A multibranch CNN-BiLSTM model for human activity recognition using wearable sensor data
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
Challa et al.'s hybrid achieved accuracy of 96.37%, 96.05%, and 94.29% on UCI-HAR, WISDM, and PAMAP2 datasets, respectively.
“The accuracy on UCI-HAR, WISDM [81], and PAMAP2 [91] datasets achieved 96.37%, 96.05%, and 94.29%, respectively.”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [247]
Challa et al. proposed a hybrid of CNN and bidirectional LSTM.
“For example, Challa et al. [247] proposed a hybrid of CNN and bidirectional long short-term memory (BiLSTM).”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [247]
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftPreprint
Ioffe, S. (2024) · arXiv (Cornell University) · doi:10.57702/o9raffed · Cited by 15,273 · see citing works
Bidirectional recurrent neural networks
Schuster, M., & Paliwal, K. K. (1997) · IEEE Transactions on Signal Processing · doi:10.1109/78.650093 · Cited by 10,172 · see citing works
Learning long-term dependencies with gradient descent is difficult
Bengio, Y., Simard, P., & Frasconi, P. (1994) · IEEE Transactions on Neural Networks · doi:10.1109/72.279181 · Cited by 8,542 · 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 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
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
(2013) · RECERCAT (Consorci de Serveis Universitaris de Catalunya) · Cited by 1,959 · see citing works
Introducing a New Benchmarked Dataset for Activity Monitoring
Reiss, A., & Stricker, D. (2012) · 2012 16th International Symposium on Wearable Computers · doi:10.1109/iswc.2012.13 · Cited by 1,456 · see citing works
LSTM Fully Convolutional Networks for Time Series Classification
Karim, F., Majumdar, S., Darabi, H., & Chen, S. (2017) · IEEE Access · doi:10.1109/access.2017.2779939 · Cited by 1,358 · see citing works
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
(2015) · Cited by 989 · 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
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
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
Real-time human activity recognition from accelerometer data using Convolutional Neural Networks
Ignatov, A. (2017) · Applied Soft Computing · doi:10.1016/j.asoc.2017.09.027 · Cited by 817 · see citing works
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 · 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
A CNN-LSTM Approach to Human Activity Recognition
Mutegeki, R., & Han, D. S. (2020) · 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) · doi:10.1109/icaiic48513.2020.9065078 · Cited by 452 · see citing works
Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensors
Zhao, Y., Yang, R., Chevalier, G., Xu, X., & Zhang, Z. (2018) · Mathematical Problems in Engineering · doi:10.1155/2018/7316954 · Cited by 347 · see citing works
UAV-Human: A Large Benchmark for Human Behavior Understanding with Unmanned Aerial Vehicles
Li, T., Liu, J., Zhang, W., Ni, Y., Wang, W., & Li, Z. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01600 · Cited by 298 · see citing works
On the use of ensemble of classifiers for accelerometer-based activity recognition
Catal, C., Tufekci, S., Pirmit, E., & Kocabag, G. (2015) · Applied Soft Computing · doi:10.1016/j.asoc.2015.01.025 · Cited by 240 · see citing works
Wang, K., He, J., & Zhang, L. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2019.2917225 · Cited by 205 · see citing works
Teng, Q., Wang, K., Zhang, L., & He, J. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.2978772 · Cited by 183 · see citing works
Stacked Lstm Network for Human Activity Recognition Using Smartphone Data
Ullah, M., Ullah, H., Khan, S. D., & Cheikh, F. A. (2019) · European Workshop on Visual Information Processing · doi:10.1109/euvip47703.2019.8946180 · Cited by 143 · see citing works
