Basic Activity Recognition from Wearable Sensors Using a Lightweight Deep Neural Network
DOI:
https://doi.org/10.13052/jicts2245-800X.1028Keywords:
human activity recognition- LSTM- deep learning- sensorsAbstract
The field of human activity recognition has undergone a great development, making its presence felt in various sectors such as healthcare and supervision. The identification of fundamental behaviours that occur regularly in our everyday lives can be extremely useful in the development of systems that aid the elderly, as well as opening the door to the detection of more complicated activities in a Smart home environment. Recently, the use of deep learning techniques allowed the extraction of features from sensor’s readings automatically, in a hierarchical way through non-linear transformations. In this study, we propose a deep learning model that can work with raw data without any pre-processing. Several human activities can be recognized by our stacked LSTM network. We demonstrate that our outcomes are comparable to or better than those obtained by traditional feature engineering approaches. Furthermore, our model is lightweight and can be applied on edge devices. Based on our expertise with two datasets, we obtained an accuracy of 97.15% on the UCI HAR dataset and 99% on WISDM dataset.
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X. Zhou, W. Liang, K. I.-K. Wang, H. Wang, L. T. Yang, and Q. Jin, “Deep-Learning-Enhanced Human Activity Recognition for Internet of Healthcare Things,” IEEE Internet of Things Journal, vol. 7, no. 7, pp. 6429–6438, Jul. 2020, doi: 10.1109/JIOT.2020.2985082.
K. K. Htike, O. O. Khalifa, H. A. Mohd Ramli, and M. A. M. Abushariah, “Human activity recognition for video surveillance using sequences of postures,” in The Third International Conference on e-Technologies and Networks for Development (ICeND2014), Apr. 2014, pp. 79–82. doi: 10.1109/ICeND.2014.6991357.
S.-R. Ke, H. L. U. Thuc, Y.-J. Lee, J.-N. Hwang, J.-H. Yoo, and K.-H. Choi, “A Review on Video-Based Human Activity Recognition,” Computers, vol. 2, no. 2, Art. no. 2, Jun. 2013, doi: 10.3390/computers2020088.
W. Lu, F. Fan, J. Chu, P. Jing, and S. Yuting, “Wearable Computing for Internet of Things: A Discriminant Approach for Human Activity Recognition,” IEEE Internet of Things Journal, vol. 6, no. 2, pp. 2749–2759, Apr. 2019, doi: 10.1109/JIOT.2018.2873594.
B. Oluwalade, S. Neela, J. Wawira, T. Adejumo, and S. Purkayastha, “Human Activity Recognition using Deep Learning Models on Smartphones and Smartwatches Sensor Data,” arXiv:2103.03836 [cs, eess], Feb. 2021, [Online]. Available: http://arxiv.org/abs/2103.03836
M. M. Hassan, Md. Z. Uddin, A. Mohamed, and A. Almogren, “A robust human activity recognition system using smartphone sensors and deep learning,” Future Generation Computer Systems, vol. 81, pp. 307–313, Apr. 2018, doi: 10.1016/j.future.2017.11.029.
P. Agarwal and M. Alam, “A Lightweight Deep Learning Model for Human Activity Recognition on Edge Devices,” Procedia Computer Science, vol. 167, pp. 2364–2373, Jan. 2020, doi: 10.1016/j.procs.2020.03.289.
T. Mahmud, A. Q. M. S. Sayyed, S. A. Fattah, and S.-Y. Kung, “A Novel Multi-Stage Training Approach for Human Activity Recognition from Multimodal Wearable Sensor Data Using Deep Neural Network,” IEEE Sensors J., vol. 21, no. 2, pp. 1715–1726, Jan. 2021, doi: 10.1109/JSEN.2020.3015781.
H. Wang et al., “Wearable Sensor-Based Human Activity Recognition Using Hybrid Deep Learning Techniques,” Security and Communication Networks, vol. 2020, p. 2132138, juillet 2020, doi: 10.1155/2020/2132138.
Z. Xiao, X. Xu, H. Xing, F. Song, X. Wang, and B. Zhao, “A federated learning system with enhanced feature extraction for human activity recognition,” Knowledge-Based Systems, vol. 229, p. 107338, Oct. 2021, doi: 10.1016/j.knosys.2021.107338.
S. Mekruksavanich and A. Jitpattanakul, “LSTM Networks Using Smartphone Data for Sensor-Based Human Activity Recognition in Smart Homes,” Sensors, vol. 21, no. 5, Art. no. 5, Jan. 2021, doi: 10.3390/s21051636.
D. Mukherjee, R. Mondal, P. Singh, R. Sarkar, and D. Bhattacharjee, “EnsemConvNet: a deep learning approach for human activity recognition using smartphone sensors for healthcare applications,” Multimedia Tools and Applications, 2020, doi: 10.1007/s11042-020-09537-7.
B. A. Mohammed Hashim and R. Amutha, “Human activity recognition based on smartphone using fast feature dimensionality reduction technique,” J Ambient Intell Human Comput, vol. 12, no. 2, pp. 2365–2374, Feb. 2021, doi: 10.1007/s12652-020-02351-x.
