Convolutional Neural Networks for Human Activity Recognition using Mobile Sensors
Open accessAbstract
A variety of real-life mobile sensing applications are becoming available, especially in the life-logging, fitness tracking and health monitoring domains. These applications use mobile sensors embedded in smart phones to recognize human activities in order to get a better understanding of human beha
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The Skoda dataset has been employed to evaluate decision fusion techniques in sensor networks and deep learning techniques, which makes it a suitable dataset to evaluate the proposed solution.
“The Skoda dataset has been employed to evaluate decision fusion techniques in sensor networks [38] and deep learning techniques [23,43], which makes it a suitable dataset to evaluate our proposed solution.”
From Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition (2016) · cites this paper as [23]
These results are consistent with those presented in [23].
“These results are consistent with those presented in [23].”
From Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition (2016) · cites this paper as [23]
The source reports activity-recognition accuracies in “B. Classification Accuracy,” but the citing sentence does not identify what “these results” are. Without the citing results, their consistency with the source’s results cannot be assessed. (LLM, conf=0.74)
Experimental — accuracy not yet measuredDeepConvLSTM outperforms other deep non-recurrent approaches on the Skoda dataset, improving the best reported result by 6%.
“From the results in Table 9, we can see that DeepConvLSTM outperforms other deep non-recurrent approaches on the Skoda dataset, improving the best reported result by 6%.”
From Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition (2016) · cites this paper as [23]
The Classification Accuracy section reports a CNN result of 88.19% on Skoda, but does not compare DeepConvLSTM with that model or report a 6% improvement. Searches for DeepConvLSTM, recurrent approaches, and the 6% Skoda comparison found no passage addressing the claimed result; the paper’s own 4.41% comparison concerns CNN versus PCA-ECDF. (LLM, conf=0.76)
Experimental — accuracy not yet measuredThe choice of 1D-CNN kernel size normally falls in the range of 1 × 3 to 1 × 5, with exceptions of 1 × 8, 2 × 101, and 1 × 20 in some studies.
“The choice of 1D-CNN kernel size normally falls in the range of 1 × 3 to 1 × 5 (with exceptions in [22,63,64] where kernels of size 1 × 8, 2 × 101, and 1 × 20 are adopted).”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [22]
HAR research has seen an explosion in Deep Learning methods, resulting in an increase in recognition accuracy.
“In terms of algorithmic implementation, HAR research has seen an explosion in Deep Learning (DL) methods, resulting in an increase in recognition accuracy [5], [7].”
From Human Activity Recognition Using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey (2020) · cites this paper as [5]
Supervised deep learning techniques have the capacity to automatically extract features from data and have proven valuable in HAR applications.
“Supervised deep learning techniques have seen development and utilization within HAR systems [13,63,64]. These techniques have the capacity to automatically extract features from data and have proven valuable in HAR applications.”
From Reducing Label Dependency in Human Activity Recognition with Wearables: From Supervised Learning to Novel Weakly Self-Supervised Approaches (2025) · cites this paper as [63]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
Object recognition from local scale-invariant features
Lowe, D. G. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.790410 · Cited by 16,472 · 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
“This is because these three activities are relatively similar [19].”
BackgroundCollecting complex activity datasets in highly rich networked sensor environments
Roggen, D., Calatroni, A., Rossi, M., Holleczek, T., Forster, K., Troster, G., Lukowicz, P., Bannach, D., Pirkl, G., Ferscha, A., et al. (2010) · 2010 Seventh International Conference on Networked Sensing Systems (INSS) · doi:10.1109/inss.2010.5573462 · Cited by 788 · see citing works
The Opportunity challenge: A benchmark database for on-body sensor-based activity recognition
Chavarriaga, R., Sagha, H., Calatroni, A., Digumarti, S. T., Tröster, G., Millán, J. D. R., & Roggen, D. (2013) · Pattern Recognition Letters · doi:10.1016/j.patrec.2012.12.014 · Cited by 742 · see citing works
Flagged first, then by citations
Research works printed but not linked: 29
- O. Abdel-Hamid, A.-r. Mohamed, H. Jiang, and G. Penn. Applying convolutional neural networks concepts to hybrid nn-hmm model for speech recognition. In Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on, pages 4277-4280. IEEE, 2012.