Human Activity Recognition using LSTM-RNN Deep Neural Network Architecture
Pienaar, S. W., & Malekian, R. (2019) · doi:10.1109/africa.2019.8843403 · Cited by 139 · see citing works
CNN based approach for activity recognition using a wrist-worn accelerometer
Panwar, M., Sristi, R. D., Konkimalla, C. P., Biswas, D., Acharyya, A., Maharatna, K., Gautam, A., & Naik, G. R. (2017) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2017.8037349 · Cited by 133 · see citing works
Semwal, V. B., Mondal, K., & Nandi, G. C. (2015) · Neural Computing and Applications · doi:10.1007/s00521-015-2089-3 · Cited by 132 · see citing works
Self-citationAbnormality Detection in Mammography using Deep Convolutional Neural Networks
Xi, P., Shu, C., & Goubran, R. A. (2018) · IEEE International Symposium on Medical Measurements and Applications (MeMeA) · doi:10.1109/memea.2018.8438639 · Cited by 124 · see citing works
A Random Forest-based ensemble method for activity recognition
Feng, Z., Mo, L., & Li, M. (2015) · doi:10.1109/embc.2015.7319532 · Cited by 112 · see citing works
Chen, Z., Jiang, C., Xiang, S., Ding, J., Wu, M., & Li, X. (2019) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2019.2945467 · Cited by 105 · see citing works
RCNet: road classification convolutional neural networks for intelligent vehicle system
Dewangan, D., & Sahu, S. (2021) · Intelligent Service Robotics · doi:10.1007/s11370-020-00343-6 · Cited by 98 · see citing works
Deep Learning-Based Speed Bump Detection Model for Intelligent Vehicle System Using Raspberry Pi
Dewangan, D. K., & Sahu, S. P. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2020.3027097 · Cited by 96 · see citing works
Ignatov, A., & Strijov, V. (2015) · Multimedia Tools and Applications · doi:10.1007/s11042-015-2643-0 · Cited by 88 · see citing works
Human Activity Recognition on Smartphones Using a Bidirectional LSTM Network
Castrillón, F., Suárez, L. F. D., Villamizar, J., & Altuve, M. (2019) · Symposium on Image, Signal Processing and Artificial Vision (STSIVA) · doi:10.1109/stsiva.2019.8730249 · Cited by 87 · see citing works
Cheng, X., Zhang, L., Tang, Y., Liu, Y., Wu, H., & He, J. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3149337 · Cited by 84 · see citing works
PotNet: Pothole detection for autonomous vehicle system using convolutional neural network
Dewangan, D. K., & Sahu, S. P. (2020) · Electronics Letters · doi:10.1049/ell2.12062 · Cited by 79 · see citing works
XKin: an open source framework for hand pose and gesture recognition using kinect
Pedersoli, F., Benini, S., Adami, N., & Leonardi, R. (2014) · The Visual Computer · doi:10.1007/s00371-014-0921-x · Cited by 72 · see citing works
Vishwakarma, D. K., & Dhiman, C. (2018) · The Visual Computer · doi:10.1007/s00371-018-1560-4 · Cited by 64 · see citing works
WITS: an IoT-endowed computational framework for activity recognition in personalized smart homes
Yao, L., Sheng, Q. Z., Benatallah, B., Dustdar, S., Wang, X., Shemshadi, A., & Kanhere, S. S. (2018) · Computing · doi:10.1007/s00607-018-0603-z · Cited by 63 · see citing works
Human Activity Recognition Using Temporal Convolutional Network
Nair, N., Thomas, C., & Jayagopi, D. B. (2018) · International Workshop on Sensor-based Activity Recognition and Interaction (iWOAR) · doi:10.1145/3266157.3266221 · Cited by 58 · see citing works
Two-stream spatiotemporal feature fusion for human action recognition
Abdelbaky, A., & Aly, S. (2020) · The Visual Computer · doi:10.1007/s00371-020-01940-3 · Cited by 57 · see citing works
Multiple Task Human Gait Analysis and Identification: Ensemble Learning Approach
Gupta, A., & Semwal, V. B. (2020) · Springer eBooks · doi:10.1007/978-3-030-48849-9_12 · Cited by 54 · see citing works
