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Two simultaneously performed tasks may compete for common brain network resources in patients with multiple sclerosis (MS), suggesting the occurrence of a cognitive-motor interference. While this phenomenon has been well described for walking and gait, data on static balance are scarce.In this cross-sectional study, 92 patients and 46 sex/age-matched healthy controls (HCs) were tested by means of static posturography under eyes opened (single-task condition) and while performing the Stroop word-colour task (dual-task condition), to estimate the dual-task cost (DTC) of standing balance. The patient group also underwent the Expanded Disability Status Scale, 25-foot walking test, 12-item MS walking scale, Modified Fatigue Impact Scale, and Symbol Digit Modalities Test.Patients had larger postural sway under both single-task and dual-task conditions (p0.001), as well as greater DTC of standing balance (p=0.021) than HCs. Although secondary progressive (SP) patients had larger sway in both conditions than relapsing-remitting (RR) patients (p0.05), these latter ones exhibited a greater DTC of postural balance (p=0.045). Deficits in sustained attention and information processing speed, as assessed by the SDMT, were also independently associated with the magnitude of DTC of standing balance (p=0.005).The phenomenon of cognitive-motor interference might be unmasked by a dual-task posturography and was associated with impaired sustained attention and information processing speed, especially in RR patients. The smaller DTC of standing balance observed in SP patients may be due to the ceiling effect of postural sway, or alternatively to the lack of postural reserve which constrained the more disabled patients to prioritize the balance over the cognitive task.

test reliability of the signal in healthy adults. Also, EMGwaveforms fromMG and TA are similar to previous reports using both FW and surface sensors [2,3]. This similarity suggests that the fine wire sensors adequately represents the muscle activity even though a FW sensor measures action potentials from an effective number of motor units within a relatively small detection volume [3]. Murley et al. [2] shows a similar pattern of TP EMG profile. Therefore the EMG profile from FW sensors represents the activity of TP during gait. In conclusion, despite the small detecting volume, EMG signals detected by a FW sensor can be taken as representative of activity of the entire muscle in TP, MG, and TA. The result is also repeatable in healthy adults.

s / Gait & Posture 42S (2015) S1–S90 S45 (Rimini, Italy).Motricity Index (MI), Trunk Control Test (TCT), Functional Ambulation Category (FAC), Walking Handicap Scale (WHS), Rivermead Mobility Index (RMI), Cumulative Illness Rating Scale (SI and CI), Timed-Up and Go Test (TUG) and Two Minute Walk Test (2MWT) were administered to subjects by the same operators. The 2MWT was administered with the addition of an IMU located on the trunk, at the height of the fifth lumbar vertebra. Four gait stability measures, namely Harmonic Ratio (HR), Index of Harmonicity (IH), Multiscale Entropy (MSE), and Recurrence Quantification Analysis (RQA), were calculated on trunk acceleration signal during gait obtained from 2MWT. Each measure was calculated for anterior-posterior (AP), mediolateral (ML) and vertical (V) acceleration directions. Log transformed measures were then used as inputs for linear regression models. Results: Subjects who used a cane showed a high correlation between MSE, RQA and TCT, RMI. More sparse but significant correlation has been found between MSE and WHS, CIRS. IH showed no correlation with clinical parameters. Subjects who were able to walk without a cane showed high correlation values betweenMSE, RQA and TUG, 2MWT and WHS. TUG, 2MWT and SI correlated in a lesser extent with HR. IH showed high correlation values with TUG and RMI; RMI correlated with MSE and RQA also. Discussion and conclusions: In subjects who walked with a cane, correlations between TCT and MSE were particularly consistent, highlighting an affinity between the instrumental measure of complexity of trunk acceleration and the clinical assessment of trunk control. For subjects who were able to walk without a cane TUG, 2MWT showed high correlations with stability measures, in particular with MSE and RQA (mainly in the ML direction), highlighting the importance of the medio-lateral control of the trunk during gait. In conclusion, gait stability measures based on trunk accelerations (in particular MSE and RQA) showed promising correlation with clinical scales in stroke patients, and could complement the standard clinical scores in the assessment of locomotor performance of subjects with stroke, helping clinicians and physical therapists in the patient’s rehabilitation process. Moreover, the use of instrumental measurements could lead, in the future, to a more reliable quantification of locomotor features, allowing avoiding inter-operator differences.

s / Gait & Posture 42S (2015) S1–S27 S5 Fig. 1. (a) Reference image. Model generated after the manual identification of the relevant anatomical landmarks. (b) Tracked model in a dynamic frame. They wore an underwear garment with a coloured patch applied, and uniformly coloured ankle socks used as segmentalmarkers (for pelvis and feet, respectively). A RGB video camera (Vicon Bonita 720C, 60 fps) was placed laterally to the walkway. Synchronous 3D marker positions were captured with a Vicon system (Bonita10). Markers were positioned according to the Davis protocol [3] (GS). The proposed ML methodology requires a preliminary model calibration step, performed on a reference image of the subject standing upright (Fig. 1a), from which a foot contour model template is automatically identified, while the lateral malleolus, lateral epicondyle and greater trochanter are manually identified [2]. Model templates of the shank and thigh segments are defined bymeans of technical reference points identified on the silhouette contour. The pelvis orientation is assumed to coincidewith the orientation of the segment obtained by interpolating the points extracted from the pelvis patch. For eachgait trial, the silhouette contourof themoving subject was extracted using a background subtraction technique, and the foot and pelvis segmental markers were identified using colour filters. The foot orientation was determined by matching the foot template with the foot current image. The shank and thigh reference points on the deformed silhouette contour were identified for each frame, and the corresponding templates fitted using a SVD procedure (Fig. 1b) [2]. The kinematic estimations provided by theMLmethodwere compared to the GS in terms of correlation coefficient (CC) and root mean square deviation (RMSD). Results: The CC (and RMSD) between the ML and the GS estimations ranged from 0.91 (4.9◦) to 0.98 (7.3◦) for the hip flexextension, from0.94 (5.4◦) to 0.97 (8.3◦) for the knee flex-extension and from0.74 (5.9◦) to0.84 (9.9◦) for theankledorsi-plantarflexion. Discussion: Joint kinematics estimatedusingML showed a good correlation with the GS joint kinematics (0.74≤CC≤0.98), however the relevant errors are clearly larger than those found in healthy adults [2] (up to 9.9◦). This difference can be attributed partly to the children’s body segments small size (particularly for the foot) and in some cases to the presence of the arm swing that reduces the visibility of the pelvis segmental marker. Therefore, the extraction of the segmental markers will have to be improved in order for this method to be applied in clinical settings.