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

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812

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

On the Properties of Neural Machine Translation: Encoder–Decoder Approaches

Cho, K., van Merriënboer, B., Bahdanau, D., & Bengio, Y. (2014) · SSST@EMNLP · doi:10.3115/v1/w14-4012 · Cited by 7,251

The Handbook of brain theory and neural networks

(1996) · Choice Reviews Online · doi:10.5860/choice.33-4484 · Cited by 3,913

Untitled work

(1989) · neural information processing systems · Cited by 3,736

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

Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations

Lee, H., Grosse, R., Ranganath, R., & Ng, A. Y. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553453 · Cited by 2,412

Untitled work

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

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 1,560

Human Activity Recognition Using Deep Learning Techniques for Healthcare Applications

Shekhar, R., Tomar, D. S., Sharan, B., & Pateriya, R. K. (2024) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt61001.2024.10725736 · Cited by 7

Sensor-Based Gymnastics Action Recognition Using Time-Series Images and a Lightweight Feature Fusion Network

Wang, W., Lian, C., Zhao, Y., & Zhan, Z. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3492004 · Cited by 7

Multi-Branch CNN GRU with attention mechanism for human action recognition

Verma, U., Tyagi, P., & Aneja, M. K. (2023) · Engineering Research Express · doi:10.1088/2631-8695/acd98c · Cited by 8

Delamination damage identification through multi-input data using impact echo and study of model efficiency

Wu, Y., Lamsal, A., & Ham, S. P. (2024) · Nondestructive Testing And Evaluation · doi:10.1080/10589759.2024.2440644 · Cited by 6

An Assessment Towards 2D and 3D Human Pose Estimation and its Applications to Activity Recognition: A Review

Verma, P., Srivastava, R., & Tripathy, S. K. (2025) · SN Computer Science · doi:10.1007/s42979-025-03708-9 · Cited by 7

TDI-TFFNet: Infusing time dependent images and two-stream feature fusion network for gymnastic activity recognition

Lian, C., Li, W., Zhou, D., Kang, Y., Sun, T., Lyu, X., Zhan, Z., & Zhao, Y. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.112726 · Cited by 6

Research on Human Activity Recognition Algorithm Based on LSTM-1DCNN

Zhao, Y., Wang, X., Luo, Y., & Aslam, M. S. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.040528 · Cited by 6

CNN-Based Multi-Factor Authentication System for Mobile Devices Using Faces and Passwords

Han, J. (2024) · Applied Sciences · doi:10.3390/app14125019 · Cited by 6

Fitcam: detecting and counting repetitive exercises with deep learning

Japhne, F., Janada, K., Theodorus, A., & Chowanda, A. (2024) · Journal Of Big Data · doi:10.1186/s40537-024-00915-8 · Cited by 6

1-DCNN with Stacked LSTM Architecture for Human Activity Recognition Using Wearable Sensing Data

Krishnaleela, P., & Prakash, R. M. (2024) · IETE Journal of Research · doi:10.1080/03772063.2024.2402881 · Cited by 6

Human activity recognition by wireless body area networks through multi‐objective feature selection with deep learning

Boga, J., & Kumar, V. D. (2022) · Expert Systems · doi:10.1111/exsy.12988 · Cited by 6

Design of methods for impact detection in geotechnical protection fences using Machine Learning

Velička, J., Pieš, M., & Hájovský, R. (2022) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2022.06.015 · Cited by 6

A Computational Approach to Identify Normal and Abnormal Persons Gait Using Various Machine Learning and Deep Learning Classifier

Yadav, R. K., Neogi, S. G., & Semwal, V. B. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-24352-3_2 · Cited by 6

A Novel Framework for Future Human Activity Prediction Using Sensor-Based Data

(2023) · International journal of intelligent engineering and systems · doi:10.22266/ijies2023.1231.81 · Cited by 5

An Unsupervised Method to Recognise Human Activity at Home Using Non-Intrusive Sensors

Ramos, R. G., Domingo, J. D., Zalama, E., & Gómez‐García‐Bermejo, J. (2023) · Electronics · doi:10.3390/electronics12234772 · Cited by 8

Recognition and detection of unusual activities in ATM using dual-channel capsule generative adversarial network

Kajendran, K., & Mayan, J. A. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122987 · Cited by 6

A Novel Dead Reckoning System Based on Wearable Exoskeleton for Rat-Robot Localization