Self-citationXi, P., Guan, H., Shu, C., Borgeat, L., & Goubran, R. (2020) · The Visual Computer · doi:10.1007/s00371-019-01775-7 · Cited by 47 · see citing works
Dewangan, D. K., Sahu, S. P., Sairam, B., & Agrawal, A. (2021) · Computing · doi:10.1007/s00607-021-00974-2 · Cited by 41 · see citing works
Percentage of human-occupied areas for fall detection from two views
Mousse, M. A., Motamed, C., & Ezin, E. C. (2016) · The Visual Computer · doi:10.1007/s00371-016-1296-y · Cited by 31 · see citing works
Zhu, R., Tu, X., & Huang, J. X. (2019) · Studies in big data · doi:10.1007/978-3-030-33966-1_13 · Cited by 28 · see citing works
Multi-label image classification with recurrently learning semantic dependencies
Chen, L. F., Wang, R., Yang, J., Xue, L., & Hu, M. Q. (2018) · The Visual Computer · doi:10.1007/s00371-018-01615-0 · Cited by 24 · see citing works
Real-time multimodal ADL recognition using convolution neural networks
Madhuranga, D., Madushan, R., Siriwardane, C., & Gunasekera, K. (2020) · The Visual Computer · doi:10.1007/s00371-020-01864-y · Cited by 23 · see citing works
UAV-Human: A Large Benchmark for Human Behavior Understanding with Unmanned Aerial VehiclesPreprint
Li, T., Liu, J., Zhang, W., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.00946 · Cited by 18 · see citing works
ARC-Net: Activity Recognition Through Capsules
Damirchi, H., Khorrambakht, R., & Taghirad, H. D. (2020) · International Conference on Machine Learning and Applications · doi:10.1109/icmla51294.2020.00215 · Cited by 15 · see citing works
Human Action Recognition from Various Data Modalities: A ReviewPreprint
Rahmani, H., Bennamoun, M., & Ke, Q. (2021) · doi:10.36227/techrxiv.13708270.v1 · Cited by 14 · see citing works
Mishra, A., Sharma, S., Kumar, S., Ranjan, P., & Ujlayan, A. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-05125-w · Cited by 12 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
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
“[55] proposed a hybrid of CNN and bidirectional long short-term memory (BiLSTM).”
BackgroundDeep Learning for Time Series Classification and Extrinsic Regression: A Current Survey
Foumani, N. M., Miller, L., Tan, C. W., Webb, G. I., Forestier, G., & Salehi, M. (2024) · ACM Computing Surveys · doi:10.1145/3649448 · Cited by 296 · see citing works
Kumar, P., Chauhan, S., & Awasthi, L. K. (2023) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-023-09986-x · Cited by 220 · see citing works
Bhattacharya, D., Sharma, D., Kim, W., Ijaz, M. F., & Singh, P. K. (2022) · Biosensors · doi:10.3390/bios12060393 · Cited by 126 · see citing works
Body-Worn Sensors for Recognizing Physical Sports Activities in Exergaming via Deep Learning Model
Afsar, M. M., Saqib, S., Aladfaj, M., Alatiyyah, M. H., Alnowaiser, K., Aljuaid, H., Jalal, A., & Park, J. (2023) · IEEE Access · doi:10.1109/access.2023.3239692 · Cited by 111 · see citing works
Wei, X., & Wang, Z. (2024) · Scientific Reports · doi:10.1038/s41598-024-57912-3 · Cited by 109 · see citing works
Human activity recognition from sensor data using spatial attention-aided CNN with genetic algorithm
Sarkar, A., Hossain, S., & Sarkar, R. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07911-0 · Cited by 107 · see citing works
Yi, M.-K., Lee, W., & Hwang, S. O. (2023) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2023.3266506 · Cited by 102 · see citing works
Essa, E., & Abdelmaksoud, I. R. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110867 · Cited by 95 · see citing works
Miao, F., Huang, Y., Lu, Z., Ohtsuki, T. O., Gui, G., & Sari, H. (2024) · ACM Computing Surveys · doi:10.1145/3705893 · Cited by 82 · see citing works
Human Activity Recognition Using Attention-Mechanism-Based Deep Learning Feature Combination
Akter, M., Shafew, A., Khan, M. A.-M., & Kim, D. (2023) · Sensors · doi:10.3390/s23125715 · Cited by 84 · see citing works
