Neural network algorithm-based fall detection modelling

Mohd Yusoff, Ainul Husna and Koh, Cheng Zhi and Ngadimon, Khairulnizam and Md Salleh, Salihatun (2020) Neural network algorithm-based fall detection modelling. The International Journal of Integrated Engineering, 12 (3). pp. 138-150. ISSN 2229-838X

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Fall is a major 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 from the previous research. 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 type of input source is divided into 4 different types for training. 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 convergence graph in term of MSE performance, accuracy of regression and non-zero value of 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.

Item Type: Article
Uncontrolled Keywords: Fall detection system; NARnet; mean square error (MSE); scenarios; Levenberg-Marquardt (LM); Scaled Conjugate Gradient (SCG); Resilient Propagation (RP)
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics
Divisions: Faculty of Mechanical and Manufacturing Engineering > Department of Mechanical Engineering
Depositing User: Miss Afiqah Faiqah Mohd Hafiz
Date Deposited: 26 Jan 2022 07:44
Last Modified: 26 Jan 2022 07:44

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