the consistency of the initial setting across the stance periods belonging to all subsequent balancing periods. The same ANOVA design was used to compare the mean CoM and CoP traces.

Pattern of adaptation during the oscillation period 12

Three-way ANOVA was computed to compare the τ value of each EMG, kinematic and kinetic variables under the different frequency and vision conditions. For the intercept values, a 2-way ANOVA (frequency and vision) was computed for TA and Sol, separately. The same analysis was performed for the asymptotic values. The intercept values of the exponential curve fitting CoM and CoP P-P oscillations were compared with a 3-way ANOVA (variables, frequency and vision condition). A similar analysis was performed for the asymptotic values.

The adaptation indexes for all EMG, kinematic and kinetic variables were compared by a 3-way ANOVA (within variables, frequency and vision). When the results of the ANOVA were significant (p < .05), the Newman-Keuls post-hoc test was run. The software package Statistica (StatSoft, USA) was used.

Results

Standing before the beginning of the platform translation

All data referring to this initial period are summarized in Table 1. Before the onset of the platform translations, the Sol showed a tonic activity revealing the postural function of the muscle; the activity of the TA was irregular, as normally observed during quiet stance. As expected, ANOVA (within frequency and vision) on the TA mean area showed no significant differences between frequency and vision conditions. Similar results were obtained for the mean area of Sol activity.

CoM and CoP mean positions showed no differences between frequency and vision conditions.

Conversely, the SD of the CoP was always significantly greater than that of CoM (see Table 1).

Vision affected the amplitude of the CoM and CoP oscillations. With EC the oscillations of both variables were significantly larger than that with EO (see Table 1), as repeatedly shown (e.g., [START_REF] Seigle | Dynamical structure of center of pressure fluctuations in elderly people[END_REF]. and CoP displacements (G-H), while balancing on the translating platform at 0.6 Hz (HF, left column) and 0.2 Hz (LF, right column) with EC. The traces of the malleolus marker, corresponding to the platform displacement, are also depicted (I-L). TA activity shows an exponential trend, regardless of the frequency condition. The strong initial activation rapidly decreases to reach a steady state value. Sol activity also shows an exponential trend, but its time decay is slower than that of TA. CoM oscillations remain roughly the same at HF; at LF, an increase of the oscillations from the onset to the end of the trial period is evident. Further, CoM oscillations are larger at LF than HF. CoP oscillations show an opposite behavior, being larger at HF than LF.

Pattern of adaptation during the oscillation period

The effects of frequency and vision

During the initial cycles of the oscillation period, both muscles were strongly activated, but this activation progressively diminished to reach a steady state. The evolution in time of the EMG activity and of the peak-to-peak (P-P) oscillations of CoM and CoP has been described by fitting the data with an exponential model (see Methods). Then, the mean coefficients defining the curves were considered in order to assess possible differences in the pattern of change of the different variables.

The mean intercept values of TA (Fig. 3, upper panel) showed differences between visual conditions, F(1, 9) = 15.77, p < .05. Regardless of the frequency, the initial activity of TA was higher with EC than EO (p < .05). Sol showed no differences between frequency, F(1, 9) = 1.17, p > .1, and vision conditions, F(1, 9) = 3.6, p > .1. CoM and CoP P-P oscillations (Fig. 3,upper panel) showed differences between variables, F(1, 9) = 131.28, p < .0005, and frequency conditions, F(1, 9) =19.07, p < .005; the interaction between variables and frequency was significant, F(1, 9) = 26.65, p < .0005. The initial oscillation of the CoP was always greater than that of CoM (p < .05, all comparisons, except for EO 0.2 Hz, p > .1). CoP values were affected by both vision and frequency.

CoP oscillations were greater EC than EO (p < .05, all comparisons), and greater at HF than at LF (p < .005 for all comparisons). Conversely, the CoM oscillations remained roughly the same independently of frequency and vision (p > .5 for all comparisons).

The mean asymptotic values of TA activity (Fig. 3, middle panels) were different between frequency, F(1, 9) = 29.61, p < .005, and vision conditions, F(1, 9) = 23.83, p < .001, and higher at HF than LF, but significantly so only for EC (p < .005). Vision affected the asymptotic value of TA only at HF (higher EC than EO) (p < .005, all comparisons). The asymptotic value of Sol was not affected by frequency and vision (frequency: F(1, 9) = 0.48, p > .5; vision: F(1, 9) = 0.42, p > .5). The analysis on the mean asymptotic values of CoM and CoP P-P oscillations showed differences between the two variables, F(1, 9) = 169.30, p < .0001; the interaction between variables and frequency was significant, F(1, 9) = 67.87, p < .0001. The asymptotic values of CoP P-P oscillation were always greater that those of CoM (p < .005, for all comparisons).