Yang, C., Chen, Y., Xu, H., Xu, K., & Yang, W. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3119381 · Cited by 6

U-TSS: a novel time series segmentation model based U-net applied to automatic detection of interference events in geomagnetic field data

Shan, W., Wang, M., Xia, J., Chen, J., Li, Q., Xing, L., Zhang, R., Wang, M., Zhang, S., & Zhang, X. (2025) · PeerJ Computer Science · doi:10.7717/peerj-cs.2678 · Cited by 5

An Architecture for Human Activity Recognition Using TCN-Bi-LSTM HAR based on Wearable Sensor

Bijrothiya, S., & Soni, V. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.03.261 · Cited by 6

HCMMA-Net: A Hybrid Convolutional Multi-Modal Attention Network for Human Activity Recognition in Smart Homes Using Wearable Sensor Data

Ashfaq, N., Aziz, Z., Khan, M. H., Nisar, M. A., & Khalid, A. (2025) · IEEE Access · doi:10.1109/access.2025.3577730 · Cited by 5

HAR: A stacked ensemble learning approach based on smartphone sensors for activity detection

Lalwani, P., & Ganeshan, R. (2024) · Internet of Things · doi:10.1016/j.iot.2024.101487 · Cited by 5

Multichannel-CNN-GRU-based surface-enhanced Raman spectroscopy for fuzzy identification of illicit drugs

Huang, R., Peng, A., Hu, J., Zhou, Y. R., & Hu, W. (2023) · Scientia Sinica Chimica · doi:10.1360/ssc-2022-0227 · Cited by 5

Subtransfer Learning in Human Activity Recognition: Boosting the Outlier User Accuracy

Kanjilal, R., Küçük, M. F., & Uysal, I. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3312146 · Cited by 5

Features gradient-based signals selection algorithm of linear complexity for convolutional neural networks

Omae, Y., Sakai, Y., & Takahashi, H. (2023) · AIMS Mathematics · doi:10.3934/math.2024041 · Cited by 5

Human Activity Recognition by BiLSTM Recurrent Neural Networks and Support Vector Machine

Ridha, A. A., Almaameri, I. M., Blázovics, L., & Abbas, H. M. (2023) · International Conference on Engineering Technology and its Applications (IICETA) · doi:10.1109/iiceta57613.2023.10351372 · Cited by 7

Comparative Study of Classification of Eye Disease Types Using DenseNet and EfficientNetB3

Jatmoko, C., Lestiawan, H., Agustina, F., & Erawan, L. (2024) · Kinetik Game Technology Information System Computer Network Computing Electronics and Control · doi:10.22219/kinetik.v9i3.1931 · Cited by 5

Two-Stream Modality-Based Deep Learning Approach for Enhanced Two-Person Human Interaction Recognition in Videos

Akash, H. S., Rahim, M. A., Miah, A. S. M., Lee, H.-S., Jang, S.-W., & Shin, J. (2024) · Sensors · doi:10.3390/s24217077 · Cited by 5

A Novel Martingale Based Model Using a Smartphone to Detect Gait Bout in Human Activity Recognition

Etumusei, J., Carracedo, J. M., & McClean, S. I. (2022) · Journal of Sensors · doi:10.1155/2022/4753732 · Cited by 6

OVERSAMPLING TECHNIQUE-BASED DATA AUGMENTATION AND 1D-CNN AND BIDIRECTIONAL GRU ENSEMBLE MODEL FOR HUMAN ACTIVITY RECOGNITION

Kim, Y. W., Cho, W. H., Kim, K., & Lee, S. (2022) · Journal of Mechanics in Medicine and Biology · doi:10.1142/s0219519422400486 · Cited by 5

Human activity recognition using deep learning techniques with spider monkey optimization

Kolkar, R., & Geetha, V. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15007-7 · Cited by 6

Circular convolution-based feature extraction algorithm for classification of high-dimensional datasets

Tajanpure, R., & Muddana, A. L. (2021) · Journal of Intelligent Systems · doi:10.1515/jisys-2020-0064 · Cited by 5

Dual-phase neural networks for feature extraction and ensemble learning for recognizing human health activities

Dhar, J., Rana, K., Goyal, P., Alavi, A., Rana, R. K., Vo, Q. B., Mishra, S., & Mistry, S. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.112550 · Cited by 4

