Publication: Determination of postural control mechanism in overweight adults using the artificial neural networks system and nonlinear autoregressive moving average model
Issued Date
2020-01-01
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ISSN
21875219
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2-s2.0-85097535582
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Mahidol University
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SCOPUS
Bibliographic Citation
Advanced Biomedical Engineering. Vol.9, (2020), 154-166
Suggested Citation
Thunyanoot Prasertsakul, Warakorn Charoensuk Determination of postural control mechanism in overweight adults using the artificial neural networks system and nonlinear autoregressive moving average model. Advanced Biomedical Engineering. Vol.9, (2020), 154-166. doi:10.14326/abe.9.154 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/60401
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Title
Determination of postural control mechanism in overweight adults using the artificial neural networks system and nonlinear autoregressive moving average model
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Abstract
© 2020 The Author(s). Being overweight is one of several causes of balance impairment, and it increases the risk of falls. Balance assessments help diagnose this impairment. The outcomes from these assessments are not usually clear to investigate balance impairment in overweight adults. Several methods such as mathematical modeling can be used to investigate the postural control mechanisms in normal balance function. However, there is no study that is focused on the postural control mechanisms in overweight adults. This study aimed to define the postur-al control models underlying the application of the artificial neural network (ANN) systems in normal weight and overweight populations. Ten participants were recruited and separated into two groups: normal weight (NW) and overweight (OW). There were two processes for determining the postural model in both groups. First, the optimal orders of the nonlinear autoregressive moving average (NARMA) model and the hidden nodes of the ANN system were identified. Mean square error (MSE), Akaike’s information criteria (AIC) and residual variance (RV) were used to identify these variables for both groups. Second, the coefficients of these models were defined by the learned weights in the ANN system. The MSE, percent coefficient of variation (%CV), Kolmogorov-Smirnov (KS) test and maximal distance of cumulative distribution function (CDF) were defined to evaluate the performance of the postural models. Furthermore, the orders of the NARMA model and relative importance were utilized to distinguish the postural control mechanisms between the two groups. During the training process, our results indicated that low MSE, AIC and RV were the criteria for hidden nodes and order selection in the NARMA model, which resulted in different patterns of postural models in each group. In the case of the testing process, the findings revealed that the proposed technique could present different postural control strategies for each group. The findings indicated that the postural control mechanism of NW subjects relied on the center of pressure (CoP) in the anterior-posterior (AP) direction, while body sway in the medio-lateral (ML) direction was vital to maintain equilibrium in the OW subjects. Accordingly, the proposed technique could be used to investigate the difference in postural control mechanism between the two groups.