Deep Convolutional Neural Networks for Human Activity Recognition with Smartphone Sensors
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A majority of CNNs consist of five to nine layers, usually including two to three convolutional layers, two to three max-pooling layers, followed by one to two fully connected layers before the output layer.
“A majority of the CNNs consist of five to nine layers [23,63,64,113,114,165,166,167,168], usually including two to three convolutional layers, two to three max-pooling layers, followed by one to two fully connected layers before feeding the feature representation into the output layer (softmax layer in most cases).”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [166]
Cites
Convolutional networks for images, speech, and time series
LeCun, Y., & Bengio, Y. (1998) · HAL · doi:10.5555/303568.303704 · Cited by 4,637 · see citing works
Activity Recognition from User-Annotated Acceleration Data
Bao, L., & Intille, S. S. (2004) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-24646-6_1 · Cited by 3,147 · see citing works
Activity recognition using cell phone accelerometers
Kwapisz, J. R., Weiss, G. M., & Moore, S. A. (2011) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1964897.1964918 · Cited by 2,912 · cited 1× · see citing works
“Years later, accelerometers in smartphones were utilized by Kwapisz et al. [5].”
MethodPractical Recommendations for Gradient-Based Training of Deep Architectures
Bengio, Y. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-35289-8_26 · Cited by 2,259 · see citing works
(2013) · RECERCAT (Consorci de Serveis Universitaris de Catalunya) · Cited by 1,959 · see citing works
Convolutional Neural Networks for Human Activity Recognition using Mobile Sensors
Zeng, M., Nguyen, L. T., Yu, B., Mengshoel, O. J., Zhu, J., Wu, P., & Zhang, J. (2014) · Proceedings of the 6th International Conference on Mobile Computing, Applications and Services · doi:10.4108/icst.mobicase.2014.257786 · Cited by 876 · see citing works
Classification Accuracies of Physical Activities Using Smartphone Motion Sensors
Wu, W., Dasgupta, S., Ramirez, E. E., Peterson, C., & Norman, G. J. (2012) · Journal of Medical Internet Research · doi:10.2196/jmir.2208 · Cited by 316 · see citing works
Learning Features for Activity Recognition with Shift-Invariant Sparse Coding
Vollmer, C., Groß, H., & Eggert, J. P. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40728-4_46 · Cited by 38 · see citing works
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Deep learning for sensor-based activity recognition: A survey
Wang, J., Chen, Y., Hao, S., Peng, X., & Hu, L. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2018.02.010 · Cited by 2,014 · see citing works
“For multi-modal signals, it is better to use CNN since (Bhattacharya and Lane, 2016) Body-worn, ambiemt RBM Gesture, ADL, transportation Self, D01 (Chen and Xue, 2015) Body-worn CNN ADL Self (Chen et al., 2016b) Body-worn CNN ADL D06 (Edel and K¨oppe, 2016) Body-worn RNN ADL D01, D04, Self (Fang and Hu, 2014) Object,…”
Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances
Zhang, S., Li, Y., Zhang, S., Shahabi, F., Xia, S., Deng, Y., & Alshurafa, N. (2022) · Sensors · doi:10.3390/s22041476 · Cited by 625 · see citing works
“A majority of the CNNs consist of 5 to 9 layers [67, 91, 193, 232, 272, 300, 313], [130], [96], usually including 2 to 3 convolutional layers, 2 to 3 max pooling layers, followed by 1 to 2 fully connected layers before feeding the feature representation into the output layer (softmax layer in most cases).”