- U. Bagci and L. Bai. A comparison of daubechies and gabor wavelets for classification of mr images. In Signal Processing and Communi- cations, 2007. ICSPC 2007. IEEE International Conference on, pages 676-679. IEEE, 2007.
- L. Bao and S. S. Intille. Activity recognition from user-annotated acceleration data. In Pervasive computing, pages 1-17. Springer, 2004.
- Y. Bengio. Learning deep architectures for ai. Foundations and trends R in Machine Learning, 2(1):1-127, 2009.
- S. Bhattacharya, P. Nurmi, N. Hammerla, and T. Plötz. Using unlabeled data in a sparse-coding framework for human activity recognition. arXiv preprint arXiv:1312.6995, 2013. · arXiv
- S. Chennuru, P.-W. Chen, J. Zhu, and J. Y. Zhang. Mobile lifelogger- recording, indexing, and understanding a mobile users life. In Mobile Computing, Applications, and Services, pages 263-281. Springer, 2012.
- D. Figo, P. C. Diniz, D. R. Ferreira, and J. M. Cardoso. Preprocessing techniques for context recognition from accelerometer data. Personal and Ubiquitous Computing, 14(7):645-662, 2010.
- K. Forster, D. Roggen, and G. Troster. Unsupervised classifier self- calibration through repeated context occurences: is there robustness against sensor displacement to gain? In Wearable Computers, 2009. ISWC'09. International Symposium on, pages 77-84. IEEE, 2009.
- J. Frank, S. Mannor, and D. Precup. Activity and gait recognition with time-delay embeddings. In AAAI, 2010.
- Z. He and L. Jin. Activity recognition from acceleration data based on discrete consine transform and svm. In Systems, Man and Cybernetics. SMC 2009. IEEE International Conference on, pages 5041-5044. IEEE, 2009.
- Z.-Y. He and L.-W. Jin. Activity recognition from acceleration data using ar model representation and svm. In Machine Learning and Cybernetics, 2008 International Conference on, volume 4, pages 2245- 2250. IEEE, 2008.
- G. Hinton, S. Osindero, and Y.-W. Teh. A fast learning algorithm for deep belief nets. Neural computation, 18(7):1527-1554, 2006.
- G. E. Hinton and R. R. Salakhutdinov. Reducing the dimensionality of data with neural networks. Science, 313(5786):504-507, 2006.
- T. Huynh and B. Schiele. Analyzing features for activity recognition. In Proceedings of the 2005 joint conference on Smart objects and ambient intelligence: innovative context-aware services: usages and technologies, pages 159-163. ACM, 2005.
- S. Ji, W. Xu, M. Yang, and K. Yu. 3d convolutional neural networks for human action recognition. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 35(1):221-231, 2013.
- A. Krause, J. Farringdon, D. P. Siewiorek, and A. Smailagic. Unsu- pervised, dynamic identification of physiological and activity context in wearable computing. In 2012 16th International Symposium on Wearable Computers, pages 88-88. IEEE Computer Society, 2003.
- Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, 1998.
- J. W. Lockhart, G. M. Weiss, J. C. Xue, S. T. Gallagher, A. B. Grosner, and T. T. Pulickal. Design considerations for the wisdm smart phone-based sensor mining architecture. In Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data, pages 25-33. ACM, 2011.
- T. Plötz, N. Y. Hammerla, and P. Olivier. Feature learning for activity recognition in ubiquitous computing. In Proceedings of the Twenty- Second IJCAI Volume Two, pages 1729-1734. AAAI Press, 2011.
- B. T. Polyak. Some methods of speeding up the convergence of iteration methods. USSR Computational Mathematics and Mathematical Physics, 4(5):1-17, 1964.
- I. Sutskever, J. Martens, G. Dahl, and G. Hinton. On the importance of initialization and momentum in deep learning. In Proceedings of the 30th International Conference on Machine Learning (ICML-13), pages 1139-1147, 2013.
- T. Tamura, M. Sekine, M. Ogawa, T. Togawa, and Y. Fukui. Classifi- cation of acceleration waveforms during walking by wavelet transform. Methods of information in medicine, 36(4-5):356-359, 1997.
- Y. Tang, R. Salakhutdinov, and G. Hinton. Robust boltzmann machines for recognition and denoising. In Computer Vision and Pattern Recog- nition (CVPR), 2012 IEEE Conference on, pages 2264-2271. IEEE, 2012.
