Fall Detection for Elderly Person using Neuro-fuzzy System and Wavelet Transformation
Sang-Hong Lee

Sang-Hong Lee*, Department of Computer Science & Engineering, Anyang University, Anyang-si, Republic of Korea.
Manuscript received on September 18, 2019. | Revised Manuscript received on 25 September, 2019. | Manuscript published on October 10, 2019. | PP: 1730-1733 | Volume-8 Issue-12, October 2019. | Retrieval Number: L32001081219/2019©BEIESP | DOI: 10.35940/ijitee.L3200.1081219
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© 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 new methodology to detect falls and non-falls using a Neural Network with Weighted Fuzzy Membership Functions (NEWFM). Dataset acquired from subjects was applied to NEWFM after carrying out wavelet transforms. In order to test the performance evaluation of the fall detection by the NEWFM, the dataset was separated test set and training set at 2 to 8 and 5 to 5 ratios to carry out experiments. Based on the performance evaluation of the NEWFM, the sensitivity, accuracy, and specificity were shown to be 94.67%, 91.86% and 89.41%, respectively when the test set to the training set at the ratio was 2 to 8 and 91%, 91% and 91%, respectively, when the test set to the training set at the ratio was 5 to 5. This study also compares the performance evaluation of backpropagation (BP) and that of NEWFM.
Keywords: Fall Detection, NEWFM, Acceleration, Wavelet Transform.
Scope of the Article: Aggregation, Integration, and Transformation