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Sensor-based and vision-based human activity recognition: A comprehensive survey
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“The sliding window technique is commonly used. It segments the input sensors data into slices with equal size [8][10]. Each slice with multiple samples is then classified as a certain class (window-wise classification).”
From DS-MS-TCN: Otago Exercises Recognition With a Dual-Scale Multi-Stage Temporal Convolutional Network (2024) · cites this paper as [8]
Citations
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
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
(1993) · doi:10.1109/cvpr.1993 · Cited by 17,989
Proceedings of IEEE International Conference on Computer Vision
(1995) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.1995.466933 · Cited by 13,117
Advances in Neural Information Processing Systems 25
Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653
McLachlan, G., & Peel, D. (2000) · Wiley Series in Probability and Statistics · doi:10.1002/0471721182 · Cited by 10,991
Large-Scale Video Classification with Convolutional Neural Networks
Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., & Fei-Fei, L. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.223 · Cited by 6,354
2009 IEEE Conference on Computer Vision and Pattern Recognition
(2009) · doi:10.1109/cvpr14067.2009 · Cited by 5,646
Ucf101: A dataset of 101 human actions classes from videos in the wild
Soomro, K., Zamir, A. R., & Shah, M. (2012) · arXiv · doi:10.48550/arxiv.1212.0402 · Cited by 4,575
Hmdb: a large video database for human motion recognition
Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., & Serre, T. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126543 · Cited by 3,965
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
Research works printed but not linked: 1
- Tsitsoulis, 2013, A first stage comparative survey on vision-based human activity recognition, J. AI Tools, 24
Cited by
Sorted by citations
Items 601–700 of 718
Challenges and Opportunities for the Recognition of Human Activity in Supervised Flats
Lloret, A. T., Valera, J. C., Gil, D., Peral, J., Ferrández, A., & Amador, S. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-031-21333-5_72
Lin, Y. (2022) · International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) · doi:10.1109/icaiss55157.2022.10010785
“It is a very difficult problem to completely ext ract the human body area in a complex background [10, 11, 12].”
BackgroundA New Effective Speed and Distance Feature Descriptor Based on Optical Flow Approach in HAR
Hua, G. H., Kumar, H., Aradhya, M., & Maheshan, M. S. (2023) · Revue d intelligence artificielle · doi:10.18280/ria.370114
Landmark Identification from Low-Resolution Real-Time Image for Pose Estimation
Sarkar, R., Bhattacharyya, S., De, D., & Datta, A. K. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-8477-8_1
Ciszewski, M. G., Söhl, J., & Jongbloed, G. (2023) · Statistical Methods & Applications · doi:10.1007/s10260-023-00696-z
“A plethora of methods are currently being used to detect human activities (Lara and Labrador 2013), with various deep learning techniques leading the charge (Minh Dang et al. 2020; Wang et al. 2019).”
BackgroundReal-Time Data Cloud Transmission and Early Warning Algorithm for Outdoor Sports with Smart Glasses
Li, G. (2023) · International Conference on Inventive Computation Technologies (ICICT) · doi:10.1109/icict57646.2023.10134159
A Study on Improved Pet Behavior Monitoring Through Data Imputation Using Multiple Data
Kim, J., Kim, H., Park, C., Park, J.-H., & Moon, N. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-1252-0_90
Firdaus, V. A. H., Apriyani, M. E., & Aprilia, N. L. (2023) · Jurnal Sistem Komputer dan Informatika (JSON) · doi:10.30865/json.v4i3.5896
“Bahkan pada setiap detik manusia telah memanfaatkan berbagai macam teknologi dalam kegiatannya [2], seperti pada contohnya yakni penerapan teknologi Internet of Things.”