J. Sun, Y. Fu, S. Li, J. He, C. Xu, and L. Tan, “Sequential Human Activity Recognition Based on Deep Convolutional Network and Extreme Learning Machine Using Wearable Sensors,” Journal of Sensors, vol. 2018, p. e8580959, Sep. 2018, doi: 10.1155/2018/8580959.
J. Cao, M. Lin, H. Wang, J. Fang, and Y. Xu, “Towards Activity Recognition through Multidimensional Mobile Data Fusion with a Smartphone and Deep Learning,” Mobile Information Systems, vol. 2021, p. 6615695, avril 2021, doi: 10.1155/2021/6615695.
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278–2324, Nov. 1998, doi: 10.1109/5.726791.
A. Murad and J.-Y. Pyun, “Deep Recurrent Neural Networks for Human Activity Recognition,” Sensors, vol. 17, no. 11, Art. no. 11, Nov. 2017, doi: 10.3390/s17112556.
L. Cong, K. Tang, J. Wang, and Y. Zhang, “AlphaPortfolio for Investment and Economically Interpretable AI,” SSRN Journal, 2020, doi: 10.2139/ssrn.3554486.
J. Gu et al., “Recent advances in convolutional neural networks,” Pattern Recognition, vol. 77, pp. 354–377, May 2018, doi: 10.1016/j.patcog.2017.10.013.
N. Zeng, H. Qiu, Z. Wang, W. Liu, H. Zhang, and Y. Li, “A new switching-delayed-PSO-based optimized SVM algorithm for diagnosis of Alzheimer’s disease,” Neurocomputing, vol. 320, pp. 195–202, Dec. 2018, doi: 10.1016/j.neucom.2018.09.001.
Zakaria Benhaili et al., “Human Activity Recognition Using Stacked Lstm,” The International Conference on Information, Communication & Cybersecurity ICI2C’21, ENSA Khouribga, Morocco, Nov. 10, 2021.
Y. Bengio, “Practical Recommendations for Gradient-Based Training of Deep Architectures,” in Neural Networks: Tricks of the Trade: Second Edition, G. Montavon, G. B. Orr, and K.-R. Müller, Eds. Berlin, Heidelberg: Springer, 2012, pp. 437–478. doi: 10.1007/978-3-642-35289-8_26.
D. Anguita, A. Ghio, L. Oneto, X. Parra, and J. L. Reyes-Ortiz, “A Public Domain Dataset for Human Activity Recognition using Smartphones,” presented at the 21th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium, Apr. 2013.
J. R. Kwapisz, G. M. Weiss, and S. A. Moore, “Activity recognition using cell phone accelerometers,” SIGKDD Explor. Newsl., vol. 12, no. 2, pp. 74–82, Mar. 2011, doi: 10.1145/1964897.1964918.
C. A. Ronao and S. B. Cho, “Human activity recognition using smartphone sensors with two-stage continuous hidden markov models: 2014 10th International Conference on Natural Computation, ICNC 2014,” 2014 10th International Conference on Natural Computation, ICNC 2014, pp. 681–686, 2014, doi: 10.1109/ICNC.2014.6975918.
C. A. Ronao and S.-B. Cho, “Recognizing human activities from smartphone sensors using hierarchical continuous hidden Markov models,” International Journal of Distributed Sensor Networks, vol. 13, no. 1, p. 1550147716683687, Jan. 2017, doi: 10.1177/1550147716683687.
Y. Lin and J. Wu, “A Novel Multichannel Dilated Convolution Neural Network for Human Activity Recognition,” Mathematical Problems in Engineering, vol. 2020, p. e5426532, Jul. 2020, doi: 10.1155/2020/5426532.
Y. Zhao, R. Yang, G. Chevalier, X. Xu, and Z. Zhang, “Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensors,” Mathematical Problems in Engineering, vol. 2018, p. e7316954, Dec. 2018, doi: 10.1155/2018/7316954.
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, Oct. 2016, doi: 10.1016/j.eswa.2016.04.032.
Y. Li, D. Shi, B. Ding, and D. Liu, “Unsupervised Feature Learning for Human Activity Recognition Using Smartphone Sensors,” in Mining Intelligence and Knowledge Exploration, Cham, 2014, pp. 99–107.
C. Catal, S. Tufekci, E. Pirmit, and G. Kocabag, “On the use of ensemble of classifiers for accelerometer-based activity recognition,” Applied Soft Computing, vol. 37, pp. 1018–1022, Dec. 2015, doi: 10.1016/j.asoc.2015.01.025.
M. A. Alsheikh, A. Selim, D. Niyato, L. Doyle, S. Lin, and H.-P. Tan, “Deep Activity Recognition Models with Triaxial Accelerometers,” arXiv:1511.04664 [cs], Oct. 2016, [Online]. Available: http://arxiv.org/abs/1511.04664
D. Ravi, C. Wong, B. Lo, and G.-Z. Yang, “A Deep Learning Approach to on-Node Sensor Data Analytics for Mobile or Wearable Devices,” IEEE J. Biomed. Health Inform., vol. 21, no. 1, pp. 56–64, Jan. 2017, doi: 10.1109/JBHI.2016.2633287.
A. Ignatov, “Real-time human activity recognition from accelerometer data using Convolutional Neural Networks,” Applied Soft Computing, vol. 62, pp. 915–922, Jan. 2018, doi: 10.1016/j.asoc.2017.09.027.
