The DOI matches its Crossref record; 38 of the 39 references it cites were found in a registry or an index.
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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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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