Browse > Article
http://dx.doi.org/10.7471/ikeee.2020.24.2.523

Improving Performance of Human Action Recognition on Accelerometer Data  

Nam, Jung-Woo (Dept. of Electronic & Computer Engineering, Seokyeong University)
Kim, Jin-Heon (Dept. of Electronic & Computer Engineering, Seokyeong University)
Publication Information
Journal of IKEEE / v.24, no.2, 2020 , pp. 523-528 More about this Journal
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.
Keywords
Human activity recognition; Deep learning; Convolutional Neural Networks; Recurrent Neural Networks; Time-series classification;
Citations & Related Records
연도 인용수 순위
  • Reference
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   DOI
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   DOI
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   DOI
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   DOI
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.   DOI
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   DOI
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   DOI
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 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
16 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.
17 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.