A Lightweight Spatial–Temporal Deep Learning Network for Multiple Events Classification in Optical Fiber Perimeter Security System

Sun, Z., Fang, M., Niu, J., & Xue, K. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3559892 · Cited by 4

Retentive-HAR: Human Activity Recognition from Wearable Sensors with Enhanced Temporal and Inter-Feature Dependency Retention

Ige, A. O., Oladele, D. A., & Sibiya, M. (2025) · Applied Sciences · doi:10.3390/app152312661 · Cited by 4

CatBoost-based stacked ensemble model for human activity recognition

Lalwani, P., Ganeshan, R., & Lalwani, P. (2025) · Multiagent and Grid Systems · doi:10.1177/15741702251403182 · Cited by 4

A Feature-Driven Hybrid Deep Learning for Human Activity Recognition in Holographic IoT Systems

Bijrothiya, S., & Soni, V. (2026) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2026.3655723 · Cited by 4

A Convolutional Neural Network Model for Classifying Resting Tremor Amplitude in Parkinson’s Disease

Ielo, A., Dattola, S., Bonanno, L., Pasquale, P. D., Cacciola, A., Quartarone, A., & Cola, M. C. D. (2025) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2025.3574999 · Cited by 4

Toward extracting and exploiting generalizable knowledge of deep 2D transformations in computer vision

Kang, J., Jia, W., & He, X. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126882 · Cited by 4

Human Activity Recognition from Accelerometry, Based on a Radius of Curvature Feature

Cavita-Huerta, E., Reyes‐Reyes, J., Ugalde, H. M. R., Osorio-Gordillo, G. L., Escobar-Jiménez, R. F., & Alvarado, V. M. (2024) · Mathematical and Computational Applications · doi:10.3390/mca29050080 · Cited by 4

Comprehensive Analysis of Deep Learning-based Human Activity Recognition approaches based on Accuracy

Fataniya, A. G., & Modi, H. P. (2022) · International Journal of Computing and Digital Systems · doi:10.12785/ijcds/120188 · Cited by 4

An efficient 3D convolutional neural network with informative 3D volumes for human activity recognition using wearable sensors

Zebhi, S. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17400-8 · Cited by 4

Deep Learning for Depression Symptomatic Activity Recognition

Anjum, F., Alam, S., Bahadur, E. H., Masum, A. K. M., & Rahman, M. Z. (2022) · International Conference on Innovations in Science, Engineering and Technology (ICISET) · doi:10.1109/iciset54810.2022.9775922 · Cited by 4

Daily life human activities recognition using a novel AR‐DenseNet

Ghalan, M., & Aggarwal, R. K. (2022) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.7142 · Cited by 4

Human Action Recognition from Videos Using Motion History Mapping and Orientation Based Three-Dimensional Convolutional Neural Network Approach

Arora, I., & Gangadharappa, M. (2025) · Modelling—International Open Access Journal of Modelling in Engineering Science · doi:10.3390/modelling6020033 · Cited by 4

Advanced AI and Temporal Analysis for Accurate Cricket Shot Classification

Pattanashetty, P. T., Prajwal, K. M., Sujeeth, K., Ram, K. P. S., & Uma, D. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-92605-1_4 · Cited by 3

Deep learning for human activity recognition: A comprehensive review of architectures, performance, and challenges across five sensory datasets

Brery, A. F., Gallardo-Antolín, A., Fakhry, M., & González‐Carrasco, I. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.132415 · Cited by 3

Human Activity Recognition with IMU and Vital Signs Feature Fusion

Xefteris, V.-R., Tsanousa, A., Mavropoulos, T., Μεδίτσκος, Γ., Vrochidis, S., & Kompatsiaris, I. Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-98358-1_23 · Cited by 3

Simulation and Experimental Research of Thermal Power Plant Pipeline Displacement via Deep Learning Algorithms

Dai, M., Jian, Y., Liu, Q., Gao, B., Zhao, X., Zhang, J., & Zhou, B. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3303495 · Cited by 3

Cascade Human Activity Recognition Based on Simple Computations Incorporating Appropriate Prior Knowledge

Wang, J., Zhang, K., Zhao, Y., Wang, X., & Aslam, M. S. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.040506 · Cited by 3

Research on TCN Model Based on SSARF Feature Selection in the Field of Human Behavior Recognition

Zhang, W., Yu, G., & Deng, S. (2024) · IET Biometrics · doi:10.1049/2024/4982277 · Cited by 3

