Authors : Huda Ali Hashim, Salim Latif Mohammed and Sadik Kamel Gharghan
Abstract: This study aimed to localize the elderly while moving in a health care center or at home. Elderly localization was achieved by using a combination of the Received Signal Strength Indicator (RSSI) of Zigbee Anchor Nodes (ANs) and an artificial neural network. A Feed-Forward Neural Network (FFNN) was selected on the basis of the Levenberg-Marquardt (LM) training algorithm to train, test and validate data with the MATLAB Software. Two experiments were conducted in an indoor environment. The first and second experiments used three and four ANs, respectively. The effect of the numbers of ANs and neurons in each hidden layer of the FFNN on localization error was examined in terms of statistical analyses. Results show the better elderly localization accuracy achieved with four ANs compared with that obtained using three ANs. The four ANs achieved a localization error of 0.232 m (for testing) and improved by 65% compared with the three ANs. The results also reveal that the increase in the numbers of ANs and neurons can improve elderly localization accuracy. The second experiment (four ANs) provided a lower minimum localization error than the first experiment. Comparison of the results showed that our proposed method outperformed the other procedures in related literature in terms of localization error.
Huda Ali Hashim, Salim Latif Mohammed and Sadik Kamel Gharghan, 2019. Accurate Localization of Elderly People Based on Neural and Wireless Sensor Networks. Journal of Engineering and Applied Sciences, 14: 3777-3789.