| Issue |
E3S Web Conf.
Volume 202, 2020
The 5th International Conference on Energy, Environmental and Information System (ICENIS 2020)
|
|
|---|---|---|
| Article Number | 15008 | |
| Number of page(s) | 12 | |
| Section | Smart Information System | |
| DOI | https://doi.org/10.1051/e3sconf/202020215008 | |
| Published online | 10 November 2020 | |
Human Activity Prediction using Long Short Term Memory
1 Master Program of Information System, School of Postgraduate Studies, Diponegoro University, Semarang, Indonesia
2 Department of Statistics, Faculty of Science and Mathematics, Diponegoro University, Semarang, Indonesia
3 Department of Informatics, Faculty of Science and Mathematics, Diponegoro University, Semarang, Indonesia
* Corresponding author: jakaseptiadi37@gmail.com
Early symptoms of dementia is one of the causes decrease in quality of life. Human activity recognition (HAR) system is proposed to recognize the daily routines which has an important role in detecting early symptoms of dementia. Long Short Term Memory (LSTM) is very useful for sequence analysis that can find the pattern of activities that carried out in daily routines. However, the LSTM model is slow to achieving convergence and take a long time during training. In this paper, we investigated the sequence of actions recorded in smart home sensors data using LSTM model, then the model will be optimized using several optimization methods. The optimization methods were Stochastic Gradient Descent (SGD), Adagrad, Adadelta, RMSProp, and Adam. The results showed that using Adam to optimized LSTM is better than other optimization methods.
© The Authors, published by EDP Sciences, 2020
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