Wavelet Transforms Based Fall Detection with Neuro-Fuzzy Systems Based Feature Selection
Sang-Hong Lee1, Seok-Woo Jang2
1Sang-Hong Lee, Department of Computer Science & Engineering, Anyang University, Anyang-si, Republic of Korea.
2Seok-Woo Jang, Department of Software, Anyang University, Anyang-si, Republic of Korea.
Manuscript received on 01 August 2019. | Revised Manuscript received on 06 August 2019. | Manuscript published on 30 September 2019. | PP: 7498-7502 | Volume-8 Issue-3 September 2019 | Retrieval Number: C5261098319/19©BEIESP | DOI: 10.35940/ijrte.C5261.098319
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This study proposes a method to detect fall with minimum features selected by a non-overlap area distribution measurement (NADM) method. In preprocessing step, wavelet transforms were carried out to extract wavelet coefficients from dataset acquired by subjects. The NADM was used to select the minimum number of features from wavelet coefficients, and then 19 features were finally selected from the 33 features. The performance result of the fall detection was tested with 19 features, and then the sensitivity, accuracy, and specificity were shown to be 95%, 96.13%, and 97.25%, respectively.
Keywords: Fall Detection, Feature Selection, Wavelet Transform, NEWFM.
Scope of the Article: Fuzzy Logics