BackgroundAdults with sedentary lifestyles seem to face a higher risk of falling in their later years. Several causes, such as impairment of strength, coordination, and cognitive function, influence worsening health conditions, including balancing ability. Many modalities can be applied to improve the balance function and prevent falling. Several studies have also recorded the effects of balance training in elderly adults for fall prevention. Accordingly, the aim of this study is to define the effect of virtual reality-based balance training on motor learning and postural control abilities in healthy adults.MethodsFor this study, ten subjects were randomly allocated into either the conventional exercise (CON) or the virtual reality (VR) group. The CON group underwent physical balance training, while the VR group used the virtual reality system 4 weeks. In the VR group, the scores from three game modes were utilized to describe the effect of motor learning and define the learning curves that were derived with the power law function. Wilcoxon Signed Ranks Test was performed to analyze the postural control in five standing tasks, and data were collected with the help of a force plate.ResultsThe average score was used to describe the effect of motor learning by deriving the mathematical models for determining the learning curve. Additionally, the models were classified into two exponential functions that relied on the aim and requirement skills. A negative exponential function was observed in the game mode, which requires the cognitive-motor function. In contrast, a positive exponential function was found in the game with use of only the motor skill. Moreover, this curve and its model were also used to describe the effect of learning in the long term and the ratio of difficulty in each game. In the balance performance, there was a significant decrease in the center of pressure parameters in the VR group, while in the CON group, there was a significant increase in the parameters during some foot placements, especially in the medio-lateral direction.ConclusionThe proposed VR-based training relies on the effect of motor learning in long-term training though different kinds of task training. In postural analysis, both exercise programs are emphasized to improve the balance ability in healthy adults. However, the virtual reality system can promote better outcomes to improve postural control post exercising.Trial registration Retrospectively registered on 25 April 2018. Trial number TCTR20180430005Electronic supplementary materialThe online version of this article (10.1186/s12938-018-0550-0) contains supplementary material, which is available to authorized users.

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This study applied the posturography framework on five static standing tasks from the Berg Balance Scale (BBS). Thirteen participants were recruited and the trajectory data of the center of pressure (CoP) were collected. To analyze the postural performance, two approaches were taken: the scores from the BBS and statistical analysis. For the statistical analysis, Spearman’s method was applied to determine the correlation of CoP parameters. The results revealed the correlations between CoP parameters in the anterior-posterior (AP) and medial-lateral (ML) directions, and on the statokinesgram (SK) plane for all tasks. To obtain the in-depth detail between normal weight and overweight groups, the differences in the postural control mechanism were defined by correlations of CoP parameters. The Mann-Whitney U test was conducted to define the difference in postural control in terms of difference in weight gain and standing task factors, while Cohen’s d was used to investigate the influence of the difference in standing tasks and weight gain on postural control. The results showed that the correlations of CoP parameters could distinguish the balance impairment in the overweight condition from the normal postural control. Otherwise, the scores of BBS, the Mann-Whitney U test and Cohen’s d did not separate this slightly compensatory movement during equilibrium. Therefore, the correlations of CoP parameters could provide more information to analyze the balance function in each individual, especially in terms of slight compensation.

At present, patients whose have suffered from stroke in Thailand are increasing every year. Stroke impairments relate to many functions such as sensory, motor function, communication, visual and emotional function which depend on brain's lesion. Physical examinations and assessments are important for planning the rehabilitation programs. For this reason, there are several information for medical decision making. Missing some data for treatment planning may occur. To solve this problem, the proposed study used two algorithms to determine the proper rehabilitation treatment program. Artificial Neural Networks and Decision Trees models were considered. Sensitivity, specificity and accuracy values were computed to define the performance of both algorithms. The results of this study indicated that both techniques can apply for data classification and define the proper treatment programs. However, the results were shown that the specificity and accuracy of decision trees model were higher than neural network model.

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