Abstract
One of the main issues regarding the monitoring of persons in a smart home environment is the accuracy of the daily control of the person, the health prevention and the timely prediction of abnormal situations. To tackle this problem, this work proposes the use of an improved version of the Elman Neural Network (Elman-NN). In order to minimize the error between inputs and desired outputs, we optimize some criteria of the network to gain good results. We propose to use the Differential Evolution algorithm in the learning step of the Elman-NN to evolve the error performance. Our proposed model is responsible to predict the activities of the monitored elderly and to detect any state changes. This hybridization will help to optimize the weight and the bias of the network to achieve our objective function and to obtain a good network. The experimental results reveal that the proposed model is satisfactory for elderly person’s movement prediction.
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Acknowledgments
The authors would like to thank all teams of the project “e-health monitoring open data project”.
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Liouane, Z., Lemlouma, T., Roose, P., Weis, F., Messaoud, H. (2017). An Improved Elman Neural Network for Daily Living Activities Recognition. In: Madureira, A., Abraham, A., Gamboa, D., Novais, P. (eds) Intelligent Systems Design and Applications. ISDA 2016. Advances in Intelligent Systems and Computing, vol 557. Springer, Cham. https://doi.org/10.1007/978-3-319-53480-0_69
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DOI: https://doi.org/10.1007/978-3-319-53480-0_69
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