The bottom panels of Fig. 3 show the mean time-constants (τ). These were different between recorded variables, F(3, 27) = 20.41, p < .0001) but not between frequency and vision conditions (frequency: F(1, 9) = 0.92, p > .3; vision: F(1, 9) = 3.51, p > .1). There were interactions between variables and frequency, F(3, 27) = = 10.71, p < .0001, variables and vision, F(3, 27) = = 4.48, p < .01, and frequency and vision, F(1, 9) = 6.94, p < .05. For each frequency and visual condition, τ was always greater for Sol than TA (p < .05, for all comparisons). During the more challenging condition (HF EC), TA activity reached the steady state very quickly, and its τ value was shorter than that of CoM P-P oscillations (p < .05), which decayed slowly. This latter τ was in turn very similar to that of Sol (p > .5). The time decay of CoM was similar to that of CoP P-P oscillations under each frequency and visual condition (p > .5, for all comparisons). With EC, the oscillation frequency had significant effects only for the kinematic and kinetic variables (p < .05), while no differences between frequency conditions were found for muscle activities (p > .5). At LF EC, τ of CoM and of CoP P-P oscillations was lower than of HF. Conversely, with EO, frequency had a significant effect for all variables (p < .05, for all comparisons): frequency led to opposite behavior of muscle activity and kinematics or kinetic variables: passing from HF to LF, τ increased for both muscles, but decreased for CoM and CoP.

The general pattern of time-varying changes can be appreciated in Fig. 4 for all variables. The plots show the exponential curves of the EMG activity and CoM and CoP P-P oscillations, computed by using the mean coefficients (τ, A and B) obtained from the curves fitted to the data recorded during each trial of each subject. From this synthetic representation, it emerges that different patterns of muscle activity are associated with different patterns of kinematic and kinetic behaviors. For example, an initially large and rapidly decaying TA activity together with a moderate and slowly decaying Sol activity produce an almost invariant behavior of CoM and CoP (black lines for 0.6 Hz).

TA and Sol behaviors similar to the above produce instead a very different pattern in both CoM and CoP at 0.2 Hz (black lines): under this condition, CoM and CoP oscillations increase during the trial.

On the other hand, at 0.2 EO (grey lines), the very low and constant activity of TA is associated with an increase in CoM: notably, in this case, Sol activity is also low and stable.

The 'adaptation index'

The adaptation index was calculated for EMG, kinematic and kinetic data to allow comparison of the behaviors of variables of different nature. At HF, TA and Sol indexes were much smaller than one, indicating a reduction in EMG activity at the end of the balancing period, for both visual conditions (Fig. 5). The reduction was smaller for Sol than TA, and more evident at EC than EO. Similar results were found for LF. Note that the index is not representative of the overall level of muscle activity, but only of its relative decay. The index for CoM was close to one at HF, for both EC and EO. Regardless of vision, this index was significantly higher than 1 at LF, indicating that CoM P-P oscillations increased as the trial evolved. Similar values were found for the CoP P-P index. The indexes of all variables entered the same ANOVA design, in order to assess potential differences under different vision and frequency conditions. There were differences between variables, F(3, 27) = 81.93, p < .0001, and between frequency conditions, F(1, 9) = 5.33, p < .05.

Interestingly, not all variables exhibited an adaptation. For instance, the index of TA activity was small at HF and LF, while the Sol index pointed to decaying activity only in the most challenging condition (HF EC). CoP and CoM P-P indexes showed adaptation at 0.2 Hz, in the opposite sense (see the right and left panels of Fig. 5). There was an interaction between variables and frequency (F(3,27) = 5.11, p < .005) and an interaction between variables, frequency and vision, F(3, 27) = 3.71, p < .05. Frequency had a significant effect only for CoM and CoP P-P indexes, both EO and EC (p < .0005 for both comparisons).