- C. Vollmer, H.-M. Gross, and J. P. Eggert. Learning features for activity recognition with shift-invariant sparse coding. In Artificial Neural Networks and Machine Learning-ICANN 2013, pages 367-374. Springer, 2013.
- S. Wang and C. Manning. Fast dropout training. In Proceedings of the 30th International Conference on Machine Learning (ICML-13), pages 118-126, 2013.
- P. Wu, H.-K. Peng, J. Zhu, and Y. Zhang. Senscare: Semi-automatic activity summarization system for elderly care. In Mobile Computing, Applications, and Services, pages 1-19. Springer, 2012.
- P. Wu, J. Zhu, and J. Y. Zhang. Mobisens: A versatile mobile sensing platform for real-world applications. Mobile Networks and Applications, 18(1):60-80, 2013.
- P. Zappi, C. Lombriser, T. Stiefmeier, E. Farella, D. Roggen, L. Benini, and G. Tröster. Activity recognition from on-body sensors: accuracy- power trade-off by dynamic sensor selection. In Wireless Sensor Networks, pages 17-33. Springer, 2008.
- M. Zhang and A. A. Sawchuk. A feature selection-based framework for human activity recognition using wearable multimodal sensors. In Proceedings of the 6th International Conference on Body Area Networks, pages 92-98. ICST, 2011.
Cited by
Sorted by citations
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 · see citing works
“, a sensor signal), often a 1D kernel is used in a temporal convolution [23].”
MethodResultInfluentialDeep Learning for Health Informatics
Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., & Yang, G.-Z. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2636665 · Cited by 2,000 · see citing works
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,…”
BackgroundMethodInfluentialHuman 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 · see citing works
Nweke, H. F., Teh, Y. W., Al-garadi, M. A., & Alo, U. R. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.03.056 · Cited by 930 · see citing works
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
“Therefore, the use of wearables in fitness and lifestyle has the potential to significantly advance one of the most prolific aspects of HAR applications [68, 136, 138, 164, 169, 233, 293, 295, 318].”
BackgroundTowards Environment Independent Device Free Human Activity Recognition
Jiang, W., Miao, C., Ma, F., Yao, S., Wang, Y., Yuan, Y., Xue, H., Song, C., Ma, X., Koutsonikolas, D., et al. (2018) · Proceedings of the 24th Annual International Conference on Mobile Computing and Networking · doi:10.1145/3241539.3241548 · Cited by 573 · see citing works
Deep Recurrent Neural Networks for Human Activity Recognition
Murad, A., & Pyun, J.-Y. (2017) · Sensors · doi:10.3390/s17112556 · Cited by 524 · see citing works
Ensembles of Deep LSTM Learners for Activity Recognition using Wearables
Guan, Y., & Plötz, T. (2017) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3090076 · Cited by 511 · see citing works
Deep 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
“el" in CNNs, so we use the term "axis" instead in this paper. A commonly used AF strategy is to design a specific neural network for each univariate time series of each sensing channel [176, 191]. The information representations from all channels are concatenated at last for input into a final classific…”
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 deep learning approach to on-node sensor data analytics for mobile or wearable devices
Ravi, D., Wong, C., Lo, B., & Yang, G.-Z. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2633287 · Cited by 439 · 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
Demrozi, F., Pravadelli, G., Bihorac, A., & Rashidi, P. (2020) · IEEE Access · doi:10.1109/access.2020.3037715 · Cited by 368 · see citing works
A multibranch CNN-BiLSTM model for human activity recognition using wearable sensor data
Challa, S. K., Kumar, A., & Semwal, V. B. (2022) · The Visual Computer · doi:10.1007/s00371-021-02283-3 · Cited by 338 · see citing works
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 · 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
“el" in CNNs, so we use the term "axis" instead in this paper. A commonly used AF strategy is to design a specific neural network for each univariate time series of each sensing channel [176, 191]. The information representations from all channels are concatenated at last for input into a final classific…”
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
Deep Learning-Based Gait Recognition Using Smartphones in the Wild
Zou, Q., Wang, Y., Wang, Q., Zhao, Y., & Li, Q. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2020.2985628 · Cited by 305 · see citing works
Can Deep Learning Revolutionize Mobile Sensing?
