Human activity recognition with smartphone sensors using deep learning neural networks

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

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works

Improving neural networks by preventing co-adaptation of feature detectors

Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012) · arXiv · doi:10.48550/arxiv.1207.0580 · Cited by 6,382 · see citing works

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 · see citing works

A Survey on Human Activity Recognition using Wearable Sensors

Lara, O. D., & Labrador, M. A. (2013) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2012.110112.00192 · Cited by 2,662 · see citing works

Practical 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

Untitled work

(2013) · RECERCAT (Consorci de Serveis Universitaris de Catalunya) · Cited by 1,959 · see citing works

Sensor-Based Activity Recognition

Chen, L., Hoey, J., Nugent, C. D., Cook, D. J., & Yu, Z. (2012) · IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) · doi:10.1109/tsmcc.2012.2198883 · Cited by 1,074 · see citing works

Theano: A CPU and GPU Math Compiler in Python

Bergstra, J., Breuleux, O., Bastien, F., Lamblin, P., Pascanu, R., Desjardins, G., Turian, J. P., Warde-Farley, D., & Bengio, Y. (2010) · Proceedings of the Python in Science Conferences · doi:10.25080/majora-92bf1922-003 · Cited by 1,063 · see citing works

Human Activity Recognition on Smartphones Using a Multiclass Hardware-Friendly Support Vector Machine

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 · see citing works

Untitled work

(2015) · Cited by 989 · 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

ImageNet classification with deep convolutional neural networks

Krizhevsky (2012) · Medical Entomology and Zoology · Cited by 799 · see citing works

Time Series Classification Using Multi-Channels Deep Convolutional Neural Networks

Zheng, Y., Liu, Q., Chen, E., Ge, Y., & Zhao, J. L. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-08010-9_33 · Cited by 689 · see citing works

Fusion of Smartphone Motion Sensors for Physical Activity Recognition

Shoaib, M., Bosch, S., Incel, O., Scholten, H., & Havinga, P. (2014) · Sensors · doi:10.3390/s140610146 · Cited by 552 · see citing works

Detection of posture and motion by accelerometry: a validation study in ambulatory monitoring

Foerster, F., Smeja, M., & Fahrenberg, J. (1999) · Computers in Human Behavior · doi:10.1016/s0747-5632(99)00037-0 · Cited by 487 · 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

The handbook of brain theory and neural networks

Cios, K. J., & Shields, M. E. (1997) · Neurocomputing · doi:10.1016/s0925-2312(97)00036-2 · Cited by 308 · see citing works

Practical recommendations for gradient-based training of deep architecturesPreprint

Bengio, Y. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1206.5533 · Cited by 266 · see citing works

Convolutional Neural Networks for Distant Speech Recognition

Świętojański, P., Ghoshal, A. K., & Renals, S. J. (2014) · IEEE Signal Processing Letters · doi:10.1109/lsp.2014.2325781 · Cited by 253 · see citing works

Pylearn2: a machine learning research libraryPreprint

Goodfellow, I., Warde-Farley, D., Lamblin, P., Dumoulin, V., Mirza, M., Pascanu, R., Bergstra, J., Bastien, F., & Bengio, Y. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1308.4214 · Cited by 245 · see citing works

Activity Recognition Using Hierarchical Hidden Markov Models on a Smartphone with 3D Accelerometer

Lee, Y.-S., & Cho, S. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-21219-2_58 · Cited by 228 · see citing works

Using unlabeled data in a sparse-coding framework for human activity recognition

Bhattacharya, S., Nurmi, P., Hammerla, N., & Plötz, T. (2014) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2014.05.006 · Cited by 116 · see citing works

Efficient duration and hierarchical modeling for human activity recognition

Duong, T., Phung, D., Bui, H., & Venkatesh, S. (2009) · Artificial Intelligence · doi:10.1016/j.artint.2008.12.005 · Cited by 108 · see citing works

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 · see citing works

3D gesture classification with convolutional neural networks

Duffner, S., Berlemont, S., Lefebvre, G., & Garcia, C. (2014) · IEEE International Conference on Acoustics, Speech, and Signal Processing · doi:10.1109/icassp.2014.6854641 · Cited by 72 · see citing works

Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI)

Chandler, J., & Richard, B. P. (2016) · Cited by 61 · 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

Recognizing Physical Activities Using Wii Remote

Khan, A. (2013) · International Journal of Information and Education Technology · doi:10.7763/ijiet.2013.v3.234 · Cited by 29 · see citing works

Deep learning for computer vision: A brief review

Voulodimos, A., Doulamis, N., Doulamis, A., & Protopapadakis, E. (2018) · Computational Intelligence and Neuroscience · doi:10.1155/2018/7068349 · Cited by 3,403 · 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

Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges

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

LSTM-CNN Architecture for Human Activity Recognition

Xia, K., Huang, J., & Wang, H. (2020) · IEEE Access · doi:10.1109/access.2020.2982225 · Cited by 851 · 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

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

IoT Wearable Sensor and Deep Learning: An Integrated Approach for Personalized Human Activity Recognition in a Smart Home Environment

Bianchi, 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

Human Activity Recognition With Smartphone and Wearable Sensors Using Deep Learning Techniques: A Review

Ramanujam, E., Perumal, T., & Padmavathi, S. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3069927 · Cited by 455 · see citing works

Multi-input CNN-GRU based human activity recognition using wearable sensors

Dua, N., Singh, S. N., & Semwal, V. B. (2021) · Computing · doi:10.1007/s00607-021-00928-8 · Cited by 432 · 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

Human Activity Recognition Using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey

Demrozi, F., Pravadelli, G., Bihorac, A., & Rashidi, P. (2020) · IEEE Access · doi:10.1109/access.2020.3037715 · Cited by 368 · 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