BackgroundDeep Learning for Sensor-based Human Activity Recognition
Chen, K., Zhang, D., Yao, L., Guo, B., Yu, Z., & Liu, Y. (2022) · ACM Computing Surveys · doi:10.1145/3447744 · Cited by 690 · see citing works
“sor signals so that local temporal dependencies can be captured. Some works employed one-dimensional (1D) convolutions on the individual univariate time series signals for temporal feature extraction [37, 46, 125, 126, 164]. When there were multiple sensors or multiple axes, multivariate time series would be yielded,…”
MethodBianchi, V., Bassoli, M., Lombardo, G., Fornacciari, P., Mordonini, M., & De Munari, I. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2920283 · Cited by 466 · see citing works
A survey on wearable sensor modality centred human activity recognition in health care
Wang, Y., Cang, S., & Yu, H. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.04.057 · Cited by 409 · see citing works
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
Chen, K., Zhang, D., Yao, L., Guo, B., Yu, Z., & Liu, Y. (2020) · arXiv · doi:10.48550/arxiv.2001.07416 · Cited by 314 · see citing works
“sor signals so that local temporal dependencies can be captured. Some works employed one-dimensional (1D) convolutions on the individual univariate time series signals for temporal feature extraction [37, 46, 125, 126, 164]. When there were multiple sensors or multiple axes, multivariate time series would be yielded,…”
MethodKanjo, E., Younis, E. M. G., & Ang, C. S. (2019) · Information Fusion · doi:10.1016/j.inffus.2018.09.001 · Cited by 293 · see citing works
Convolutional Neural Networks for Human Activity Recognition Using Body-Worn Sensors
Moya Rueda, F., Grzeszick, R., Fink, G. A., Feldhorst, S., & Ten Hompel, M. (2018) · Informatics · doi:10.3390/informatics5020026 · Cited by 196 · see citing works
Zeng, M., Gao, H., Yu, T., Mengshoel, O. J., Langseth, H., Lane, I., & Liu, X. (2018) · Proceedings of the 2018 ACM International Symposium on Wearable Computers · doi:10.1145/3267242.3267286 · Cited by 185 · see citing works
“DNNs also provided promising results in HAR domain ( e.g., [20, 22, 10]).”
BackgroundDistilling the Knowledge From Handcrafted Features for Human Activity Recognition
Chen, Z., Zhang, L., Cao, Z., & Guo, J. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2789925 · Cited by 160 · see citing works
Abedin, A., Ehsanpour, M., Shi, Q., Rezatofighi, H., & Ranasinghe, D. C. (2021) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3448083 · Cited by 107 · see citing works
Deep Neural Network based Human Activity Recognition for the Order Picking Process
Grzeszick, R., Lenk, J. M., Rueda, F. M., Fink, G. A., Feldhorst, S., & ten Hompel, M. (2017) · Proceedings of the 4th International Workshop on Sensor-based Activity Recognition and Interaction · doi:10.1145/3134230.3134231 · Cited by 93 · see citing works
Lv, M., Xu, W., & Chen, T. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.06.051 · Cited by 64 · see citing works
Face Recognition Based on Deep Learning and FPGA for Ethnicity Identification
AlBdairi, A. J. A., Xiao, Z., Alkhayyat, A., Humaidi, A. J., Fadhel, M. A., Taher, B. H., Alzubaidi, L., Santamaría, J., & Al-Shamma, O. (2022) · Applied Sciences · doi:10.3390/app12052605 · Cited by 54 · see citing works
User adaptation of convolutional neural network for human activity recognition
Matsui, S., Inoue, N., Akagi, Y., Nagino, G., & Shinoda, K. (2017) · 2017 25th European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco.2017.8081308 · Cited by 46 · see citing works
Learning Deep and Shallow Features for Human Activity Recognition
Sani, S., Massie, S., Wiratunga, N., & Cooper, K. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-63558-3_40 · Cited by 45 · see citing works
Miao, S., Chen, L., Hu, R., & Luo, Y. (2022) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3550331 · Cited by 36 · see citing works
Towards deep clustering of human activities from wearables
Abedin, A., Motlagh, F., Shi, Q., Rezatofighi, H., & Ranasinghe, D. (2020) · Proceedings of the 2020 International Symposium on Wearable Computers · doi:10.1145/3410531.3414312 · Cited by 27 · see citing works
- Authors
- Ronao, C. A., & Cho, S.-B.
- Affiliations
- Yonsei University (KR)
- Corresponding author
- Cho, Sung-Bae
- ORCID
- Cho, Sung-Bae
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2015
- Pages
- 46-53
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2200813474
- Cited by
- 176
- References
- 9
- Citations by year
- 2025: 4 · 2024: 17 · 2023: 17 · 2022: 22 · 2021: 37 · 2020: 22 · 2019: 22 · 2018: 26 · 2017: 7 · 2016: 2
- FWCI
- 4.78
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion, public-domain)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Keywords
- human activity recognition, smartphone sensors, convolutional neural networks, time-series sensor data, multivariate time series, translation invariance, kernel size, feature complexity
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