Dong, W., Sheng, K., Huang, B., Xiong, K., Liu, K., & Cheng, X. L. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3464633 · Cited by 71 · see citing works
Choudhury, N. A., & Soni, B. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3229522 · Cited by 72 · see citing works
Novel Deep Learning Network for Gait Recognition Using Multimodal Inertial Sensors
Shi, L., Liu, Z.-Y., Zhou, K.-J., Shi, Y., & Xiao, J. (2023) · Sensors · doi:10.3390/s23020849 · Cited by 68 · see citing works
Rani, G. J., Hashmi, M. F., & Gupta, A. D. (2023) · IEEE Access · doi:10.1109/access.2023.3316509 · Cited by 64 · see citing works
Human activity recognition using a multi-branched CNN-BiLSTM-BiGRU model
Lalwani, P., & Ganeshan, R. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.111344 · Cited by 64 · see citing works
Transformer-based models to deal with heterogeneous environments in Human Activity Recognition
Ek, S., Portet, F., & Lalanda, P. (2023) · Personal and Ubiquitous Computing · doi:10.1007/s00779-023-01776-3 · Cited by 74 · see citing works
Efficient human activity recognition on edge devices using DeepConv LSTM architectures
Zhou, H., Zhang, X., Feng, Y., Zhang, T., & Xiong, L. (2025) · Scientific Reports · doi:10.1038/s41598-025-98571-2 · Cited by 59 · see citing works
Gaud, N., Rathore, M., & Suman, U. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3450499 · Cited by 58 · see citing works
DCapsNet: Deep capsule network for human activity and gait recognition with smartphone sensors
Sezavar, A., Atta, R., & Ghanbari, M. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.110054 · Cited by 54 · see citing works
Activity-Based Person Identification Using Multimodal Wearable Sensor Data
Luo, F., Khan, S., Huang, Y., & Wu, K. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3209084 · Cited by 50 · see citing works
Attention-Based Residual BiLSTM Networks for Human Activity Recognition
Zhang, J., Liu, Y., & Yuan, H. (2023) · IEEE Access · doi:10.1109/access.2023.3310269 · Cited by 51 · see citing works
An ensemble deep learning model for human activity analysis using wearable sensory data
Batool, S., Khan, M. H., & Farid, M. S. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.111599 · Cited by 52 · see citing works
Zhao, Y., Dong, F., Sun, T., Ju, Z., Yang, L., Shan, P., Li, L., Lv, X., & Lian, C. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121978 · Cited by 49 · see citing works
Li, S., Zhu, T., Duan, F., Chen, L., Ning, H., Nugent, C., & Wan, Y. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3463405 · Cited by 53 · see citing works
A Survey on Human Activity Recognition Using Deep Learning Techniques and Wearable Sensor Data
Dua, N., Singh, S. N., Challa, S. K., Semwal, V. B., & Kumar, M. L. S. S. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-24352-3_5 · Cited by 48 · see citing works
Self-citationA DCNN-LSTM based human activity recognition by mobile and wearable sensor networks
Jameer, S., & Syed, H. (2023) · Alexandria Engineering Journal · doi:10.1016/j.aej.2023.09.013 · Cited by 47 · see citing works
Qureshi, T. S., Shahid, M. H., Farhan, A. A., & Alamri, S. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11275-x · Cited by 48 · see citing works
Smart-Wearable Sensors and CNN-BiGRU Model: A Powerful Combination for Human Activity Recognition
Imran, H. A., Riaz, Q., Hussain, M., Tahir, H., & Arshad, R. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3338264 · Cited by 45 · see citing works
Wang, C., He, B., Wei, W., Yi, Z., Li, P., Duan, S., & Wu, X. (2022) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2022.3185706 · Cited by 43 · see citing works
An optimized deep learning model for human activity recognition using inertial measurement units