Ensem-DeepHAR: Identification of human activity in smart environments using ensemble of deep learning methods and motion sensor data

Islam, S. M. M., & Talukder, K. H. (2024) · Measurement Sensors · doi:10.1016/j.measen.2024.101398 · Cited by 4

A Sequential-Data Approach for Human Gait Parameter Estimation and Surgical Event Inference

Hamid, S., Khosa, I., Iftikhar, M. A., Usman, M. R., & Usman, M. A. (2024) · IEEE Access · doi:10.1109/access.2024.3493389 · Cited by 3

Next‐Generation Human Activity Recognition Using Locality Constrained Linear Coding Combined With Machine Learning (NG‐HAR‐LCML)

Shabbir, M., Ahmad, F., Alanazi, S., Khan, M. H., Li, J., Mahmood, T., Naseem, S., & Anwar, M. (2025) · Healthcare Technology Letters · doi:10.1049/htl2.70013 · Cited by 3

CNN-SLSTM framework for human activity recognition using wearable sensor data

Krishnaleela, P., & Prakash, R. M. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-11410-3 · Cited by 6

Monitoring Discrete Activities of Daily Living of Young and Older Adults Using 5.8 GHz Frequency Modulated Continuous Wave Radar and ResNet Algorithm

Saeed, U., Alqahtani, F., Baothman, F. A., Shah, S. Y., Shah, S. I., Badshah, S. S., Imran, M. A., Abbasi, Q. H., & Shah, S. A. (2022) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-95593-9_3 · Cited by 3

Hand Gesture Recognition

Sharma, H. K., & Choudhury, T. (2022) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-7998-9434-6.ch003 · Cited by 3

Recognition and Analysis of Sports on Mental Health Based on Deep Learning

Li, L., & Li, H. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.897642 · Cited by 3

Human Activity Recognition Using 1-Dimensional CNN and Comparison with LSTM

Kashyap, S. K., Mahalle, P. N., & Shinde, G. R. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-4364-5_72 · Cited by 3

Merging-Squeeze-Excitation Feature Fusion for Human Activity Recognition Using Wearable Sensors

Laitrakun, S. (2023) · Applied Sciences · doi:10.3390/app13042475 · Cited by 3

Skipper: A Federated Siamese Network-Based Group Activity Segregator for IoMT Systems

Kotiyal, V., Gupta, A., Deb, P. K., Misra, S. C., Das, D., & Udutalapally, V. (2023) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2023.3244188 · Cited by 4

Proposed Hybrid Model Recurrent Neural Network for Human Activity Recognition

Errafik, Y., Kenzi, A., & Dhassi, Y. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-29857-8_8 · Cited by 3

Human activity detection using smartphone sensor data in real-time based on deep learning approaches

Bera, S., Pati, A., & Parhi, M. (2023) · AIP conference proceedings · doi:10.1063/5.0137525 · Cited by 4

Conditional Human Activity Signal Generation and Generative Classification with a GPT-2 Model

Zilelioglu, H., Khodabandelou, G., Chibani, A., & Amirat, Y. (2023) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn54540.2023.10191464 · Cited by 3

Diagnosis of Mental Illness Using Deep Learning: A Survey

Rajendran, S., Gandhi, R., Smruthi, S., Chaudhari, S., & Kumar, S. (2023) · Intelligent systems reference library · doi:10.1007/978-3-031-12419-8_12 · Cited by 4

A Deep Learning Based System to Estimate Crowd and Detect Violence in Videos

Kumar, Y. H. S., & Naveena, C. (2023) · Intelligent systems reference library · doi:10.1007/978-3-031-12419-8_3 · Cited by 3

Legal Transition Sequence Recognition of a Bounded Petri Net Using a Gate Recurrent Unit

Zeng, Q., Guo, S., Cao, R., Zhao, Z., & Duan, H. (2023) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2023.3319252 · Cited by 3

Human Activity Recognition Using Sensor Fusion and Deep Learning for Ergonomics in Logistics Applications

Arab, A., Schmidt, A., & Aufderheide, D. (2023) · IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS) · doi:10.1109/iotais60147.2023.10346052 · Cited by 3

Enhanced Recognition of Human Activity using Hybrid Deep Learning Techniques

Abinaya, S., Rajasenbagam, T., Indira, K., Uttej Kumar, K., & Jayanth, P. (2024) · International Journal of Electrical and Electronics Research · doi:10.37391/ijeer.120106 · Cited by 3

