International Journal of Internet, Broadcasting and Communication

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
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Design of Falling Recognition Application System using Deep Learning

  • Kwon, TaeWoo (Department of Information System KwangWoon University Graduate School of Smart Convergence) ;
  • Lee, Jong-Yong (Ingenium College of liberal arts, KwangWoon University) ;
  • Jung, Kye-Dong (Ingenium College of liberal arts, KwangWoon University)
  • Received : 2020.04.10
  • Accepted : 2020.04.22
  • Published : 2020.05.31
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Abstract

Studies are being conducted regarding falling recognition using sensors on smartphonesto recognize falling in human daily life. These studies use a number of sensors, mostly acceleration sensors, gyro sensors, motion sensors, etc. Falling recognition system processes the values of sensor data by using a falling recognition algorithm and classifies behavior based on thresholds. If the threshold is ambiguous, the accuracy will be reduced. To solve this problem, Deep learning was introduced in the behavioral recognition system. Deep learning is a kind of machine learning technique that computers process and categorize input data rather than processing it by man-made algorithms. Thus, in this paper, we propose a falling recognition application system using deep learning based on smartphones. The proposed system is powered by apps on smartphones. It also consists of three layers and uses DataBase as a Service (DBaaS) to handle big data and address data heterogeneity. The proposed system uses deep learning to recognize the user's behavior, it can expect higher accuracy compared to the system in the general rule base.

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References

  1. Kim, N. H., and Yu, Y. S. (2013). Fall recognition algorithm using gravity-weighted 3-axis accelerometer data. Journal of the Institute of Electronics and Information Engineers, 50(6), 254-259. DOI: https://doi.org/10.5573/ieek.2013.50.6.254
  2. Su, X., Tong, H., and Ji, P. (2014). Activity recognition with smartphone sensors. Tsinghua science and technology, 19(3), 235-249. DOI: https://doi.org/10.1109/TST.2014.6838194
  3. Özdemir, A., and Barshan, B. (2014). Detecting falls with wearable sensors using machine learning techniques. Sensors, 14(6), 10691-10708. DOI: https://doi.org/10.3390/s140610691
  4. Kwon, T.W, Lee, J. Y., and Jung, K. D. (2017). Design of Cloud-based Context-aware System Based on Falling Type. International Journal of Internet, Broadcasting and Communication, 9(4), 44-50. IJIBC. DOI: http://dx.doi.org/10.7236/IJIBC.2018.10.3.42
  5. Kwon, T.W, Yun, D.Y, Lee, J.Y and Jung, K.D. (2018). A Study of Behaviors Recognition Method using Smartphone Sensors. Journal of Engineering and Applied Sciences, (13): 8722-8725. DOI: https://doi.org/10.36478/jeasci.2018.8722.8725
  6. Batista, G. E., Prati, R. C., and Monard, M. C. (2004). A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD explorations newsletter, 6(1), 20-29. DOI: https://doi.org/10.1145/1007730.1007735
  7. Rieck, K., Trinius, P., Willems, C., and Holz, T. (2011). Automatic analysis of malware behavior using machine learning. Journal of Computer Security, 19(4), 639-668. DOI: https://doi.org/10.3233/JCS-2010-0410
  8. Shin, S., and Cha, J. (2018). Human Activity Recognition System Using Multimodal Sensor and Deep Learning Based on LSTM. Transactions of the Korean Society of Mechanical Engineers. A, 42(2), 111-121. DOI: https://doi.org/10.3795/KSME-A.2018.42.2.111
  9. Lehner, W., and Sattler, K. U. (2010, March). Database as a service (DBaaS). In 2010 IEEE 26th International Conference on Data Engineering (ICDE 2010) (pp. 1216-1217). IEEE. DOI: https://doi.org/10.1109/ICDE.2010.5447723
  10. Lee, S., Kim, H., Seok, H., & Nang, J. (2017). Comparison of Fine-Tuned Convolutional Neural Networks for Clipart Style Classification. International Journal of Internet, Broadcasting and Communication, 1(1), 4. IJIBC. DOI: https://doi.org/10.7236/IJIBC.2017.9.4.1