Estimation of Human Postural Models Using Artificial Neural Networks Under Normal and Overweight Conditions
Issued Date
2024-01-01
Resource Type
Scopus ID
2-s2.0-105000417231
Journal Title
16th Biomedical Engineering International Conference, BMEiCON 2024
Rights Holder(s)
SCOPUS
Bibliographic Citation
16th Biomedical Engineering International Conference, BMEiCON 2024 (2024)
Suggested Citation
Petngam P., Ongwattanakul S., Prasertsakul T., Charoensuk W. Estimation of Human Postural Models Using Artificial Neural Networks Under Normal and Overweight Conditions. 16th Biomedical Engineering International Conference, BMEiCON 2024 (2024). doi:10.1109/BMEiCON64021.2024.10896297 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/108612
Title
Estimation of Human Postural Models Using Artificial Neural Networks Under Normal and Overweight Conditions
Author's Affiliation
Corresponding Author(s)
Other Contributor(s)
Abstract
The study of human postural control has been a continuous area of research due to the complexity of postural mechanisms, which are not fully understood. Existing models of human postural control provide partial explanations but often face limitations such as lack of generalization. This study aims to investigate postural models for two weight groups-normal weight (NW) and overweight (OW)-across three balance conditions: eyes-opened (EO), eyes-closed (EC), and single stance (SS). Using data collected from 11 participants, simulations were conducted using two time series models: the Autoregressive (AR) model and the Non-linear Autoregressive Moving Average (NARMA) model. Additionally, Artificial Neural Networks (ANNs) were employed to determine the optimal model orders. The results reveal different postural control mechanisms for each weight group. In stable conditions (EO), NARMA(4,1) and NARMA(8,10) models yielded center of pressure (COP) estimations with Mean Squared Errors (MSE) of 7.20 × 10-5 and 2.03 × 10-4 for NW and OW groups, respectively. The findings indicate that OW individuals require different mechanisms to maintain balance, with a higher reliance on previous COP information.