An Ensemble Analysis of Electromyographic Activity during Whole Body Pointing with the Use of Support Vector Machines

We explored the use of support vector machines (SVM) in order to analyze the ensemble activities of 24 postural and focal muscles recorded during a whole body pointing task. Because of the large number of variables involved in motor control studies, such multivariate methods have much to offer over the standard univariate techniques that are currently employed in the field to detect modifications. The SVM was used to uncover the principle differences underlying several variations of the task. Five variants of the task were used. An unconstrained reaching, two constrained at the focal level and two at the postural level. Using the electromyographic (EMG) data, the SVM proved capable of distinguishing all the unconstrained from the constrained conditions with a success of approximately 80% or above. In all cases, including those with focal constraints, the collective postural muscle EMGs were as good as or better than those from focal muscles for discriminating between conditions. This was unexpected especially in the case with focal constraints. In trying to rank the importance of particular features of the postural EMGs we found the maximum amplitude rather than the moment at which it occurred to be more discriminative. A classification using the muscles one at a time permitted us to identify some of the postural muscles that are significantly altered between conditions. In this case, the use of a multivariate method also permitted the use of the entire muscle EMG waveform rather than the difficult process of defining and extracting any particular variable. The best accuracy was obtained from muscles of the leg rather than from the trunk. By identifying the features that are important in discrimination, the use of the SVM permitted us to identify some of the features that are adapted when constraints are placed on a complex motor task

''Modulation of Anticipatory Postural Activity For Multiple Conditions of A Whole-body Pointing Task''

Tolambiya, A. | Chiovetto, E. | Pozzo, T. | Thomas, E.International audience''This is a study on associated postural activities during the anticipatory segments of a multijoint movement. Several previous studies have shown that they are task dependant. The previous studies, however, have mostly been limited in demonstrating the presence of modulation for one task condition, that is, one aspect such as the distance of the target or the direction of reaching. Real-life activities like whole-body pointing, however, can vary in several ways. How specific is the adaptation of the postural activities for the diverse possibilities of a whole-body pointing task? We used a classification paradigm to answer this question. We examined the anticipatory postural electromyograms for four different types of whole-body pointing tasks. The presence of task-dependent modulations in these signals was probed by performing four-way classification tests using a support vector machine (SVM). The SVM was able to achieve significantly higher than chance performance in correctly predicting the movements at hand (Chance performance 25%). Using only anticipatory postural muscle activity, the correct movement at hand was predicted with a mean rate of 62%. Because this is 37% above chance performance, it suggests the presence of postural modulation for diverse conditions. The anticipatory activities consisted of both activations and deactivations. Movement prediction with the use of the activating muscles was significantly better than that obtained with the deactivating muscles. This suggests that more specific modulations for the movement at hand take place through activation, whereas the deactivation is more general. The study introduces a new method for investigating adaptations in motor control. It also sheds new light on the quantity and quality of information available in the feedforward segments of a voluntary multijoint motor activity. (c) 2012 IBRO. Published by Elsevier Ltd. All rights reserved.'

A classification study of kinematic gait trajectories in hip osteoarthritis

International audienceThe clinical evaluation of patients in hip osteoarthritis is often done using patient questionnaires. While this provides important information it is also necessary to continue developing objective measures. In this work we further investigate the studies concerning the use of 3D gait analysis to attain this goal. The gait analysis was associated with machine learning methods in order to provide a direct measure of patient control gait discrimination. The applied machine learning method was the support vector machine (SVM). Applying the SVM on all the measured kinematic trajectories, we were able to classify individual patient and control gait cycles with a mean success rate of 88%. With the use of an ROC curve to establish the threshold number of cycles necessary for a subject to be identified as a patient, this allowed for an accuracy of higher than 90% for discriminating patient and control subjects. We then went on to determine the importance of each trajectory. By ranking the capacity of each trajectory for this discrimination, we provided a guide on their order of importance in evaluating patient severity. In order to be clinically relevant, any measure of patient deficit must be compared with clinically validated scores of functional disability. In the case of hip osteoarthritis (OA), the WOMAC scores are currently one of the most widely accepted clinical scores for quantifying OA severity. The kinematic trajectories that provided the best patient-control discrimination with the SVM were found to correlate well but imperfectly with the WOMAC scores, hence indicating the presence of complementary information in the two. (C) 2014 Elsevier Ltd. All rights reserved