BackgroundBouchabou, D., Nguyen, S. M., Lohr, C., Kanellos, I., & Leduc, B. (2023) · Revue Ouverte d Intelligence Artificielle · doi:10.5802/roia.53 · Cited by 3
Dhage, J. S., Gulve, A. K., & Shetiye, P. C. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-9748-8_1
Khan, M. H., Shabbir, M., Li, J., Naseem, S., Mahmood, T., & Ahmad, F. (2023) · Research Square · doi:10.21203/rs.3.rs-3100158/v1
Rawat, P. S., & Kane, L. (2023) · Advances in intelligent systems research/Advances in Intelligent Systems Research · doi:10.2991/978-94-6463-196-8_21
RetractedWang, S., Zhou, C., Pan, J., Liu, P., Feng, D., & Li, Z. (2022) · IEEE Smart World Congress (SWC) · doi:10.1109/smartworld-uic-atc-scalcom-digitaltwin-pricomp-metaverse56740.2022.00370
“In the area of computer vision, human action recognition is a significant task with applications in human-machine interaction, video surveillance, virtual reality and so on [1], [2].”
BackgroundAssessment of Human Personality Traits Using Smartphone Sensing
Rafique, S., Ehatisham-ul-Haq, M., Ibrar, K., Yasin, A., Murtaza, F., & Azam, M. A. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-37717-4_39
Research on IoT data aggregation by fusing fast matching algorithms
Jiang, C., & Chen, Q. (2023) · Applied Mathematics and Nonlinear Sciences · doi:10.2478/amns.2023.2.00305
Unsupervised Human Activity Recognition Approach in Multi-User HouseholdsPreprint
Gómez-Ramos, R., Domingo, J. D., Zalama, E., & Gómez‐García‐Bermejo, J. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4586324
Person Activity Classification from an Aerial Sensor Based on a Multi-level Deep Features
Bouhlel, F., Mliki, H., & Hammami, M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-45382-3_6
Pattern Recognition in Embedded Systems for Event Occurrences
-, A. D. U., & Tirmare, A. H. (2023) · International Journal For Multidisciplinary Research · doi:10.36948/ijfmr.2023.v05i06.8620
Szczerba, A., Prochor, P., & Piszczatowski, S. (2023) · Bulletin of the Polish Academy of Sciences Technical Sciences · doi:10.24425/bpasts.2024.148835
Artificial Neural Network-Powered, Driverless Vehicle Concept Development
Kadeem, S. R. A., Naser, A. H., Hassan, A. R., & Betti, G. A. (2023) · AlKadhum Journal of Science · doi:10.61710/akjs.v1i2.63
Xie, W., Huang, Q., Li, C., Wang, Y., & Liu, Y. (2023) · ACM International Conference on Multimedia in Asia (MMAsia) · doi:10.1145/3595916.3626425
“HAR can be performed in both visual and non-visual modalities [3, 4], with visual modalities including data types such as RGB video, depth, bone, and point cloud, and non-visual modalities including sensor signals from wearable devices, radar, magnetic field, and Wi-Fi signal data types [5].”
MethodPattern Recognition in Embedded Systems for Event Occurrences
-, A. D. U., Khapre, S., & Kasabe, M. (2024) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2024.58523
Aziz, K., Dua, S., & Gupta, P. (2023) · International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES) · doi:10.1109/icses60034.2023.10465456
“[7,17,18] A. Vision-Based Human vision recognition (HAR) involves the use of cameras to monitor user behaviour and changes in the environment [17,18].”
BackgroundAn Unsupervised Approach to Motion Detection Using WiFi Signals
Tahir, N., Liu, Y., Wang, T., Katz, G. E., & Chen, B. (2023) · International Conference on Machine Learning and Applications · doi:10.1109/icmla58977.2023.00143 · Cited by 1
“In doing so, it does away with the need for specialized embedded or vision sensors often relied upon in conventional sensing methods and instead uses ambient WiFi signals [2].”