Discussion

Balance training leads to changes in the performance of a task [START_REF] Anderson | Cognitive skills and their acquisition[END_REF][START_REF] Diener | Early stabilization of human posture after a sudden disturbance: Influence of rate and amplitude of displacement[END_REF] and improves static and dynamic balance [START_REF] Distefano | Evidence supporting balance training in healthy individuals: A systemic review[END_REF]. The CNS is malleable and flexible and adapts to practice and experience [START_REF] Hall | Adaptive changes in anticipatory postural adjustments with novel and familiar postural supports[END_REF][START_REF] Hu | Multisensory training of standing balance in older adults: I. Postural stability and one-leg stance balance[END_REF]Ledin et al., 1991). Adaptation mainly consists in the definition of a new motor strategy that appears to be the most appropriate in terms of taskvariable control and energy expenditure minimization [START_REF] Ting | Neuromechanics of muscle synergies for posture and movement[END_REF]. Part of this process consists in a gradual shift from feed-back to feed-forward rules [START_REF] Dietz | Human stance on a sinusoidally translating platform: Balance control by feedforward and feedback mechanisms[END_REF]. With repeated perturbations, the CNS builds new or updates existing internal models to enhance feed-forward control [START_REF] Bhatt | Adaptive control of gait stability in reducing slip-related backward loss of balance[END_REF][START_REF] Bhushan | Computational nature of human adaptive control during learning of reaching movements in force fields[END_REF][START_REF] De Nunzio | Head stabilization on a continuously oscillating platform: The effect of a proprioceptive disturbance on the balancing strategy[END_REF][START_REF] Kawato | A computational model of four regions of the cerebellum based on feedback-error learning[END_REF][START_REF] Pavol | Feedforward adaptations are used to compensate for a potential loss of balance[END_REF].

Dynamic perturbations of balance have been previously used by various laboratories and the balancing pattern described in detail [START_REF] Buchanan | Emergence of postural patterns as a function of vision and translation frequency[END_REF][START_REF] Corna | Standing on a continuously moving platform: Is body inertia counteracted or exploited[END_REF][START_REF] Schmid | Equilibrium during static and dynamic tasks in blind subjects: No evidence of cross-modal plasticity[END_REF]. However, the balancing patterns have been analyzed mainly under steady-state condition [START_REF] Buchanan | Emergence of postural patterns as a function of vision and translation frequency[END_REF][START_REF] Dietz | Human stance on a sinusoidally translating platform: Balance control by feedforward and feedback mechanisms[END_REF][START_REF] Schieppati | Variability in a dynamic postural task attests ample flexibility in balance control mechanisms[END_REF][START_REF] Schmid | Graded changes in balancing behavior as a function of visual acuity[END_REF]. [START_REF] Dietz | Human stance on a sinusoidally translating platform: Balance control by feedforward and feedback mechanisms[END_REF] pointed to a relatively quick adaptation to a change in acceleration of subjects (which were already 'adapted' to smaller or to faster continuous displacements of the treadmill upon which they stood). The adaptation was studied within four cycles (corresponding to a time period of about 15 s). Both TA and calf muscle activity and body center of mass featured this process: the forces acting on the body were minimized, as a manifestation of the take-over of a feed-forward control as opposed to the outcome of a reflex chain.

The present study investigated the adaptation phenomenon that occurs on passing from quiet stance to a continuous balance perturbation consisting in prolonged back and forth horizontal translations of a platform upon which subjects stand. In particular, we investigated the features and the time-course of the adaptation that ensues in the postural muscles' activity and body balancing pattern during a 3-min period of continuous, predictable, sinusoidal oscillation, and the modulation in this adaptation depending on visual condition (eyes-open, EO; eyes-closed, EC) and frequency (LF, 0.2 Hz; HF, 0.6 Hz) of platform translations.

General features of adaptation

When our naïve subjects were presented with platform perturbations, we observed a clear-cut adaptation, consisting in a progressive reduction of the initially very prominent leg muscle activities (Fig. 2). This occurred in the face of a remarkably unvaried kinematics of the CoM cyclic displacements (Fig. 2). The entity of the variation has been represented by the value of the adaptation index. For example, at high oscillation frequency without vision, the EMG activity of the TA muscle was reduced at the end of the perturbation period to about 40% of the initial value.

Under the same condition, the CoM and CoP antero-posterior oscillation hardly changed at the end of the perturbation period (Fig. 5). Vision, as well as oscillation frequency, affected the adaptation process. Frequency mainly affected the time course of the kinematic and kinetic variables, as

shown by the values of the time-constant (τ, see the bottom panels of Fig. 3, and Fig. 4), and vision mainly affected the level of the muscle activity (see the upper and middle panels of Fig. 3).

