Abstract
Fall Detection is one of the most interesting and challenging research topics in the world today because of its implications in society and also because the complexity of processing Time Series (TS). Plenty of research has been published in the literature, several of them introducing Deep Learning (DL) Neural Network (NN) as the modelling element. In this study we analyse one of these contributions and address several enhancement using TS data augmentation and dropout. Moreover, the possibility of reducing the NN to make it lighter has been studied. The NN has been implemented using Keras in Python and the experimentation includes an staged fall publicly available data set. Results show the TS data augmentation together with dropout helped in learning a more robust and precise model. Future work includes introducing different types of cross-validation as well as introducing other types of DL models more suitable for TS.
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References
Jahanjoo, A., Naderan, M., Rashti, M.J.: Detection and multi–class classification of falling in elderly people by deep belief network algorithms. Ambient Intell. Human. Comput., 1–21 (2020)
Khojasteh, S.B., Villar, J.R., Chira, C., Suárez, V.M.G., de la Cal, E.A.: Improving fall detection using an on-wrist wearable accelerometer. Sensors 18(5), 1350 (2018)
Zhang, T., Wang, J., Xu, L., Liu, P.: Fall detection by wearable sensor and one-class SVM algorithm. In: Huang, D.S., Li, K., Irwin, G. (eds.) Intelligent Computing in Signal Processing and Pattern Recognition. Lecture Notes in Control and Information Systems, vol. 345, pp. 858–863. Springer, Heidelberg (2006)
Wu, F., Zhao, H., Zhao, Y., Zhong, H.: Development of a wearable-sensor-based fall detection system. Int. J. Telemedicine Appl. 2015, 11 (2015)
Bourke, A., O’Brien, J., Lyons, G.: Evaluation of a threshold-based triaxial accelerometer fall detection algorithm. Gait Posture 26, 194–199 (2007)
Fang, Y.C., Dzeng, R.J.: A smartphone-based detection of fall portents for construction workers. Procedia Eng. 85, 147–156 (2014)
Fang, Y.C., Dzeng, R.J.: Accelerometer-based fall-portent detection algorithm for construction tiling operation. Autom. Constr. 84, 214–230 (2017)
Huynh, Q.T., Nguyen, U.D., Irazabal, L.B., Ghassemian, N., Tran, B.Q.: Optimization of an accelerometer and gyroscope-based fall detection algorithm. J. Sens. 2015, 8 (2015)
Kangas, M., Konttila, A., Lindgren, P., Winblad, I., Jämsaä, T.: Comparison of low-complexity fall detection algorithms for body attached accelerometers. Gait Posture 28, 285–291 (2008)
Hakim, A., Huq, M.S., Shanta, S., Ibrahim, B.: Smartphone based data mining for fall detection: analysis and design. Procedia Comput. Sci. 105, 46–51 (2017)
Villar, J.R., de la Cal, E.A., Fáñez, M., Suárez, V.M.G., Sedano, J.: User-centered fall detection using supervised, on-line learning and transfer learning. Progress in AI 8(4), 453–474 (2019)
Fáñez, M., Villar, J.R., de la Cal, E.A., Suárez, V.M.G., Sedano, J.: Feature clustering to improve fall detection: a preliminary study. SOCO 2019, 219–228 (2019)
Godfrey, A.: Wearables for independent living in older adults: gait and falls. Maturitas 100, 16–26 (2017)
Igual, R., Medrano, C., Plaza, I.: Challenges, issues and trends in fall detection systems. BioMedical Eng. OnLine 12, 66 (2013)
Casilari-P’erez, E., Lagos, F.G.: A comprehensive study on the use of artificial neural networks in wearable fall detection systems. Expert Syst. Appl. 138 (2019)
Wu, X., Cheng, L., Chu, C.H., Kim, J.: Using deep learning and smartphone for automatic detection of fall and daily activities. In: Lecture Notes in Computer Science, vol. 11924, pp. 61–74 (2019)
Casilari, E., Lora-Rivera, R., García-Lagos, F.: A wearable fall detection system using deep learning. In: Advances and Trends in Artificial Intelligence, pp. 445–456 (2019)
Casilari, E.: Umafall: a multisensor dataset for the research on automatic fall detection. Procedia Comput. Sci. 110, 32–39 (2017)
Acknowledgment
This research has been funded by the Spanish Ministry of Science and Innovation under project MINECO-TIN2017-84804-R and by the Grant FCGRUPIN-IDI/2018/000226 project from the Asturias Regional Government.
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González, E.G., Villar, J.R., de la Cal, E. (2021). Time Series Data Augmentation and Dropout Roles in Deep Learning Applied to Fall Detection. In: Herrero, Á., Cambra, C., Urda, D., Sedano, J., Quintián, H., Corchado, E. (eds) 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020). SOCO 2020. Advances in Intelligent Systems and Computing, vol 1268. Springer, Cham. https://doi.org/10.1007/978-3-030-57802-2_54
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DOI: https://doi.org/10.1007/978-3-030-57802-2_54
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