Predictive Models for Evaluating Cognitive Ability in Dementia Diagnosis Applications Based on Inertia- and Gait-Related Parameters

Wei Hsin Wang, Yu Liang Hsu, Pau Choo Chung, Ming Chyi Pai

研究成果: Article

1 引文 斯高帕斯(Scopus)


An impaired cognitive ability is an important indicator of dementia-related disease. Accordingly, this paper utilizes 12 inertia-related features, 19 gait-related features, and 2 balance-related features to analyze the gait performance of subjects while performing single-task and dual-task walking tests and balance tests. The features most closely correlated with the cognitive ability of the subjects are extracted via a correlation analysis method and a sequential forward floating selection (SFFS) algorithm, respectively. The extracted features are then used to predict the cognitive assessment screening instrument (CASI) and mini mental state examination (MMSE) scores of the subjects using three different prediction models, namely a linear regression model, a nonlinear regression model, and a feedforward neural network (FNN) model. It is shown that the optimal prediction performance (i.e., prediction error = 7.89±5.86 for the CASI score and prediction error = 3.21±2.86 for the MMSE score) is obtained using the SFFS feature selection method and the FNN model. Overall, the results show that the feature selection and modeling methods proposed in this paper provide an accurate and objective means of evaluating the cognitive ability of individuals for dementia diagnosis purposes.

頁(從 - 到)3338-3350
期刊IEEE Sensors Journal
出版狀態Published - 2018 四月 15


All Science Journal Classification (ASJC) codes

  • Instrumentation
  • Electrical and Electronic Engineering