Challa, S. K., Kumar, A. A., Semwal, V. B., & Dua, N. (2023) · Expert Systems · doi:10.1111/exsy.13457 · Cited by 44 · see citing works
Self-citationHunger games search optimization with deep learning model for sustainable supply chain management
Xu, Z., Jain, D. K., Neelakandan, S., & Abawajy, J. (2023) · Discover Internet of Things · doi:10.1007/s43926-023-00040-7 · Cited by 42 · see citing works
A Novel Smartphone-Based Human Activity Recognition Using Deep Learning in Health care
Soni, V., Yadav, H., Semwal, V. B., Roy, B., Choubey, D. K., & Mallick, D. K. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-5868-7_36 · Cited by 43 · see citing works
Self-citationA Novel CNN-BiLSTM-GRU Hybrid Deep Learning Model for Human Activity Recognition
Lalwani, P., & Ganeshan, R. (2024) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-024-00689-0 · Cited by 39 · see citing works
Mudawi, N. A., Azmat, U., Alazeb, A., Alhasson, H. F., Alabdullah, B. I., Rahman, H., Liu, H., & Jalal, A. (2025) · Scientific Reports · doi:10.1038/s41598-025-94689-5 · Cited by 38 · see citing works
Hybrid Deep Learning Approaches for sEMG Signal-Based Lower Limb Activity Recognition
Vijayvargiya, A., Singh, B., Kumar, R., Desai, U., & Hemanth, D. J. (2022) · Mathematical Problems in Engineering · doi:10.1155/2022/3321810 · Cited by 36 · see citing works
Deep ensemble learning approach for lower limb movement recognition from multichannel sEMG signals
Tokas, P., Semwal, V. B., & Jain, S. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-09465-9 · Cited by 33 · see citing works
Self-citationSmartphone Inertial Measurement Unit Data Features for Analyzing Driver Driving Behavior
Kanwal, K., Rustam, F., Chaganti, R., Jurcut, A. D., & Ashraf, I. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3256000 · Cited by 33 · see citing works
A deep local-temporal architecture with attention for lightweight human activity recognition
Ige, A. O., & Noor, M. H. M. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110954 · Cited by 30 · see citing works
Human Activity Recognition Using Wearable Sensors Based on Image Classification
Zebhi, S. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3174280 · Cited by 31 · see citing works
Zou, H., & Wang, Z. (2023) · Journal Of Big Data · doi:10.1186/s40537-023-00710-x · Cited by 31 · see citing works
Senane, Z., Cao, L., Buchner, V., Tashiro, Y., You, L., Herman, P. A., Nordahl, M. G., Tu, R., & von Ehrenheim, V. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3671673 · Cited by 31 · see citing works
Zhongkai, Z., Kobayashi, S., Kondo, K., Hasegawa, T., & Koshino, M. (2022) · IEEE Access · doi:10.1109/access.2022.3152530 · Cited by 32 · see citing works
Deep learning models for real-life human activity recognition from smartphone sensor data
Garcia-Gonzalez, D., Rivero, D., Fernández-Blanco, E., & Luaces, M. R. (2023) · Internet of Things · doi:10.1016/j.iot.2023.100925 · Cited by 28 · see citing works
An Improved Deep Convolutional LSTM for Human Activity Recognition Using Wearable Sensors
Zhang, N., Song, Y., Fang, D., Gao, Z., & Yan, Y. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3335213 · Cited by 27 · see citing works
Detection of human activities using multi-layer convolutional neural network
Abdellatef, E., Al‐Makhlasawy, R. M., & Shalaby, W. A. (2025) · Scientific Reports · doi:10.1038/s41598-025-90307-6 · Cited by 28 · see citing works
Alikhanov, J., & Kim, H. (2023) · IEEE Access · doi:10.1109/access.2023.3292539 · Cited by 28 · see citing works
Open Datasets in Human Activity Recognition Research—Issues and Challenges: A Review
Alam, G., McChesney, I., Nicholl, P., & Rafferty, J. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3317645 · Cited by 26 · see citing works
Miah, A. S. M., Hwang, Y. S., & Shin, J. (2024) · IEEE Access · doi:10.1109/access.2024.3473828 · Cited by 26 · see citing works