BackgroundOptimizing End-to-End Sensor-Based Human Activity Recognition Through Multi-Attention InteractionPreprint
Yu, Y., Wang, H., Wang, J., Yan, M., Han, X., Wu, D., Shen, Q., & Liu, H. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4777248
Self-citationRudraksha, L., & Naidu, T. M. P. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-0644-0_39
Filtering walking actigraphy data in children with unilateral cerebral palsy: A preliminary study
Hwang, Y., & Kwon, J. (2024) · PLoS ONE · doi:10.1371/journal.pone.0303090
Feature representation learning for human activity recognition
Khaertdinov, B. (2024) · Research Publications (Maastricht University) · doi:10.26481/dis.20240618bk
Association Rule-based Soft Labeled Human Activity Recognition Using Wearable Sensors
Geng, X., Chen, X., Ma, Z., Zhang, J., Lin, S., & Hao, Z. (2024) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc62350.2024.10588250
“Compared with the traditional vision-based recognition techniques aiming for the images or videos from optical sensors in a specific area [8], the wearable sensors enable automatically detecting and tracking the daily activities that are suitable for the recognition at any place [9], [10].”
BackgroundYu, Z., Crane, C. A., Chen, L., Testa, M., & Zheng, Z. (2024) · ACM Transactions on Computing for Healthcare · doi:10.1145/3664826
“Regression models are effective tools to investigate the association between elementary behavioral cues and high-level activity interpretations [19].”
Muscle Atrophy Monitoring Using Machine Learning and Surface Electromyography
Chaczko, Z., Chiu, C., Ho, T. P., Hassani, H., Wajs-Chaczko, P., Kulbacki, M., Klempous, R., & Kulbacki, M. (2024) · Topics in intelligent engineering and informatics · doi:10.1007/978-3-031-58257-8_10
Survey on Objective Measurement and Sensor-Based Detection of Physical and Social Activities
Mahmood, A., Sridevi, P., Meikandan, P. V., & Ahamed, S. I. (2024) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac61105.2024.00083
“In the literature, two primary methods stand out for approaching physical activity detection: sensor-based and vision-based [5] [6].”
BackgroundObfuscating Sensor-Based Activity Recognition in eHealth Applications: Is Encryption Enough Secure?
Marcello, F. C., Pettorru, G., Martalò, M., & Pilloni, V. (2024) · International Conference on Communications · doi:10.1109/icc51166.2024.10623045
“In terms of the type of data analyzed, it can be divided into vision-based and sensor-based HAR [9].”
BackgroundHuman Activity Recognition Based on Multidomain Fusion Network for LFMCW Radar
Liu, Z., Guo, S., Ding, C., Tang, L., & Cui, G. (2024) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss53475.2024.10641965
“What’s more, compared to other commonly used sensors, radar avoids the temperature effects of infrared sensors and the device requirements of wearable devices [5].”
BackgroundA Review on the Classification of Body Movement Time Series to Support Clinical Decision-Making
Spolaôr, N., Lee, H., Ensina, L. A., Takaki, W. S. R., Coy, C. S. R., & Wu, F. C. (2024) · Studies in computational intelligence · doi:10.1007/978-3-031-69769-2_2
Applications and challenges of deep learning in human activity detection
Huang, S. (2024) · AIP conference proceedings · doi:10.1063/5.0222844
Lopes, A., García, N. M., Ríos, I. J. C., Medina-Quero, J., & Polo-Rodríguez, A. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-77571-0_32
MoViAn: Advancing Human Motion Analysis with 3D Visualization and Annotation
Qi, D., Browen, I., Zhang, D., Camarillo-Abad, H. M., & Cibrian, F. L. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-77571-0_2
Enhancing Fall Detection Accuracy: The Ground-Face Coordinate System for 3D Accelerometer Data
Sözer, A. T. (2024) · Sakarya University Journal of Computer and Information Sciences · doi:10.35377/saucis...1522290
Engaging Older Adults at Meal-Time Through AI-Empowered Socially Assistive Robots
Carolis, B. N. D., Loglisci, C. A. A. M. D., Macchiarulo, N., & Palestra, G. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-74640-6_29
Sharma, N., Sun, C., Zhao, Z., Ngu, A. H. H., Latapie, H. M., & Yan, Y. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-2071-5_7
“According to the type of data input as shown in figure 1, HAR systems can be roughly classified into two lines: non-visual modality-based sys-tem [38] and visual modality-based one [34].”