Deep learning analysis of mobile physiological, environmental and location sensor data for emotion detection

Kanjo, 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 CNN-LSTM With Self-Attention Model for Human Activity Recognition Using Wearable Sensor

Khatun, M. A., Yousuf, M. A., Ahmed, S., Uddin, M. Z., Alyami, S. A., Al-Ashhab, S., Akhdar, H. F., Khan, A., Azad, A., & Moni, M. A. (2022) · IEEE Journal of Translational Engineering in Health and Medicine · doi:10.1109/jtehm.2022.3177710 · Cited by 288 · 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

Feature Representation and Data Augmentation for Human Activity Classification Based on Wearable IMU Sensor Data Using a Deep LSTM Neural Network

Steven Eyobu, O., & Han, D. S. (2018) · Sensors · doi:10.3390/s18092892 · Cited by 259 · see citing works

Applications of Artificial Intelligence and Big Data Analytics in m-Health: A Healthcare System Perspective

khan, Z. F., & Alotaibi, S. R. (2020) · Journal of Healthcare Engineering · doi:10.1155/2020/8894694 · Cited by 235 · see citing works

Distilling 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

Cross-subject transfer learning in human activity recognition systems using generative adversarial networks

Soleimani, E., & Nazerfard, E. (2021) · Neurocomputing · doi:10.1016/j.neucom.2020.10.056 · Cited by 146 · 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

Human activity recognition using wearable sensors by heterogeneous convolutional neural networks

Han, C., Zhang, L., Tang, Y., Huang, W., Min, F., & He, J. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.116764 · Cited by 130 · see citing works

Feature extraction for robust physical activity recognition

Zhu, J., San-Segundo, R., & Pardo, J. M. (2017) · Human-centric Computing and Information Sciences · doi:10.1186/s13673-017-0097-2 · Cited by 68 · see citing works

Race Recognition Using Deep Convolutional Neural Networks

Vo, T., Nguyen, T., & Le, C. T. (2018) · Symmetry · doi:10.3390/sym10110564 · Cited by 59 · see citing works

Learning hierarchical time series data augmentation invariances via contrastive supervision for human activity recognition

Cheng, D., Zhang, L., Bu, C., Wu, H., & Song, A. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110789 · Cited by 55 · see citing works

An optimized feature selection using bio-geography optimization technique for human walking activities recognition

Semwal, V. B., Lalwani, P., Mishra, M. K., Bijalwan, V., & Chadha, J. S. (2021) · Computing · doi:10.1007/s00607-021-01008-7 · Cited by 55 · see citing works

HiHAR: A Hierarchical Hybrid Deep Learning Architecture for Wearable Sensor-Based Human Activity Recognition

Hoai Thu, N. T., & Han, D. S. (2021) · IEEE Access · doi:10.1109/access.2021.3122298 · Cited by 54 · 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

Remote Health Monitoring Systems for Elderly People: A Survey

Ahmed, S., Irfan, S., Kiran, N., Masood, N., Anjum, N., & Ramzan, N. (2023) · Sensors · doi:10.3390/s23167095 · Cited by 52 · see citing works

A multilayer interval type-2 fuzzy extreme learning machine for the recognition of walking activities and gait events using wearable sensors

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

Harmonic Loss Function for Sensor-Based Human Activity Recognition Based on LSTM Recurrent Neural Networks

Hu, Y., Zhang, X.-Q., Xu, L., Xian He, F., Tian, Z., She, W., & Liu, W. (2020) · IEEE Access · doi:10.1109/access.2020.3003162 · Cited by 36 · see citing works

Deep learning of smartphone sensor data for personal health assistance

Li, H., & Trocan, M. (2019) · Microelectronics Journal · doi:10.1016/j.mejo.2018.01.015 · Cited by 30 · see citing works

MaskCAE: Masked Convolutional AutoEncoder via Sensor Data Reconstruction for Self-Supervised Human Activity Recognition

Cheng, D., Zhang, L., Qin, L., Wang, S., Wu, H., & Song, A. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3373019 · Cited by 31 · see citing works

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

Recognition of Basic Activities of Daily Living Using Wearable Devices for Older Adults: Scoping Review (Preprint)

Ray, S. J., Cherian, J., Liberty, A. M., Hammond, T. A., & Shireman, P. K. (2024) · doi:10.2196/preprints.67373 · Cited by 18 · see citing works

The benefit of the doubt: Uncertainty aware sensing for edge computing platforms

Qendro, L., Chauhan, J., Ramos, A. G. C. P., & Mascolo, C. (2021) · arXiv · doi:10.48550/arxiv.2102.05956 · Cited by 6 · see citing works

A Recurrent Neural Network Optimization Method for Anticipation of Hierarchical Human Activity

Khodabandelou, G., Chibani, A., Amirat, Y., & Tanimoto, S. (2024) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2023.3300821 · Cited by 4 · see citing works

A Fully Trainable Network with RNN-based PoolingPreprint

Li, S., Li, W., Cook, C., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.05157 · Cited by 1 · see citing works

A fully trainable network with RNN-based pooling

Li, S., Li, W., Cook, C., Zhu, C., & Gao, Y. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.02.004 · Cited by 24 · see citing works

Alpha-Pooling for Convolutional Neural Networks.Preprint

Eom, H., & Choi, H. (2018) · arXiv (Cornell University) · Cited by 2 · see citing works

Pooling in high-throughput drug screening.

Kainkaryam, R. M., & WOOLF, P. J. (2009) · PubMed · Cited by 69 · see citing works

Why and when should you pool? Analyzing Pooling in Recurrent\n ArchitecturesPreprint

Maini, P., Kolluru, K., Pruthi, D., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.00159