Tracking of Fall Detection Using IMU Sensor: An IoHT Application
Semwal, V. B., Kumar, A., Nargesh, P., & Soni, V. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-5868-7_61 · Cited by 25 · see citing works
Self-citationHuman Activity Recognition Using Machine Learning Algorithms Based on IMU Data
Nia, N. G., Kaplanoğlu, E., Nasab, A., & Qin, H. H. (2023) · International Conference on Bio-engineering for Smart Technologies (BioSMART) · doi:10.1109/biosmart58455.2023.10162095 · Cited by 25 · see citing works
Han, C., Zhang, L., Tang, Y., Xu, S., Min, F., Wu, H., & Song, A. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3191653 · Cited by 24 · see citing works
Jaramillo, I. E., Chola, C., Jeong, J.-G., Oh, J.-H., Jung, H., Lee, J.-H., Lee, W. H., & Kim, T. (2023) · Sensors · doi:10.3390/s23146491 · Cited by 22 · see citing works
Leveraging spatio-temporal features using graph neural networks for human activity recognition
Raj, M. S. S., George, S. N., & Raja, K. B. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110301 · Cited by 24 · see citing works
Otago Exercises Monitoring for Older Adults by a Single IMU and Hierarchical Machine Learning Models
Shang, M., Dedeyne, L., Dupont, J., Vercauteren, L., Amini, N., Lapauw, L., Gielen, E., Verschueren, S., Varon, C., De Raedt, W., et al. (2024) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2024.3355299 · Cited by 21 · see citing works
Human activity recognition from UAV videos using a novel DMLC-CNN model
Sinha, K. P., & Kumar, P. (2023) · Image and Vision Computing · doi:10.1016/j.imavis.2023.104674 · Cited by 21 · see citing works
An eXplainable Self-Attention-Based Spatial–Temporal Analysis for Human Activity Recognition
Meena, T., & Sarawadekar, K. P. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3335449 · Cited by 22 · see citing works
Human Activity Recognition Based on Two-Channel Residual–GRU–ECA Module with Two Types of Sensors
Wang, X., & Shang, J. (2023) · Electronics · doi:10.3390/electronics12071622 · Cited by 21 · see citing works
Fatima, R., Khan, M. H., Nisar, M. A., Doniec, R., Farid, M. S., & Grzegorzek, M. (2023) · Sensors · doi:10.3390/s24010075 · Cited by 20 · see citing works
Stojchevska, M., Brouwer, M. D., Courteaux, M., Ongenae, F., & Hoecke, S. V. (2023) · Sensors · doi:10.3390/s23104606 · Cited by 23 · see citing works
A Hybrid Visual Transformer for Efficient Deep Human Activity Recognition
Djenouri, Y., & Belbachir, A. N. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00080 · Cited by 19 · see citing works
Nguyen, V. S., Kim, H.-S., & Suh, D. (2023) · IEEE Access · doi:10.1109/access.2023.3338137 · Cited by 17 · see citing works
Yuan, Q., He, M., Chen, Z., Liu, M., & Chen, X. (2025) · Ocean Engineering · doi:10.1016/j.oceaneng.2025.120820 · Cited by 18 · see citing works
Smart-Data-Glove-Based Gesture Recognition for Amphibious Communication
Fan, L., Zhang, Z., Zhu, B., Zuo, D., Yu, X., & Wang, Y. (2023) · Micromachines · doi:10.3390/mi14112050 · Cited by 17 · see citing works
DSANet: A lightweight hybrid network for human action recognition in virtual sports
Xiao, Z., Yu, F. Q., Liu, L., Peng, T., Hu, X., & Jiang, M. (2024) · Computer Animation and Virtual Worlds · doi:10.1002/cav.2274 · Cited by 18 · see citing works
Yuan, W., Xia, Q., Qian, H., Qiao, B., Xu, J., & Xiao, B. (2025) · Advances in Space Research · doi:10.1016/j.asr.2025.01.002 · Cited by 17 · see citing works
Mekruksavanich, S., & Jitpattanakul, A. (2025) · Applied System Innovation · doi:10.3390/asi8030057 · Cited by 16 · see citing works
Yan, Y., Huang, Y.-C., Zhao, J., Liu, Y.-S., Ma, L., Yang, J., Yan, X., Xiong, J., & Wang, L. (2023) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2023.3275774 · Cited by 16 · see citing works