Lane, N. D., & Georgiev, P. (2015) · Proceedings of the 16th International Workshop on Mobile Computing Systems and Applications · doi:10.1145/2699343.2699349 · Cited by 282 · see citing works
Blockchain-based Personal Health Data Sharing System Using Cloud Storage
Zheng, X., Mukkamala, R. R., Vatrapu, R., & Ordieres-Mere, J. (2018) · 2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) · doi:10.1109/healthcom.2018.8531125 · Cited by 281 · see citing works
Ravi, D., Wong, C., Lo, B., & Yang, G.-Z. (2016) · 2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN) · doi:10.1109/bsn.2016.7516235 · Cited by 269 · see citing works
Multiscale Deep Feature Learning for Human Activity Recognition Using Wearable Sensors
Tang, Y., Zhang, L., Min, F., & He, J. (2023) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2022.3161812 · Cited by 224 · see citing works
DanHAR: Dual Attention Network for multimodal human activity recognition using wearable sensors
Gao, W., Zhang, L., Teng, Q., He, J., & Wu, H. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107728 · Cited by 214 · 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
“The 1D convolutional neural netowrks (CNN) employ more effective signal processing units, such as convolution (capturing local dependency), pooling (capturing time invariant features), and it also makes use of the available label information in feature extraction [28].”
MethodSelf-citationDeep 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 · see citing works
On attention models for human activity recognition
Murahari, V. S., & Plötz, T. (2018) · Proceedings of the 2018 ACM International Symposium on Wearable Computers · doi:10.1145/3267242.3267287 · Cited by 157 · see citing works
Contrastive Predictive Coding for Human Activity Recognition
Haresamudram, H., Essa, I., & Plötz, T. (2021) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3463506 · Cited by 150 · see citing works
“Convolutional networks have been utilized to classify multi-variate sensor data [29, 79, 81].”
MethodMorales, F. J. O., & Roggen, D. (2016) · Proceedings of the 2016 ACM International Symposium on Wearable Computers · doi:10.1145/2971763.2971764 · Cited by 136 · see citing works
Peng (2018) · doi:10.1145/3214277 · Cited by 134 · see citing works
Shallow Convolutional Neural Networks for Human Activity Recognition Using Wearable Sensors
Huang, W., Zhang, L., Gao, W., Min, F., & He, J. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3091990 · Cited by 129 · see citing works
A CNN-SVM combined model for pattern recognition of knee motion using mechanomyography signals
Wu, H., Huang, Q., Wang, D., & Gao, L. (2018) · Journal of Electromyography and Kinesiology · doi:10.1016/j.jelekin.2018.07.005 · Cited by 115 · see citing works
Deep Learning for Human Activity Recognition in Mobile Computing
Plotz, T., & Guan, Y. (2018) · Computer · doi:10.1109/mc.2018.2381112 · Cited by 99 · see citing works
Towards occupant activity driven smart buildings via WiFi-enabled IoT devices and deep learning
Zou, H., Zhou, Y., Yang, J., & Spanos, C. J. (2018) · Energy and Buildings · doi:10.1016/j.enbuild.2018.08.010 · Cited by 100 · see citing works
On the role of features in human activity recognition
Haresamudram, H., Anderson, D. V., & Plötz, T. (2019) · Proceedings of the 23rd International Symposium on Wearable Computers · doi:10.1145/3341163.3347727 · Cited by 90 · see citing works
Semi-supervised convolutional neural networks for human activity recognition
Zeng, M., Yu, T., Wang, X., Nguyen, L. T., Mengshoel, O. J., & Lane, I. (2017) · 2017 IEEE International Conference on Big Data (Big Data) · doi:10.1109/bigdata.2017.8257967 · Cited by 85 · see citing works
Self-citationHuynh-The, T., Hua, C.-H., Tu, N. A., & Kim, D.-S. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3013272 · Cited by 72 · see citing works
Mathur, A., Zhang, T., Bhattacharya, S., Velickovic, P., Joffe, L., Lane, N. D., Kawsar, F., & Lio, P. (2018) · 2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) · doi:10.1109/ipsn.2018.00048 · Cited by 70 · see citing works
Gao, Y., Long, Y., Guan, Y., Basu, A., Baggaley, J., & Ploetz, T. (2019) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3314399 · Cited by 49 · see citing works
Qin, Y., Luo, H., Zhao, F., Wang, C., Wang, J., & Zhang, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2944686 · Cited by 46 · 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
Rubio-Solis, A., Panoutsos, G., Beltran-Perez, C., & Martinez-Hernandez, U. (2020) · Neurocomputing · doi:10.1016/j.neucom.2019.11.105 · Cited by 39 · see citing works
Synthetic Sensor Data for Human Activity Recognition
Alharbi, F., Ouarbya, L., & Ward, J. A. (2020) · 2020 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9206624 · Cited by 40 · see citing works
Sensor-data augmentation for human activity recognition with time-warping and data masking
Jeong, C. Y., Shin, H. C., & Kim, M. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-10600-0 · Cited by 38 · see citing works
Haresamudram, H., Tang, C. I., Suh, S., Lukowicz, P., & Plötz, T. (2025) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3729467 · Cited by 33 · see citing works
“The wearables community has therefore moved on from studying deeper supervised neural networks, towards opportunistically leveraging available and more easily collected unlabeled data for activity recognition.”
