Journal of IKEEE (전기전자학회논문지)

Institute of Korean Electrical and Electronics Engineers (한국전기전자학회)
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Improving Performance of Human Action Recognition on Accelerometer Data

가속도 센서 데이터 기반의 행동 인식 모델 성능 향상 기법

  • Nam, Jung-Woo (Dept. of Electronic & Computer Engineering, Seokyeong University) ;
  • Kim, Jin-Heon (Dept. of Electronic & Computer Engineering, Seokyeong University)
  • Received : 2020.06.01
  • Accepted : 2020.06.17
  • Published : 2020.06.30
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Abstract

With a widespread of sensor-rich mobile devices, the analysis of human activities becomes more general and simpler than ever before. In this paper, we propose two deep neural networks that efficiently and accurately perform human activity recognition (HAR) using tri-axial accelerometers. In combination with powerful modern deep learning techniques like batch normalization and LSTM networks, our model outperforms baseline approaches and establishes state-of-the-art results on WISDM dataset.

스마트 모바일 장치의 확산은 인간의 일상 행동 분석을 보다 일반적이고 간단하게 만들었다. 행동 분석은 이미 본인 인증, 감시, 건강 관리 등 많은 분야에서 사용 중이고 그 유용성이 증명되었다. 본 논문에서는 스마트폰의 가속도 센서 신호를 사용하여 효율적이고 정확하게 행동 인식을 수행하는 합성곱 신경망(모델 A)과 순환 신경망까지 적용한(모델 B) 심층 신경망 모델을 제시한다. 모델 A는 batch normalization과 같은 단순한 기법만 적용해도 이전의 결과보다 더 작은 모델로 더 높은 성능을 달성할 수 있다는 것을 보인다. 모델 B는 시계열 데이터 모델링에 주로 사용되는 LSTM 레이어를 추가하여 예측 정확도를 더욱 높일 수 있음을 보인다. 이 모델은 29명의 피실험자를 대상으로 수집한 벤치마크 데이트 세트에서 종합 예측 정확도 97.16%(모델 A), 99.50%(모델 B)를 달성했다.

Keywords

References

  1. O. D. Lara and M. A. Labrador, "A survey on human activity recognition using wearable sensors," IEEE Commun. Surv. Tutorials, vol.15, no.3, pp.1192-1209, 2013. DOI: 10.1109/SURV.2012.110112.00192
  2. C. Jobanputra, J. Bavishi, and N. Doshi, "Human activity recognition: A survey," in Procedia Computer Science, vol.155, pp.698-703, 2019. DOI: 10.1016/j.procs.2019.08.100
  3. A. Avci, S. Bosch, M. Marin-Perianu, R. Marin-Perianu, and P. Havinga, "Activity recognition using inertial sensing for healthcare, wellbeing and sports applications: A survey," in 23th International Conference on Architecture of Computing Systems 2010, ARCS 2010 - Workshop Proceedings, pp.167-176, 2010. DOI: 10.1.1.604.8265
  4. I. Hwang, G. Cha, and S. Oh, "Multi-modal human action recognition using deep neural networks fusing image and inertial sensor data," in IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, vol.2017-Novem, pp.278-283, 2017. DOI: 10.1109/MFI.2017.8170441
  5. C. A. Ronao and S. B. Cho, "Human activity recognition with smartphone sensors using deep learning neural networks," Expert Syst. Appl., vol.59, pp.235-244, 2016. DOI: 10.1016/j.eswa.2016.04.032
  6. A. Ignatov, "Real-time human activity recognition from accelerometer data using Convolutional Neural Networks," Appl. Soft Comput. J., vol.62, pp.915-922, 2018. DOI: 10.1016/j.asoc.2017.09.027
  7. S. Ioffe and C. Szegedy, "Batch normalization: Accelerating deep network training by reducing internal covariate shift," in 32nd International Conference on Machine Learning, ICML 2015, vol.1, pp.448-456, 2015. DOI: 10.5555/3045118.3045167
  8. S. Hochreiter and J. Schmidhuber, "Long Short-Term Memory," Neural Comput., vol.9, no.8, pp.1735-1780, 1997. https://doi.org/10.1162/neco.1997.9.8.1735
  9. J. R. Kwapisz, G. M. Weiss, and S. A. Moore, "Activity recognition using cell phone accelerometers," ACM SIGKDD Explorations Newsletter, vol.12, no.2, pp.74-82, 2011. DOI: 10.1145/1964897.1964918
  10. G. Weiss, "WISDM Project," https://storm.cis.fordham.edu/-gweiss/wisdm/
  11. V. Nair and G. E. Hinton, "Rectified linear units improve Restricted Boltzmann machines," in ICML 2010 - Proceedings, 27th International Conference on Machine Learning, pp.807-814, 2010. DOI: 10.5555/3104322.3104425
  12. K. Smagulova and A. P. James, "A survey on LSTM memristive neural network architectures and applications," Eur. Phys. J. Spec. Top. 2019 22810, vol.228, no.10, pp.2313-2324, 2019. DOI: 10.1140/epjst/e2019-900046-x
  13. R. Fernandez-Fernandez, J. G. Victores, D. Estevez, and C. Balaguer, "Quick, Stat!: A Statistical Analysis of the Quick, Draw! Dataset," CoRR 2019, abs/1907.06417, 2019. DOI: 10.11128/arep.58
  14. M. Abadi et al., "TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems," Preliminary White Paper, 9, 2015.
  15. X. Glorot and Y. Bengio, "Understanding the difficulty of training deep feedforward neural networks," in Journal of Machine Learning Research, vol.9, pp.249-256, 2010.
  16. D. P. Kingma and J. L. Ba, "Adam: A method for stochastic optimization," in 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, 2015.
  17. B. Kolosnjaji and C. Eckert, "Neural networkbased user-independent physical activity recognition for mobile devices," in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol.9375 LNCS, pp.378-386, 2015. DOI: 10.1007/978-3-319-24834-9_44