Selecting Minimized Input Features for Detecting Automatic Fall Detection Based on NEWFM

Dong-Kun Shin; Sang-Hong Lee; Joon-Shik Lim

Selecting Minimized Input Features for Detecting Automatic Fall Detection Based on NEWFM

Dong-Kun Shin, Sang-Hong Lee, Joon-Shik Lim, Journal of Internet Computing and Services, Vol. 10, No. 3, pp. 17-26, Jun. 2009
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Keywords: Fall Detection, Fuzzy Neural Networks, Wavelet Transforms, Feature selection

Abstract

This paper presents a methodology for a fall detection using the feature extraction method based on the neural network with weighted fuzzy membership functions (NEWFM). The distributed non-overlap area measurement method selects the minimized number of input features by removing the worst input features one by one. Nineteen number of wavelet transformed coefficients captured by a triaxial accelerometer are selected as minimized features using the non-overlap area distribution measurement method. The proposed methodology shows that sensitivity, specificity, and accuracy are 95%, 97.25%, and 96.125%, respectively.

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Cite this article

[APA Style]

Shin, D., Lee, S., & Lim, J. (2009). Selecting Minimized Input Features for Detecting Automatic Fall Detection Based on NEWFM. Journal of Internet Computing and Services, 10(3), 17-26.

[IEEE Style]

D. Shin, S. Lee, J. Lim, "Selecting Minimized Input Features for Detecting Automatic Fall Detection Based on NEWFM," Journal of Internet Computing and Services, vol. 10, no. 3, pp. 17-26, 2009.

[ACM Style]

Dong-Kun Shin, Sang-Hong Lee, and Joon-Shik Lim. 2009. Selecting Minimized Input Features for Detecting Automatic Fall Detection Based on NEWFM. Journal of Internet Computing and Services, 10, 3, (2009), 17-26.