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Distilling the Knowledge From Handcrafted Features for Human Activity Recognition
Open accessAbstract
Human activity recognition is a core problem in intelligent automation systems due to its far-reaching applications including ubiquitous computing, health-care services, and smart living. Due to the nonintrusive property of smartphones, smartphone sensors are widely used for the identification of human activities. However, unlike applications in vision or data mining domain, feature embedding from deep neural networks performs much worse in terms of recognition accuracy than properly designed handcrafted features. In this paper, we posit that feature embedding from deep neural networks may convey complementary information and propose a novel knowledge distilling strategy to improve its performance. More specifically, an efficient shallow network, i.e., single-layer feedforward neural network (SLFN), with handcrafted features is utilized to assist a deep long short-term memory (LSTM) network. On the one hand, the deep LSTM network is able to learn features from raw sensory data to encode temporal dependencies. On the other hand, the deep LSTM network can also learn from SLFN to mimic how it generalizes. Experimental results demonstrate the superiority of the proposed method in terms of recognition accuracy against several state-of-the-art methods in the literature.
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“However, these complex network framework suffered from low ef ciency and can hardly meet real-time requirements in practice applications.”
From InnoHAR: A Deep Neural Network for Complex Human Activity Recognition (2019) · cites this paper as [14]
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
(2014) · Cited by 33,419
Distilling the Knowledge in a Neural Network
Hinton, G., Vinyals, O., & Dean, J. (2015) · arXiv · doi:10.48550/arxiv.1503.02531 · Cited by 14,204
Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition
Ordóñez, F., & Roggen, D. (2016) · Sensors · doi:10.3390/s16010115 · Cited by 2,761 · cited 2×
“presented a deep convolutional LSTM network for human activity recognition [19].”
Background(2013) · RECERCAT (Consorci de Serveis Universitaris de Catalunya) · Cited by 1,959
Deeply-Supervised NetsPreprint
Lee, C., Xie, S., Gallagher, P., Zhang, Z., & Tu, Z. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1409.5185 · Cited by 1,693
Generating Text with Recurrent Neural Networks
Sutskever, I., Martens, J., & Hinton, G. E. (2011) · Cited by 1,266
Human activity recognition with smartphone sensors using deep learning neural networks
Ronao, C. A., & Cho, S.-B. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.04.032 · Cited by 1,215
Anguita, D., Ghio, A., Oneto, L., Parra, X., & Reyes-Ortiz, J. L. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-35395-6_30 · Cited by 998
Deeplysupervised netsPreprint
Lee, C., Xie, S., Gallagher, P., Zhang, Z., & Tu, Z. (2015) · Cited by 850
Feature Extraction Construction and Selection: A Data Mining Perspective
MTW, Liu, H., & Motoda, H. (1999) · Journal of the American Statistical Association · doi:10.2307/2669967 · Cited by 719
Human Activity Recognition Using Wearable Sensors by Deep Convolutional Neural Networks
Jiang, W., & Yin, Z. (2015) · Proceedings of the 23rd ACM international conference on Multimedia · doi:10.1145/2733373.2806333 · Cited by 676
Feature Extraction, Construction and Selection
(1998) · Springer eBooks · doi:10.1007/978-1-4615-5725-8 · Cited by 576
Fleury, A., Vacher, M., & Noury, N. (2009) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2009.2037317 · Cited by 499
A comprehensive evaluation of random vector functional link networks
Zhang, L., & Suganthan, P. N. (2015) · Information Sciences · doi:10.1016/j.ins.2015.09.025 · Cited by 471
Self-citationYang, J.-Y., Wang, J.-S., & Chen, Y.-P. (2008) · Pattern Recognition Letters · doi:10.1016/j.patrec.2008.08.002 · Cited by 335
A Comparative Study on Human Activity Recognition Using Inertial Sensors in a Smartphone
Wang, A., Chen, G., Yang, J., Zhao, S., & Chang, C.-Y. (2016) · IEEE Sensors Journal · doi:10.1109/jsen.2016.2545708 · Cited by 324 · cited 8×
“The shallow models include HNN [20], FW KNN [12], FW NB [12], HF-SVM [21], two-stage CHMM [22], SRC-SVD [23], and FMM-CART [24]; and the deep models contain SAEs-c [25], Convnet [17], HCF Convnet [26], and tFFT Convnet [17].”