Mekruksavanich, S., & Jitpattanakul, A. (2024) · Symmetry · doi:10.3390/sym16050554 · Cited by 18 · see citing works
Li, S., Zhu, T., Nie, M., Ning, H., Liu, Z., & Chen, L. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3493380 · Cited by 18 · see citing works
Shakerian, R., Yadollahzadeh‐Tabari, M., & Rad, S. Y. B. (2022) · IET Biometrics · doi:10.1049/bme2.12066 · Cited by 15 · see citing works
Deep Learning in the Recognition of Activities of Daily Living Using Smartwatch Data
Cavalcante, A. F., Kunst, V. H. D. L., Chaves, T., de Souza, J. D. T., Ribeiro, I. M., Quintino, J. P., da Silva, F. Q. B., Santos, A. L. M., Teichrieb, V., & Gama, A. E. F. D. (2023) · Sensors · doi:10.3390/s23177493 · Cited by 15 · see citing works
Depression prediction based on LassoNet-RNN model: A longitudinal study
Han, J., Li, H., Lin, H., Wu, P., Wang, S., Tu, J., & Lu, J. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e20684 · Cited by 15 · see citing works
Robust Human Activity Recognition via Wearable Sensors Using Dynamic Gaussian Kernel Learning
Wang, S., Sun, Y., Sha, Y., Yang, G., Cheng, D., Zhang, L., Wu, H., & Song, A. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3355704 · Cited by 16 · see citing works
Multimodal Human Activity Recognition for Smart Healthcare Applications
Islam, M. M., Nooruddin, S., & Karray, F. O. (2022) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc53654.2022.9945513 · Cited by 15 · see citing works
Deep Learning Model for CO2 Leakage Detection Using Pressure Measurements
Zhang, Z., He, X., AlSinan, M. M., Li, Y., Kwak, H. T., & Hoteit, H. (2022) · SPE Annual Technical Conference and Exhibition · doi:10.2118/209959-ms · Cited by 14 · see citing works
Human Activity Recognition Method Based on Edge Computing-Assisted and GRU Deep Learning Network
Huang, X., Yuan, Y., Chang, C., Gao, Y., Zheng, C., & Yan, L. (2023) · Applied Sciences · doi:10.3390/app13169059 · Cited by 17 · see citing works
Liu, M., Cheng, X., Shi, F., Liu, X., Dai, H., & Chen, S. (2024) · IEEE Transactions on Sustainable Computing · doi:10.1109/tsusc.2024.3353183 · Cited by 14 · see citing works
Few-shot transfer learning for wearable IMU-based human activity recognition
Ganesha, H. S., Gupta, R., Gupta, S. H., & Rajan, S. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-09645-7 · Cited by 16 · see citing works
Nisar, M. A., Shirahama, K., Irshad, M. T., Huang, X., & Grzegorzek, M. (2023) · Sensors · doi:10.3390/s23198234 · Cited by 13 · see citing works
Zou, H., Chen, Z.-J., Zhang, C., Yuan, A., Wang, B., Wang, L., Li, J.-Q., & Pan, Y.-Y. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3373444 · Cited by 13 · see citing works
ChatABL: Abductive Learning via Natural Language Interaction With ChatGPT
Zhong, T., Pan, Y., Zhang, Y., Wei, Y., Yang, L., Liu, Z., Wei, X., Li, W., Yao, J., Ma, C., et al. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3567945 · Cited by 13 · see citing works
Statistical Database of Human Motion Recognition Using Wearable IoT—A Review
Asl, E. F., Ebadollahi, S., Vahidnia, R., & Jalali, A. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3282171 · Cited by 15 · see citing works
Fire Hawk Optimizer with Deep Learning Enabled Human Activity Recognition
Alonazi, M., & Alnfiai, M. M. (2022) · Computer Systems Science and Engineering · doi:10.32604/csse.2023.034124 · Cited by 12 · see citing works
Ahmadian, S., Rostami, M., Farrahi, V., & Oussalah, M. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106159 · Cited by 13 · see citing works
MSMFT: Multi-Stream Multimodal Factorized Transformer for Human Activity Recognition
Zhou, X., Yuan, J., Fan, L., Niu, X., Zha, K., & Liu, X. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3529889 · Cited by 15 · see citing works
Human-in-the-loop layered architecture for control of a wearable ankle–foot robot