PER-HDL: Physiotherapy exercise recognition with RGB-D Kinect sensor and hybrid deep learning models
Bijalwan, V., Baek, H., Khan, A. M., Mohamed, S., & Kim, Y. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-21505-6.00006-2
Machinery Activity Recognition in the Industry Based on Heterogeneous Data
Podobińska-Staniec, M., Kęsek, M., & Brzychczy, E. (2025) · Lecture notes in business information processing · doi:10.1007/978-3-031-78666-2_10
Flexible Strain Sensors With Multifusion Algorithm Models for Handwriting Recognition
Zhou, X., Wang, W., Hui, Y., Li, X., Yan, X., & Cheng, T. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3540598
“The third type is a fiber-based sensor that achieves conductivity by coating silver nanowires onto electrospun SBS yarn, but these nanowires tend to break under dry conditions or external forces, leading to lower stability and durability compared with carbon-based materials [7], [8], [9].”
Preserving Privacy in CSI-based Human Activity Recognition: a Data Obfuscation Case Study
Marcello, F. C., Pettorru, G., Martalò, M., & Pilloni, V. (2024) · Globecom · doi:10.1109/globecom52923.2024.10901018
“In particular, it is possible to distinguish between sensor-based and video-based approaches [6].”
Anomaly Detection in Kitchen Appliance Usage using Machine Learning Approach
Gahramanov, J., Naseer, N., & Gahramanova, A. (2025) · doi:10.38124/ijisrt/25mar233
Badhagouni, S. K., Raju, V., Yoganand, B. V., & Pushkarna, G. (2025) · AIP conference proceedings · doi:10.1063/5.0262819
Shejuti, Z. T., & Fuad, M. (2025) · IEEE SoutheastCon · doi:10.1109/southeastcon56624.2025.10971620
La esclavitud, el tráfico de esclavizados y la infancia de José Martí
Utset, M. I. (2024) · Revista de Indias · doi:10.3989/revindias.2024.1739
yassen, K. D., Abbas, H. H., & Hameed, A. (2024) · Kerbala Journal for Engineering Sciences · doi:10.63463/kjes1137
Movement Detection Controller for Customized Smart Automation
Kulshreshtha, K., Bisht, S., Arora, J. S., Almusawi, M., Sharofiddin, U., & Kandpal, V. (2024) · International Conference on Communication, Computing and Energy Efficient Technologies (I3CEET) · doi:10.1109/i3ceet61722.2024.10993785
A Multi-Algorithm Feature Extraction for Accurate and Efficient Face Recognition
Darmawan, M. A., Pane, I. Z., Kusnadi, A., Tobing, F. A. T., Winantyo, R., & Pomalingo, S. (2024) · Asia Conference on Cognitive Engineering and Intelligent Interaction (CEII) · doi:10.1109/ceii65291.2024.00056
“GMM is subsequently applied to isolate the foreground object and extract texture data, functioning as a feature vector evaluator with high discriminatory power while utilizing a low-frequency energy band[13].”
