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Neural Network Models for Estimation of Balance Control, Detection of Imbalance, and Estimation of Falls Risk

Neural Network Models for Estimation of Balance Control, Detection of Imbalance, and Estimation of Falls Risk

Michael E. Hahn, Arthur M. Farley, Li-Shan Chou
Copyright: © 2006 |Pages: 26
ISBN13: 9781591408369|ISBN10: 1591408369|ISBN13 Softcover: 9781591408376|EISBN13: 9781591408383
DOI: 10.4018/978-1-59140-836-9.ch007
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MLA

Hahn, Michael E., et al. "Neural Network Models for Estimation of Balance Control, Detection of Imbalance, and Estimation of Falls Risk." Computational Intelligence for Movement Sciences: Neural Networks and Other Emerging Techniques, edited by Rezaul Begg and Marimuthu Palaniswami, IGI Global, 2006, pp. 217-242. https://doi.org/10.4018/978-1-59140-836-9.ch007

APA

Hahn, M. E., Farley, A. M., & Chou, L. (2006). Neural Network Models for Estimation of Balance Control, Detection of Imbalance, and Estimation of Falls Risk. In R. Begg & M. Palaniswami (Eds.), Computational Intelligence for Movement Sciences: Neural Networks and Other Emerging Techniques (pp. 217-242). IGI Global. https://doi.org/10.4018/978-1-59140-836-9.ch007

Chicago

Hahn, Michael E., Arthur M. Farley, and Li-Shan Chou. "Neural Network Models for Estimation of Balance Control, Detection of Imbalance, and Estimation of Falls Risk." In Computational Intelligence for Movement Sciences: Neural Networks and Other Emerging Techniques, edited by Rezaul Begg and Marimuthu Palaniswami, 217-242. Hershey, PA: IGI Global, 2006. https://doi.org/10.4018/978-1-59140-836-9.ch007

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Abstract

Gait patterns of the elderly are often adjusted to accommodate for reduced function in the balance control system. Recent work has demonstrated the effectiveness of artificial neural network (ANN) modeling in mapping gait measurements onto descriptions of whole body motion during locomotion. Accurate risk assessment is necessary for reducing incidence of falls. Further development of the balance estimation model has been used to test the feasibility of detecting balance impairment using tasks of sample categorization and falls risk estimation. Model design included an ANN and a statistical discrimination method. Sample categorization results reached accuracy of 0.89. Relative risk was frequently assessed at high or very high risk for experiencing falls in a sample of balance impaired older adults. The current model shows potential for detecting balance impairment and estimating falls risk, thereby indicating the need for referral for falls prevention intervention.

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