A Comparative Study of Javanese Script Classification with GoogleNet, DenseNet, ResNet, VGG16 and VGG19

Susanto, A., Sari, C. A., Rachmawanto, E. H., Mulyono, I. U. W., & Yaacob, N. M. (2024) · Scientific Journal of Informatics · doi:10.15294/sji.v11i1.47305 · Cited by 12

Enhanced human activity recognition through deep multi-layer perceptron on the UCI-HAR dataset

Hossain, M. A., Ray, S. K., Islam, N., Alamin, A., & Hasan, M. A. R. (2024) · International Journal of Advances in Applied Sciences · doi:10.11591/ijaas.v13.i2.pp429-438 · Cited by 3

Multi-head CNN-based activity recognition and its application on chest-mounted sensor-belt

Verma, U., Tyagi, P., & Aneja, M. K. (2024) · Engineering Research Express · doi:10.1088/2631-8695/ad43b9 · Cited by 3

Fruit Deformity Classification Through Single-Input and Multi-input Architectures Based on CNN Models Using Real and Synthetic Images

Beltran, T. D., Villao, R. J., Chuquimarca, L. E., Vintimilla, B. X., & Velastín, S. A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-76607-7_4 · Cited by 3

DiscHAR: A Discrete Approach to Enhance Human Activity Recognition in Cyber Physical Systems: Smart Homes

Fatima, I., Farhan, A. A., Tamoor, M., Rehman, S. U., Alhulayyil, H. A., & Tariq, F. (2024) · Computers · doi:10.3390/computers13110300 · Cited by 4

An Intelligent Human Activity Recognizer for Visually Impaired People Using VGG-SVM Model

Saleem, R., Ahmad, T., Aslam, M., & Martínez-Enríquez, A. M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19496-2_28 · Cited by 3

Expanding the Horizons of 1-D Tasks: Leveraging 2-D Convolutional Neural Networks Pretrained by ImageNet

Zhongkai, Z., Takenaka, K., & Hasegawa, T. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3415114 · Cited by 2

WASENN: Wavelet Assisted Stochastic Enabled Neural Network for Human Activity Recognition

Sengupta, R., Polian, I., & Hayes, J. P. (2025) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2025.3540564 · Cited by 2

Long Short-Term Memory Network (LSTM) is used to Model Action Sequences

Wang, Y., & Guo, W. (2025) · International Conference on Intelligent Systems and Computational Networks (ICISCN) · doi:10.1109/iciscn64258.2025.10934377 · Cited by 2

Fall Detection Using Sound with FPGA Signal Analysis and Machine Learning Integration

Andre, M. V., Özdemir, A. T., & Kavuncuoğlu, E. (2025) · International Conference on Machine Learning and Autonomous Systems (ICMLAS) · doi:10.1109/icmlas64557.2025.10967824 · Cited by 2

Hyperbolic Transformers with LLMs for Multimodal Human Activity Recognition

Soleimani, F., Khodabandelou, G., Chibani, A., & Amirat, Y. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11247069 · Cited by 2

From benchmarks to interpretability: A holistic survey of deep learning for time series classification

Jeong, J., Oh, Y., Kim, D., & Yoon, H. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.115251 · Cited by 2

TCP-HAR: On-Device Transferable and Copyright-Preserving Human Activity Recognition

Sacco, A., Palermo, B., Figliolino, G., Contoli, C., Marchetto, G., & Esposito, F. (2026) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2026.102163 · Cited by 2

Enhanced human activity recognition using 2DCNN, BiLSTM, and soft attention mechanism

Naghavi, K., Kamel, S. R., & Hosseini, S. A. (2026) · Multimedia Tools and Applications · doi:10.1007/s11042-026-21312-8 · Cited by 2

MLConvTrans: Multi-Level Convolutional Transformer for Wearable Sensor Based Human Activity Recognition

VUONG, T. H., Doan, T., & Takasu, A. (2026) · Neural Processing Letters · doi:10.1007/s11063-026-11854-1 · Cited by 2

Activity Recognition Method Based on Kernel Supervised Laplacian Eigenmaps

Tu, P., Cheng, T., Du, D., Li, J., & Wang, H. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446979 · Cited by 2

Revolutionizing Gait Analysis with Blockchain Technology: Enhancing the Privacy and Security