It appears that, when the subject is not familiar with the perturbation, and the latter is particularly challenging (HF, EC and EO; LF, EC), aim of the CNS is to keep the CoM within limits even at the cost of a remarkable dissipation of energy, witnessed by the large TA and Sol activation (Fig. 3, upper panels). This parallels the initially large CoP oscillations. As soon as the CNS estimates the degree and type of balance challenge, muscle activity diminishes. It seems that the balance control mechanism is effective in restricting the CoM oscillations from the beginning, but this goal is achieved in a more efficient way during the adaptation process (see also, in a different context, [START_REF] Bachmann | Vertical perturbations of human gait: Organisation and adaptation of leg muscle responses[END_REF]. These findings are in line with the idea of an optimal trade-off between the opposing demands of achieving a task-level performance goal versus minimizing energy expenditure [START_REF] Körding | Decision theory: What "should" the nervous system do?[END_REF][START_REF] Lockhart | Optimal sensorimotor transformations for balance[END_REF]Shadmer & Krakauer, 2008;[START_REF] Ting | Neuromechanics of muscle synergies for posture and movement[END_REF][START_REF] Todorov | Optimal feedback control as a theory of motor coordination[END_REF]. On the other hand, when the postural system is only slightly perturbed, for example when the platform translates at LF EO, no major activation of the two postural muscles occurs, even at the beginning of the trial. As expected, since the CoP displacements reflect the balance-controlling muscle activity [START_REF] Nardone | Postural adjustments associated with voluntary contraction of leg muscles in standing man[END_REF], the CoP oscillations were initially smaller than under the more challenging conditions (Fig. 3, upper panels).

Adaptation of Sol and TA activity

In our hands, the continuous oscillations (with constant characteristics of amplitude and frequency from the beginning to the end of the trial) induced an adaptation phenomenon that started from the beginning of the perturbations and followed an exponential law. Under challenging conditions (HF, EC and EO; LF EC), the muscle activity, particularly for TA, showed a progressive reduction from an initial massive activity until a steady-state value, definitely smaller than that at the beginning of trial (Figs. 4 and5). The mean time constant of this process was not much different across perturbation conditions, amounting for TA to about 70 s on average, so that TA activity reached the steady state roughly at the end of the perturbation period. The time constant of the Sol adaptation was again not different across perturbation conditions, but it was clearly longer than that of TA, amounting to about 400 s on average. Therefore, Sol activity diminished much more slowly, without reaching the steady state value by the end of the oscillation period. On the other hand, the similar time constants within each muscle across LF and HF, in spite of the different overall amount of EMG activity at the two frequencies, signifies that the adaptation process does not depend on the number of oscillation cycles or on the level of muscle activity, but rather on the time elapsed from the beginning of the perturbation. On the basis of the present data, it seems that such adaptation process should in fact take into account not only the number of perturbing cycles but also the body reactions to these continuous displacements, and this would be a time-consuming process. Such supposition needs confirmation by testing different, higher perturbation frequencies and different perturbation amplitudes.

It is reasonable to assume that the dichotomy between TA and Sol behavior may be due to their different role in the postural control. TA operates the necessary corrections to maintain the CoM within the support base, while Sol mainly counteracts gravity allowing upright posture. In passing, changes in visual condition during a steady-state balancing behavior on the translating platform also produced larger changes in muscle activity for TA than Sol (De Nunzio & Schieppati, 2007).

Under challenging conditions and during the initial oscillation cycles, when the characteristics of the perturbations are not yet known, both muscles contribute strongly to the initial fall-preventing responses by producing a considerable effort. An 'optimization' process is progressively activated, whereby TA activity rapidly diminishes whilst Sol activity is reduced until the level necessary and sufficient for keeping the body upright. The Sol EMG does not further diminish below the level observed under quite stance. Notably, the large initial muscle activities must nonetheless be finely coordinated, likely by in-built, largely reflex mechanisms, whereby the CoM is kept within restricted limits from the beginning.

