2020
DOI: 10.17784/mtprehabjournal.2017.15.527
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Effects of dual task training on gait temporal-spatial parameters of children with autism

Abstract: Background: Postural balance consists of information emanating from the interaction of visual, somatosensory and vestibular systens. This information is impaired in aging, leading to postural control changes in the elderly, increasing the risk of falls in this population. The postural balance may be improved with the addition of sensory information, such as a subpatelar bandage. Objective: To investigate the effect of using additional sensory information in gait speed and functional mobility of older fallers. … Show more

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Cited by 3 publications
(2 citation statements)
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“…Based on the results, MR seems to be a great tool to provide effective perceptual-motor training. Further evidence of its efficiency is that another study [ 53 ], with a similar slight multi-modal training protocol, but not in MR (i.e., walking while doing an upper limb motor task, a cognitive task, or both) showed no effect on the subjects’ gait. According to the authors, the protocol application duration was insufficient.…”
Section: Discussionmentioning
confidence: 99%
“…Based on the results, MR seems to be a great tool to provide effective perceptual-motor training. Further evidence of its efficiency is that another study [ 53 ], with a similar slight multi-modal training protocol, but not in MR (i.e., walking while doing an upper limb motor task, a cognitive task, or both) showed no effect on the subjects’ gait. According to the authors, the protocol application duration was insufficient.…”
Section: Discussionmentioning
confidence: 99%
“…Note that in classifying the ASD and normal walking gait, some studies implemented statistical analysis [43,44] and machine learning (ML) [27][28][29]32] too. For instance, studies done by [15,45] utilized statistical analysis method specifically t-test, while Pearson correlation was used as reported in [2], Ancova [46], stepwise method of discriminant analysis (SWDA) [25] as well as ML methods such as Linear Discriminant Analysis (LDA) [28], Neural Network (NN), Support Vector Machine (SVM) [25,32] and k-nearest neighbor (kNN) [29]. Table 6 tabulated the summary of gait classifiers used by previous researches related to ASD gait.…”
Section: Gait Classifiersmentioning
confidence: 99%