BackgroundMethodInfluentialRobust Human Activity Recognition Using Smartphone Sensors via CT-PCA and Online SVM
Chen, Z., Zhu, Q., Soh, Y. C., & Zhang, L. (2017) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2017.2712746 · Cited by 247
Self-citationJalal, A., Uddin, M. Z., & Kim, T. (2012) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2012.6311329 · Cited by 184
Deep Convolutional Neural Networks for Human Activity Recognition with Smartphone Sensors
Ronao, C. A., & Cho, S.-B. (2015) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-26561-2_6 · Cited by 176
A Pattern Mining Approach to Sensor-Based Human Activity Recognition
Gu, T., Wang, L., Wu, Z., Tao, X., & Lü, J. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2010.184 · Cited by 138
Geng, Y., Chen, J., Fu, R., Bao, G., & Pahlavan, K. (2015) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2015.2416186 · Cited by 136
Smart Random Neural Network Controller for HVAC Using Cloud Computing Technology
Javed, A., Larijani, H., Ahmadinia, A., & Gibson, D. (2016) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2016.2597746 · Cited by 129
Human activity recognition using smartphone sensors with two-stage continuous hidden Markov models
Ronao, C. A., & Cho, S.-B. (2014) · International Conference on Natural Computation (ICNC) · doi:10.1109/icnc.2014.6975918 · Cited by 126
3-D Posture and Gesture Recognition for Interactivity in Smart Spaces
Tran, C., & Trivedi, M. M. (2011) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2011.2172450 · Cited by 121
Designing a Robust Activity Recognition Framework for Health and Exergaming Using Wearable Sensors
Alshurafa, N. I., Xu, W., Liu, J. J., Huang, M.-C., Mortazavi, B. J., Roberts, C. K., & Sarrafzadeh, M. (2013) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2013.2287504 · Cited by 104
Toward Personalized Activity Recognition Systems With a Semipopulation Approach
Hong, J.-H., Ramos, J., & Dey, A. K. (2016) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2015.2489688 · Cited by 94
Unsupervised Feature Learning for Human Activity Recognition Using Smartphone Sensors
Li, Y., Shi, D., Ding, B., & Liu, D. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-13817-6_11 · Cited by 89
Ghasemzadeh, H. G., & Jafari, R. (2010) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2010.2089990 · Cited by 88
Multicolumn bidirectional long short-term memory for mobile devices-based human activity recognition
Tao, D., Wen, Y., & Hong, R. (2016) · IEEE Internet of Things Journal · doi:10.1109/jiot.2016.2561962 · Cited by 83
Rank Preserving Discriminant Analysis for Human Behavior Recognition on Wireless Sensor Networks
Tao, D., Jin, L., Wang, Y., & Li, X. (2013) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2013.2255061 · Cited by 81
Parietal and temporal activity during a multimodal dance video game: An fNIRS study
Tachibana, A., Noah, J. A., Bronner, S., Ono, Y., & Onozuka, M. (2011) · Neuroscience Letters · doi:10.1016/j.neulet.2011.08.023 · Cited by 72
Rana, R. K., Kusý, B., Wall, J., & Hu, W. S. (2015) · Energy · doi:10.1016/j.energy.2015.09.002 · Cited by 63
Chen, X., Liu, X., Wang, Y., Gales, M., & Woodland, P. C. (2016) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2016.2598304 · Cited by 57
Evaluation of hyperbox neural network learning for classification
Eastwood, M., & Jayne, C. (2014) · Neurocomputing · doi:10.1016/j.neucom.2013.11.011 · Cited by 16
A hybrid FMM-CART model for human activity recognition
Seera, M., Loo, C. K., & Lim, C. P. (2014) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2014.6973904 · Cited by 13
Research works printed but not linked: 1
- smola, 1998, Learning with kernels
Cited by
Sorted by citations
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021) · Journal of Big Data · doi:10.1186/s40537-021-00444-8 · Cited by 7,906
Deep Learning Models for Real-time Human Activity Recognition with Smartphones
Wan, S., Qi, L., Xu, X., Tong, C., & Gu, Z. (2019) · Mobile Networks and Applications · doi:10.1007/s11036-019-01445-x · Cited by 597
InnoHAR: A Deep Neural Network for Complex Human Activity Recognition
Xu, C., Chai, D., He, J., Zhang, X., & Duan, S. (2019) · IEEE Access · doi:10.1109/access.2018.2890675 · Cited by 333
Model Compression for Deep Neural Networks: A Survey
Li, Z., Li, H., & Meng, L. (2023) · Computers · doi:10.3390/computers12030060 · Cited by 342
Human Activity Recognition Using Inertial Sensors in a Smartphone: An Overview
Lima, W. S., Souto, E., El‐Khatib, K., Jalali, R., & da Gama, J. M. P. (2019) · Sensors · doi:10.3390/s19143213 · Cited by 273
Metadata
- Authors
- Chen, Z., Zhang, L., Cao, Z., & Guo, J.
- Affiliations
- Nanyang Technological University (SG)
- Advanced Digital Sciences Center (SG)
- Guangdong University of Technology (CN)
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2018-10
- Volume
- 14
- Issue
- 10
- Pages
- 4334-4342
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W2781486669
- Cited by
- 160
- References
- 39
- Citations by year
- 2026: 10 · 2025: 18 · 2024: 23 · 2023: 31 · 2022: 20 · 2021: 22 · 2020: 20 · 2019: 12 · 2018: 4
- FWCI
- 7.19
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- IEEE Transactions on Industrial Informatics (publishedVersion)
- DR-NTU (Nanyang Technological University) (submittedVersion, public-domain)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Keywords
- human activity recognition, handcrafted features, deep neural network, LSTM, single-layer feedforward neural network, smartphone sensors, temporal dependencies, feature embedding, model compression
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