The effect of vision

The time-course of adaptation in muscle activity is affected by vision only at LF. At HF vision has a minor effect: the mean time constants describing the exponential trend of the muscle activity were in fact not different for either muscle under the two visual conditions. Vision per se should therefore not be held responsible for a 'better' or 'quicker' adaptation of postural muscle activity. Conversely, vision would provide the CNS useful information to allow balancing with minimal muscle activity, particularly in the TA, independently of the oscillation frequency. A similar effect of vision on the level of muscle activity has been previously shown by [START_REF] Dietz | Human stance on a sinusoidally translating platform: Balance control by feedforward and feedback mechanisms[END_REF], who found a generally higher EMG activity with EC than EO. This effect was more evident in the TA than calf muscle responses to the changes in treadmill acceleration. However, no specific influence of vision was found on the rate of attenuation of muscle activity.

CoM and CoP behavior, and its relationship with the EMG changes

Previous studies [START_REF] Alexandrov | Axial synergies during human upper trunk bending[END_REF][START_REF] Crenna | Forward and backward axial synergies in man[END_REF][START_REF] Oddsson | Co-ordination of a simple voluntary multi-joint movement with postural demands: Trunk extension in standing man[END_REF]Oddsson & Thortensson, 1986) have shown that during forward and backward trunk bending movements, the CoM is efficiently regulated with respect to the base of support. The idea that CoM is the stabilised reference for posture and movement coordination [START_REF] Massion | Movement, posture and equilibrium: Interaction and coordination[END_REF] has been put forward by other authors investigating a range of whole body movements with changing the configuration of the support base [START_REF] Mouchnino | Coordination between equilibrium and head-trunk orientation during leg movement: A new strategy build up by training[END_REF][START_REF] Nardone | Postural adjustments associated with voluntary contraction of leg muscles in standing man[END_REF], with different moving segments [START_REF] Bouisset | A sequence of postural movements precedes voluntary movement[END_REF][START_REF] Eng | Interaction of the reactive movements and centre of mass displacement for postural control during voluntary arm movements[END_REF] or during altered environmental conditions [START_REF] Horstmann | A basic posture control mechanism: The stabilization of the centre of gravity[END_REF][START_REF] Mouchnino | Is the regulation of the center of mass maintained during leg movement under microgravity conditions[END_REF]. During dynamic tasks, the CNS controls the CoM trajectory more precisely than individual joint angles [START_REF] Scholz | The uncontrolled manifold concept: Identifying control variables for a functional task[END_REF], suggesting that successful balance control depends on having accurate knowledge of the entire body configuration in space, as well as of the location of the CoM.

At high frequency, our findings show that no exponential trend, similar to that visible in the EMG activity, is obvious in the mean oscillations of CoM, EC and EO. In the face of the large and fairly rapid changes in the TA EMG pattern, no parallel changes characterise the CoM oscillation, which remains almost constant in amplitude (Figs. 4 and5). This is evidence that different patterns of muscle activity can be successful in accurately controlling CoM and keeping it in a safe position (compare the adaptation indexes in Fig. 5). Vision does not affect the pattern of CoM displacement, either. Therefore, CoM represents an invariant variable of this adaptation process. Conversely, CoP oscillations, which control the CoM oscillations, do change their amplitude depending on visual conditions. With EO, both the initial oscillation amplitude and the asymptotic value of CoP are smaller with respect to EC, in parallel with the EMG activity (Fig. 3). Nevertheless, vision did not greatly modify the time course of this process (Fig. 3, bottom left panel).

Different conclusions can be inferred for the LF condition. From the onset to the end of the trial, the amplitude of CoM and CoP displacement increases to reach the amplitude of the platform translation at steady state (Figs. 4 and5). Note that, at the beginning of the LF platform translation, the amplitude of the CoM oscillations in the very first cycles is equal to that observed at the very beginning of the HF platform translations (see Fig. 3, upper right panel). The CoM starts to move with relatively small oscillations, which then increase successively in an exponential way, at the same time as subjects learn to cope with the slow platform translations. A similar behavior is exhibited by the CoP. At LF, the boundaries within which CoM can oscillate safely may not be immediately estimated. As the perturbation continues, less and less effort is made for keeping body oscillations within restricted limits, and EMG gradually diminish in this LF condition while CoM gradually increases. The adaptation process would now consist in developing the more economical mode to passively follow the platform displacement. Therefore, not only it is possible to note a variation in CoM behavior, but this event is accompanied by a reduction the TA muscle, which is evident only at EC because at EO TA activity is very small from the beginning. On the other hand, Sol activity does not contribute to this process. At LF, for both EC and EO, Sol almost exclusively plays the role of postural muscle leaving to TA the control of the dynamic body stabilization.

