Neural Network Algorithm-based Fall Detection Modelling - Md Salleh, Salihatun; mohd yusoff, ainul husna; ngadimon, Khairulnizam; Koh , Cheng Zhi (International Journal of Integrated Engineering, 2020)
Falling is a well-known as a threat among elderly people which may lead to injuries or even death. High recognition of developed fall detection model is very significance for the elderly to detect the falls. Related algorithm for the fall detection has been discussed in depth by researcher during the past decade. However, the improvement of model accuracy is still needed. This article presents results of modelling for fall detection system by using nonlinear autoregression neural network NARnet algorithm. The algorithm is trained by network training function; LM, SCG and RP by collocation with threshold-based setting value. Two participants involved in obtaining the acceleration and angular velocity. The training is divided into 4 different type of input source. The selection of the model was based on the comparison of optimization epochs, magnitude of validate error or mean square error (MSE), magnitude of correlation performance, the progress of convergence graph in term of MSE performance, accuracy of regression and non-zero value in autocorrelation graph. The simulated result shows that the training model of Type 2 is the best model with a training result of 6.1551mse, 40 epochs, time 0.06s, and 0.92742 accuracy. The result indicates that LM function produce the better solution when compared to another optimization function. In fact, the model accuracy demonstrated that the proposed method was reliable and efficient.