Buy this Article for $10.95

Have a coupon or promotional code? Enter it here:

When you buy this you'll get access to the ePub version, a downloadable PDF, and the ability to print the full article.


  1. Lin, I-Ching MD, PhD
  2. Yang, Chia-Chi PhD
  3. Lai, Yi-Horng PhD
  4. Guo, Lan-Yuen PhD


Background and Purpose: Optimal approaches in fall risk assessment involve interdisciplinary collaboration of assessment. This current work aimed at screening the fall risk characteristics from the objective balance and mobility tests between older fallers and nonfallers and further assessing the feasibility of 2 statistical dimensionality reduction models, Linear Discriminant Analysis (LDA) and Generalized Discriminant Analysis (GDA) for discriminating older nonspecific fallers. We hypothesized that the high-dimensionality objective sensor-based parameters, followed by a feature selection and dimensionality reduction process, would be able to discriminate older nonspecific fallers.


Methods: Thirty-one community-living older individuals who were older than 60 years (faller: n = 15; nonfaller: n = 16) were recruited. The measurements include gait, balance, and ankle proprioception performances. LDA and GDA were further applied to obtain more discriminative feature space. Receiver-operating characteristic (ROC) curves were constructed to compare the classification quality in all the features.


Results: Although some features in single objective measure reached statistical significance, the original features still resulted in high within-class and low between-class variances in the feature space. By further applying LDA and GDA on the original features, the performance of LDA in the feature space was improved. The area under the curve of ROC was GDA dimensionality reduction feature (1), LDA dimensionality reduction feature (0.99), proprioception (0.752), inertial measurement unit (0.745), and center of pressure (0.72), respectively.


Conclusions: Experimental results showed the GDA feature has the best classification quality and the additional advantage in combination of interdisciplinary multifactorial fall risk assessment.