Tokas, P., Semwal, V. B., Jain, S., & Tomar, D. S. (2024) · Contributions to environmental sciences & innovative business technology · doi:10.1007/978-981-97-0088-2_12 · Cited by 2

Assessment of IMU Configurations for Human Activity Recognition Using Ensemble Learning

Murphy, S. J., & Vitali, R. V. (2024) · IEEE Access · doi:10.1109/access.2024.3440215 · Cited by 3

A New Model for Assembly Task Recognition: A Case Study of Seru Production System

Torkul, O., Selvi, İ. H., Şişci, M., & Diren, D. D. (2024) · IEEE Access · doi:10.1109/access.2024.3484955 · Cited by 2

Non-Contact Cross-Person Activity Recognition by Deep Metric Ensemble Learning

Ye, C., Xu, S., He, Z., Yin, Y., Ohtsuki, T. O., & Gui, G. (2024) · Bioengineering · doi:10.3390/bioengineering11111124 · Cited by 2

Development of Risk Activity Detection System for Forklifts Based on Inertial Sensors

Radrigán, L., & Godoy, S. E. (2024) · IEEE Access · doi:10.1109/access.2024.3524032 · Cited by 5

Multiscale Residual Weighted Classification Network for Human Activity Recognition in Microwave Radar

Gao, Y., Cao, L., Zhao, Z., Wang, D., Fu, C., & Guo, Y. (2025) · Sensors · doi:10.3390/s25010197 · Cited by 2

An Algorithm for Mining the Living Habits of Elderly People Living Alone Based on AIoT

Wu, J., Lu, Y., & Jiang, Y. (2025) · Italian National Conference on Sensors · doi:10.3390/s25072299 · Cited by 2

The application of suitable sports games for junior high school students based on deep learning and artificial intelligence

Ji, X., Samsudin, S., Hassan, M., Farizan, N. H., Yuan, Y., & Wang, C. (2025) · Scientific Reports · doi:10.1038/s41598-025-01941-z · Cited by 8

Distance graph routing capsule network with vote elimination for human activity recognition

Feraydounraad, E., Hoseini, K., & Nazerfard, E. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128236 · Cited by 2

Optimizing Human Action Recognition in Still Images Using Deep Learning Models and Grad-CAM++ for Visualization

Alam, M. T., Dasgupta, S., & Roy, K. (2024) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccit64611.2024.11022452 · Cited by 3

Efficient Keyset Design for Neural Networks Using Homomorphic Encryption

Joo, Y., Ha, S., Oh, H., & Paek, Y. (2025) · Sensors · doi:10.3390/s25144320 · Cited by 3

Temporal and Modality Awareness-Based Lightweight Residual Network With Attention Mechanism for Human Activity Recognition Using a Lower-Limb Exoskeleton Robot

Son, C.-S., & Kang, W. (2025) · IEEE Access · doi:10.1109/access.2025.3590407 · Cited by 2

Performance Assessment for Heart-Disease Prediction Using Machine Learning Algorithms

Singh, V., & Arora, A. V. (2022) · Smart innovation, systems and technologies · doi:10.1007/978-981-19-2719-5_23 · Cited by 2

TConvRec: temporal convolutional-recurrent fusion model with additional pattern learning

Singh, B., & Jaiswal, R. (2022) · International Journal of Information Technology · doi:10.1007/s41870-022-01116-3 · Cited by 2

Deep CNN-GRU Based Human Activity Recognition with Automatic Feature Extraction Using Smartphone and Wearable Sensors

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

Single Input-Based CNN-LSTM and CNN-GRU Based HAR Using Wearable SensorsPreprint

Bhardwaj, K., Aryan, A., & Yadav, R. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4159485

Single Input Single Head CNN-GRU-LSTM Architecture for Recognition of Human Activities

Verma, U., Tyagi, P., & Kaur, M. (2022) · Indonesian Journal of Electrical Engineering and Informatics (IJEEI) · doi:10.52549/ijeei.v10i2.3475 · Cited by 8

Multi-sensor human activity recognition using CNN and GRU

Nafea, O., Abdul, W., & Muhammad, G. (2022) · International Journal of Multimedia Information Retrieval · doi:10.1007/s13735-022-00234-9 · Cited by 36

Deep learning based human activity recognition (HAR) using wearable sensor data

Gupta, S. (2021) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2021.100046 · Cited by 225