AN ADVANCED REVIEW ON HUMAN ACTIVITY RECOGNITION USING ARTIFICIAL INTELLIGENCE
Reddy, A. S. (2025) · Journal of Science Engineering Technology and Management Sciences · doi:10.63590/jsetms.2025.v02.i07.pp517-525
Elkelany, A., Ross, R., & McKeever, S. (2026) · Journal of Ambient Intelligence and Smart Environments · doi:10.1177/18761364261470009
Whose Smartphone? Pairing Each Individual in the Panoramic Video with Their Respective Smartphone
Yano, K., Ito, K., & Yokoyama, S. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-93598-5_30
A Novel Attention-Based Parallel Blocks Deep Architecture for Human Action Recognition
Jadoon, Y. K., Khalid, Y. N., Khan, M. A., Shin, J., Alhayan, F., Cho, H., & Chang, B. (2025) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2025.066984
Inertial Signal Forecasting With Foundation Model Techniques
Wieland, C., & Pankratius, V. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3603456
“…is the process of identifying human actions and behavior using data collected from a variety of sensors, including vision sensors (e.g., cameras), environmental sensors (e.g., pressure or particulate matter sensors), wearable inertial sensors (e.g., accelerometers or gyroscopes), and others [1].”
Human Action Recognition – Analysis of Deep Learning Methods
Mol, P. M. A., & Mathew, T. E. (2025) · International Conference on Advancements in Power, Communication and Intelligent Systems (APCI) · doi:10.1109/apci65531.2025.11136916
“It has applications in security, healthcare, industry monitoring, and human-computer interactions [1], and the application in security and surveillance context is highly important because of its crucial objectives.”
Efficient Exercise Monitoring with Reduced Sensor Usage: A Machine Learning Approach
Roy, S., Molla, M. T. I., Hossain, F., Hasan, M. N., Barua, H., & Hasan, K. (2025) · IEEE International Conference on Quantum Photonics, Artificial Intelligence & Networking (QPAIN) · doi:10.1109/qpain66474.2025.11171701
Ray, L. S. S., Liu, M., Gurung, D., Zhou, B., Suh, S., & Lukowicz, P. (2025) · ACM International Symposium on Wearable Computers (ISWC) · doi:10.1145/3715071.3750424 · Cited by 1
Can LLMs Learn the Language of Inertial Sensors in Human Activity Recognition?
Bigelli, L., Bogliolo, A., Calisti, L., Capellacci, P., Contoli, C., Kania, N., & Lattanzi, E. (2025) · International Conference on Machine Learning Technologies (ICMLT) · doi:10.1109/icmlt65785.2025.11193221
“Human activity recognition (HAR) using inertial sensors has emerged as a key research area due to its broad applications in healthcare, fitness tracking, and smart environments [1], [2].”
Alohali, M. A., Alzahrani, M. Y., Alghamdi, A. M., & Yaseen, I. (2025) · Journal of the Chinese Institute of Engineers · doi:10.1080/02533839.2025.2560446
Lee, Y. L., Shin, H., Bang, M. S., Shin, S. H., Kim, E., Lee, E. S., Jo, S., Shin, H. I., & Lee, W. H. (2025) · Scientific Reports · doi:10.1038/s41598-025-23010-1
Mustafa, E. M., ElSayed, M., & Elreedy, D. (2025) · Novel Intelligent and Leading Emerging Sciences Conference (NILES) · doi:10.1109/niles68063.2025.11231895
“There are two primary sensor modalities: fixed laboratory sensors [6] offering high accuracy but limited practicality, and mobile sensors [7], that provide ubiquitous e data collection, generate more noise [8].”
Wang, Z., Jia, Y., Yang, C., & Zhang, X. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11229315
Karakish, M., ELsawaf, A., & Fouz, M. A. (2025) · Discover Applied Sciences · doi:10.1007/s42452-025-07637-3
GAN-HLT: Generative Hierarchical Light-Transformer for Extendable Human Activity Recognition
Fan, H., Zheng, C., Shu, L., Qian, K., Aliverti, A., & Wen, Q. (2025) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc58623.2025.11254502
“However, sensor-based HAR systems face challenges such as environmental changes, sensor drift, and movement variability, which can affect accuracy [5].”
Datasets for Human Activity Recognition: A Comparative Analysis
Mercado, L. F. B., Beltrán, J., & Castro, L. A. (2025) · Avances en Interacción Humano-Computadora · doi:10.47756/aihc.y10i1.198
“Several studies have compared public datasets to identify variations in acquisition protocols, capture environments, and sensor configurations [17].”
