Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
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Hierarchical Deep Belief Network for Activity Recognition Using Smartphone Sensor

스마트폰 센서를 이용하여 행동을 인식하기 위한 계층적인 심층 신뢰 신경망

  • Lee, Hyunjin (Division of ICT Engineering, Korea Soongsil Cyber University)
  • Received : 2017.07.16
  • Accepted : 2017.07.25
  • Published : 2017.08.31
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Abstract

Human activity recognition has been studied using various sensors and algorithms. Human activity recognition can be divided into sensor based and vision based on the method. In this paper, we proposed an activity recognition system using acceleration sensor and gyroscope sensor in smartphone among sensor based methods. We used Deep Belief Network (DBN), which is one of the most popular deep learning methods, to improve an accuracy of human activity recognition. DBN uses the entire input set as a common input. However, because of the characteristics of different time window depending on the type of human activity, the RBMs, which is a component of DBN, are configured hierarchically by combining them from different time windows. As a result of applying to real data, The proposed human activity recognition system showed stable precision.

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References

  1. J. Wang, Z. Liu, Y. Wu, and J. Yuan, "Learning Actionlet Ensemble for 3D Human Action Recognition," IEEE Transactions on Pattern Analysis Machine Intelligence, Vol. 36, pp. 914-927, 2013.
  2. P. Li, Y. Wang, Y. Tian, T.S. Zhou, and J.S. Li, “An Automatic User-Adapted Physical Activity Classification Method Using Smartphones,” IEEE Transactions on Biomedical Engineering, Vol. 64, No. 3, pp. 706-714, 2017. https://doi.org/10.1109/TBME.2016.2573045
  3. J. Wen and Z. Wang, "Sensor-based Adaptive Activity Recognition with Dynamically Available Sensors," Neurocomputing, Vol. 218, pp. 307-317, 2016. https://doi.org/10.1016/j.neucom.2016.08.077
  4. J.I. Choi and H.S. Yong, “Activity Data Modeling and Visualization Method for Human Life Activity Recognition,” Journal of Korea Multimedia Society, Vol. 15, No. 8, pp. 1059-1066, 2012. https://doi.org/10.9717/kmms.2012.15.8.1059
  5. M.C. Lee and S.B. Cho, "Accelerometer-based Gesture Recognition Using Hierarchical Recurrent Neural Network with Bidirectional Long Short-Term Memory," Journal of Korean Institute of Information Scientists and Engineers : Software and Applications, Vol. 39, No. 12, pp. 1005-1011, 2012.
  6. D. Anguita, A. Ghio, L. Oneto, X. Parra, and J.L. Reyes-Ortiz, "A Public Domain Dataset for Human Activity Recognition Using Smartphones," Proceeding of European Symposium on Artificial Neural Networks, computational Intelligence and Machine Learning, pp. 437-442, 2013.
  7. A. Stisen, H. Blunck, S. Bhattacharya, T.S. Prentow, M.B. Kjærgaard, A. Dey, et al., "Smart Devices are Different: Assessing and Mitigating Mobile Sensing Heterogeneities for Activity Recognition," Proceeding of 13th Association for Computing Machinery Conference on Embedded Networked Sensor Systems, pp. 127-140, 2015.
  8. A. Reiss, G. Hendeby, and D. Stricker, "A Competitive Approach for Human Activity Recognition on Smartphones," Proceeding of European Symposium an Artificial Neural Networks, pp. 455-460, 2013.
  9. G. Chetty, M. White, and F. Akther, "Smart Phone Based Data Mining For Human Activity Recognition," Procedia Computer Science, Vol. 40, pp. 1181-1187, 2015.
  10. C.A. Ronao and S.B. Cho, "Human Activity Recognition Using Smartphone Sensors with Two-stage Continuous Hidden Markov Models," Proceeding of 10th International Conference on Natural Computation, pp. 686-691, 2014.
  11. C.A. Ronao and S.B. Cho, "Human Activity Recognition with Smartphone Sensors Using Deep Learning Neural Networks," Expert Systems with Applications, Vol. 59, pp. 235-244, 2016. https://doi.org/10.1016/j.eswa.2016.04.032
  12. W. Jiang and Z. Yin, "Human Activity Recognition Using Wearable Sensors by Deep Convolutional Neural Networks," Proceeding of the 23rd Association for Computing Machinery International Conference on Multimedia, pp. 1307-1310, 2015.
  13. H.J. Lee, "Human Activity Recognition Using Multi-temporal Neural Networks," Journal of Digital Contents Society, Vol. 18, No. 3, pp. 151-159, 2017.
  14. Y.C. Lee and C.W. Lee, "Motion Recognition of Smartphone Using Sensor Data," Journal of Korea Multimedia Society, Vol. 17, No. 12, pp. 1437-1445, 2014. https://doi.org/10.9717/kmms.2014.17.12.1437