Martínez-Hernández, U., Firouzy, S., Mehryar, P., Meng, L., Childs, C. R., Buis, A. W. P., & Dehghani-Sanij, A. A. (2023) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2022.104353 · Cited by 12 · see citing works
Khatun, M. A., Yousuf, M., & Moni, M. A. (2023) · International Conference on Electrical, Computer and Communication Engineering · doi:10.1109/ecce57851.2023.10101550 · Cited by 12 · see citing works
The Power of ANN-Random Forest Algorithm in Human Activities Recognition Using IMU Data
Nia, N. G., Amiri, A., Nasab, A., Kaplanoğlu, E., & Liang, Y. (2023) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi58575.2023.10313507 · Cited by 12 · see citing works
Liu, J., Zhang, L., Liu, X., Yao, M., Wang, L., Wu, H., & Song, A. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3565646 · Cited by 12 · see citing works
Refat, M. A. R., Hossain, M. P., Islam, M. R., Rahman, A., Farid, F. A., Karim, H. B. A., & Miah, A. S. M. (2025) · Discover Applied Sciences · doi:10.1007/s42452-025-07896-0 · Cited by 11 · see citing works
Ryu, S., Yun, S., Lee, S., & Jeong, I. C. (2024) · Sensors · doi:10.3390/s24051610 · Cited by 11 · see citing works
Encoding human activities using multimodal wearable sensory data
Khan, M. H., Shafiq, H., Farid, M. S., & Grzegorzek, M. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125564 · Cited by 12 · see citing works
AbdelRaouf, H., Abouyoussef, M. S., & Ibrahem, M. I. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3585540 · Cited by 11 · see citing works
Human Activity Recognition via Attention-Augmented TCN-BiGRU Fusion
He, J.-L., Wang, J., Lo, C.-M., & Jiang, Z. (2025) · Sensors · doi:10.3390/s25185765 · Cited by 11 · see citing works
Data Integration Based Human Activity Recognition using Deep Learning Models
Patil, B. U., Ashoka, D. V., & Ajay Prakash, B., V (2023) · Karbala International Journal of Modern Science · doi:10.33640/2405-609x.3286 · Cited by 12 · see citing works
Enhancing Human Activity Recognition Through Sensor Fusion And Hybrid Deep Learning Model
Tarekegn, A. N., Ullah, M., Cheikh, F. A., & Sajjad, M. (2023) · IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) · doi:10.1109/icasspw59220.2023.10193698 · Cited by 11 · see citing works
Activity Recognition Using Different Sensor Modalities and Deep Learning
Ascioglu, G., & Şenol, Y. (2023) · Applied Sciences · doi:10.3390/app131910931 · Cited by 12 · see citing works
Enhancing Text Sentiment Classification with Hybrid CNN-BiLSTM Model on WhatsApp Group
Susandri, S., Defit, S., & Tajuddin, M. (2024) · Journal of Advances in Information Technology · doi:10.12720/jait.15.3.355-363 · Cited by 11 · see citing works
Li, J., Wang, Y., & Liu, Y. (2023) · Proceedings - International Symposium on Mixed and Augmented Reality, ISMAR · doi:10.1109/ismar59233.2023.00080 · Cited by 10 · see citing works
- Authors
- Challa, S. K., Kumar, A., & Semwal, V. B.
- Affiliations
- National Institute of Technology Jamshedpur (IN)
- Maulana Azad National Institute of Technology (IN)
- Corresponding author
- Challa, Sravan Kumar
- Venue
- The Visual Computer
- Published
- 2021-08-20
- Volume
- 38
- Issue
- 12
- Pages
- 4095-4109
- ISSN
- 0178-2789 (ISSN-L)
- 1432-2315
- OpenAlex
- W3193597430
- Cited by
- 338
- References
- 52
- Citations by year
- 2026: 44 · 2025: 85 · 2024: 78 · 2023: 96 · 2022: 34 · 2021: 1
- FWCI
- 18.81
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- The Visual Computer (publishedVersion, public-domain)
- Funders
- Department of Science and Technology (ECR/2018/000203)
- 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, wearable sensors, automatic feature extraction, time series classification, long-range dependencies, smartphone sensor data, WISDM, UCI-HAR, PAMAP2
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