A strain gauge based locomotion mode recognition method using convolutional neural network
Feng, Y., Chen, W., & Wang, Q. (2019) · Advanced Robotics · doi:10.1080/01691864.2018.1563500 · Cited by 28 · see citing works
Sensor Data Required for Automatic Recognition of Athletic Tasks Using Deep Neural Networks
Clouthier, A. L., Ross, G. B., & Graham, R. B. (2020) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2019.00473 · Cited by 29 · 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
Siamese Networks for Weakly Supervised Human Activity Recognition
Sheng, T., & Huber, M. (2019) · 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) · doi:10.1109/smc.2019.8914045 · Cited by 21 · see citing works
If only we had more data!: Sensor-Based Human Activity Recognition in Challenging Scenarios
Plötz, T. (2023) · 2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) · doi:10.1109/percomworkshops56833.2023.10150267 · Cited by 16 · see citing works
Jangir, M. K., & Singh, K. (2019) · Journal of Discrete Mathematical Sciences and Cryptography · doi:10.1080/09720529.2019.1696552 · Cited by 11 · see citing works
Deep Convolutional Bidirectional LSTM for Complex Activity Recognition with Missing Data
Saha, S. S., Sandha, S. S., & Srivastava, M. (2021) · Smart Innovation, Systems and Technologies · doi:10.1007/978-981-15-8269-1_4 · Cited by 12 · see citing works
Deriving Effective Human Activity Recognition Systems through Objective Task Complexity Assessment
Hiremath, S. K., & Plötz, T. (2020) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3432227 · Cited by 11 · see citing works
Akbari, A., & Jafari, R. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2020.3023109 · Cited by 8 · see citing works
Sheng, T., & Huber, M. (2025) · Sensors · doi:10.3390/s25134032 · Cited by 5 · see citing works
Haresamudram, H., Tang, C. I., Suh, S., Lukowicz, P., & Ploetz, T. (2024) · arXiv · Cited by 31 · see citing works
“The wearables community has therefore moved on from studying deeper supervised neural networks, towards opportunistically leveraging available and more easily collected unlabeled data for activity recognition.”
- Authors
- Zeng, M., Nguyen, L. T., Yu, B., Mengshoel, O. J., Zhu, J., Wu, P., & Zhang, J.
- Affiliations
- Carnegie Mellon University (US)
- Corresponding author
- Zeng, Ming
- Venue
- Proceedings of the 6th International Conference on Mobile Computing, Applications and Services
- Publisher
- ICST
- Published
- 2014
- OpenAlex
- W2002261403
- Cited by
- 876
- References
- 5
- Citations by year
- 2026: 27 · 2025: 65 · 2024: 75 · 2023: 88 · 2022: 84 · 2021: 115 · 2020: 138 · 2019: 113 · 2018: 95 · 2017: 43 · 2016: 27 · 2015: 6
- FWCI
- 13.62
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Mobile Computing, Applications, and Services (publishedVersion)
- Funders
- National Science Foundation (1344768)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Keywords
- human activity recognition, convolutional neural networks, mobile sensors, smartphones, lifelogging, fitness tracking, health monitoring, mobile sensing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