Perhaps, the CNS selects the mechanisms subserving adaptation by computing the risk that the equilibrium is challenged beyond a certain threshold [START_REF] Tjernström | Improved postural control through repetition and consolidation[END_REF][START_REF] Ishizaki | Repeatability and effect of instruction of body sway[END_REF]. [START_REF] Fujiwara | Postural control adaptability to floor oscillation in the elderly[END_REF] noted that the subjects, who were more unsteady in the first of five trials, had a greater need for adaptability of postural control.

A noteworthy point of this adaptation process is that the time constant that shapes the increase in oscillation amplitude of kinematic and kinetic variables is similar to that of the decrease of TA EMG at HF EC and EO and LF EC, conditions in which the muscular adaptation process is clearly visible. This would mean that the time course of the adaptation is not different with respect to the nature of the controlled variable: a single temporal law controls the variables directly involved in the adaptation process. This is in keeping with the view that the kinematics of the balancing body (the controlled variable in the balancing process) is strictly associated with the activity of the recorded leg muscles, which are the muscles directly opposing the ground reaction force (the controlling variable). No time-dependent changes in the CoM oscillation are instead obvious under HF condition, in spite of a clearly diminishing muscle activity. Under this condition, the initial oscillation of the CoM would be the 'default' value, probably a 'safe' value weighted based on our anthropometric variables and the more frequently encountered day-to-day perturbations (the same value of oscillation amplitude exhibited at the onset of the adaptation process during LF condition).

The absence of a reduction in oscillation at HF may be connected with the already small enough 'default' value. On the other hand, an increase in oscillation such as that observed under LF would have been detrimental to balance, because it would produce a large displacement velocity, which would require stronger forces to keep it from falling at reaching the anterior or posterior turning points of the platform. As expected, the asymptotic values of body oscillations at HF and LF are are comparable with those recoded under the respective steady state conditions [START_REF] Buchanan | Emergence of postural patterns as a function of vision and translation frequency[END_REF][START_REF] Corna | Standing on a continuously moving platform: Is body inertia counteracted or exploited[END_REF].

Conclusion

The adaptive behavior of the postural control under dynamic conditions, which emerges from our data collected while standing on a continuously moving platform, is not stereotyped and univocal.

Under challenging conditions, represented by fast balance-challenging oscillations of the support base, the amplitude of CoM oscillations remains invariant in spite of EMG reduction. During slow platform translations, a similar reduction in muscle activity parallels a progressive increase in the CoM cyclic displacement; under this condition, the modulation of the command to the muscles controlling balance accompanies the shift from active to 'passive' displacement of the body.

These findings expand the current knowledge on the mechanisms subserving adaptation to continuous balance perturbations. Contrary to the wealth of papers on the adaptation of arm reaching movements, few studies have dealt with the problem of adaptation of the whole body to perturbation. Presence, extent and time-course of the adaptation process have been addressed mostly as a secondary part of a main investigation, or during time-intervals limited to few cycles of perturbation. Otherwise, aspecific adaptation modes have been described when continuous perturbations have been used. In other studies, the perturbations were separate, and the interval between perturbations must surely have affected the reported adaptation rates. Our use of a prolonged continuous perturbation, at different frequencies and sensory conditions, allows to focus on the time-course of an adaptation process evolving during the continuous administration of the perturbation, in addition to establishing the different modes of adaptive interactions between kinematics and muscle activity depending on mechanical constraints and vision. Possibly, as suggested earlier, the adaptation described here relies on a central process receiving and integrating a number of afferent inputs without being wholly dependent on any one for its function.

Processing of this information may continuously update the internal model of the moving body, allowing shifting the postural control from a feedback to a feed-forward mode. Our next aim will be to assess, by ad-hoc experiments, the persistence in time and the dependence on sensory information of this adapted state. Such a protocol can be administered within a relatively short period of time, and recording of kinematic, kinetic and EMG activities can be simplified by accepting reasonable approximations, in order to test balancing capacities in the elderly or in patients with movement disorders. The same protocol can be adjusted to train balancing capacities and reduce the likelihood of falls in ). The bestfit curve thus obtained is superimposed to the EMG trace (grey line). oscillations, for 0.6 Hz and 0.2 Hz, under EC (grey circles) and EO (white circles) conditions. For each subject, the adaptation index has been computed as the ratio between the value of the exponential curve y = A + B e -t/τ at the end of the platform translation period (t = 180 s) divided by the value at time zero. An index close to 1 means that the variable value was similar at the beginning and at the end of the oscillation period. TA and Sol activities were smaller at the end than at the onset of the platform translation period under all perturbation conditions. Conversely, the oscillation amplitudes of CoM and CoP did not vary throughout the perturbation period at 0.6

Hz; at 0.2 Hz these variables were larger at the end than that at the onset of the period. 255.46 367.48 ± 251.75 346.94 ± 262.64 333.35 ± 257.57 3.23, p > .5 0.2, p > .5 -----------------------------Sol area (µVolt) 914. 43 ± 308.57 903.58 ± 356.97 911.74 ± 362.96 910.12 ± 317.98 0.01, p > .5 0.01, p > .5 --------------------- --------

Fig. 2

 2 Fig. 2 gives examples of the traces of TA and Sol EMG (panels A-B and C-D), CoM (panels E-F)

Fig. 1 .

 1 Fig. 1. Example of the EMG activity of the TA muscle acquired during the platform translation trial of three minute duration at 0.6 Hz with eyes closed (EC). The trace corresponds to the rectified and filtered muscle activity. The trace has been fitted by an exponential model (y = A + Be -t/τ

Fig. 2 .

 2 Fig. 2. Traces of TA and Sol EMG activity (panels A-B and C-D), CoM (panels E-F) and CoP displacements (panels G-H) of one representative subject while balancing on the translating platform at 0.6 Hz (left column) and 0.2 Hz oscillation frequency (right column). The traces of the malleolus marker are also depicted (panels I-L). All the traces have been recorded during the same trial performed under EC condition.

Fig. 3 .

 3 Fig. 3. The histograms show the intercept (top panels), asymptotic (middle panels) and timeconstant value (bottom panels), obtained by fitting the data of TA and Sol EMG activity and of CoM and CoP peak-to-peak oscillations. Mean data under the two visual (EO and EC) and frequency (HF, 0.6 Hz and LF, 0.2 Hz) conditions are shown.

Fig. 4 .

 4 Fig. 4. Reconstructed exponential curves fitting the TA and Sol activity profiles (upper panels) and the CoM and CoP peak-to-peak oscillations throughout a perturbation trial (bottom panels), for both vision and frequency conditions. For each panel, the two visual conditions are superimposed (grey lines, EO; black lines, EC). These exponential curves have been computed by inserting in the exponential equation y = A + B e -t/τ the mean values of the parameters A, B and τ. Each mean value has been obtained by averaging the corresponding values of each subject. These data are detailed in the insets to each plot. Note that the functions intercept the ordinate at A + B.

Fig. 5 .

 5 Fig. 5. Mean adaptation indices of TA and Sol activity and of CoM and CoP peak-to-peak

  amplitude of CoM and CoP during the quiet stance periods preceding the beginning of the platform translations (0.6 and 0.2 Hz). Note that an approximation of the oscillation amplitude is expressed by the standard deviation of the CoM and CoP trace (CoM SD and CoP SD). Both CoM and CoP were referred to the position of the malleolus' marker. The results of the 2-way ANOVA (frequency and vision conditions) are shown for both TA and Sol. The results of the 3-way ANOVA (variable, frequency and vision conditions) for CoM and CoP position and for CoM and CoP SDs are also reported.

Table legend Table 1 .

 legend1 Mean values (± standard deviations) of the mean area of TA and Sol, position and oscillation

populations at risk. The challenge would be to provide evidence about the efficacy of specific training approaches facilitating people reaching an optimal level of balance control in daily life[START_REF] Nardone | Balance rehabilitation by moving platform and exercises in patients with neuropathy or vestibular deficit[END_REF][START_REF] Schubert | Balance training and ballistic strength training are associated with task-specific corticospinal adaptations[END_REF][START_REF] Pai | Inoculation against falls: Rapid adaptation by young and older adults to slips during daily activities[END_REF].

Patel, M.,Gomez, S., Lush, D., & Fransson, P. A. (2009). Adaptation and vision change the relationship between muscle activity of the lower limbs and body movement during human

Acknowledgments

This study was supported by grant PRIN 2007 # 2007HTFN9L from the Italian Ministry of University and Research (MIUR). The financial contribution of CARIPLO and of Regione Lombardia is gratefully acknowledged.