Robust Cross-Domain RF-Based Multimodal Activity Recognition With Few-Shot Adaptation
Zhang, J., Qin, Z., Mu, B., He, L., Wang, Z., & Chen, J. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3622118
“Existing research in this field can be divided into two categories in terms of data types [1], namely, sensor-based HAR [28], [29] and vision-based HAR [30].”
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Elgaier, A. M., & Abushaala, A. M. (2026) · Studies in computational intelligence · doi:10.1007/978-3-032-00232-7_4
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Li, J., Xu, M., Huang, Z., & Song, C. (2026) · Smart innovation, systems and technologies · doi:10.1007/978-3-032-10951-4_34
A Categorical Kalman Filter for Human Activity Recognition
de França, D. S., Neto, A. R. P., Cruz, L. A., & Macedo, J. A. F. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-15987-8_1
Continual fine-tuning with model soup for human activity recognition
Tanjung, S. Y., Yahya, B. N., & Lee, S. (2026) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-025-02918-0
Multinational sign language recognition framework for inclusive multimedia communication
Yüksel, A., & Rada, L. (2026) · PeerJ Computer Science · doi:10.7717/peerj-cs.3634
Patil, S. S., & Bombale, U. L. (2025) · IEEE International Conference for Women in Computing (InCoWoCo) · doi:10.1109/incowoco68239.2025.11407229
Hariharasudhan, T., Kalaivani, K., Santhosh, S., A, S. R. K., Madhan, M., & Naveenkumar, S. (2026) · International Conference on Mobile Computing and Sustainable Informatics (ICMCSI) · doi:10.1109/icmcsi67283.2026.11412954
Alghasham, N., & Albattah, W. (2026) · PLoS ONE · doi:10.1371/journal.pone.0343132
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Saha, U., Misra, S., & Das, D. (2025) · IEEE International Conference on Advanced Networks and Telecommunications Systems · doi:10.1109/ants66931.2025.11430001
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Zhang, B., Luo, X., Wu, C., & Zhao, Q. (2025) · doi:10.1145/3788149.3788221
“Important machine learning methods include Support Vector Machines (SVM) [15, 16], Decision Trees (DT), Random Forests (RF), and k-Nearest Neighbors (KNN) [17].”
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Yang, J., Liu, Y., Perera, M., Ji, P., Kim, D., Xu, M., Wang, T., Anwar, S., Gedeon, T., Wang, L., et al. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6465233
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Wu, J., & Chen, B.-Q. (2025) · IEEE International Conference on Computer and Communications (ICCC) · doi:10.1109/iccc68654.2025.11438161
“Furthermore, many existing models are highly sensitive to noise interference, they struggle to preserve long-term correlations in the presence of random noise, leading to a significant degradation in accuracy within real noisy environments [14]–[16].”
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“The model performance and bias protection are enhanced through data augmentation pre-processing methods that apply rotational modifications and scaling operations and adjustments in brightness levels [4].”
Ma, X., Yan-kan, Y., & Bian, K. (2026) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2026.3675889
Li, B., Zuo, K., Li, L., & Wu, Y. (2026) · Measurement Science and Technology · doi:10.1088/1361-6501/ae6469 · Cited by 2
“Applications ranging from healthcare monitoring and sports analytics to smart factory environments increasingly require embedded HAR systems capable of running under tight resource budgets while ensuring low latency and high reliability [3].”
Interpretable human activity recognition with multi-sensor knowledge fusion
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Mashukov, I., Chen, P., & Ding, W. (2026) · International Conference on Machine Learning and Autonomous Systems (ICMLAS) · doi:10.1109/icmlas67792.2026.11483903
“[24].”
A Deep Attention-BLSTM Pipeline for Human Action Recognition
Yozbakee, A. A., Ghozzi, Y., Hamdani, T. M., & Alimi, A. M. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-21585-7_3
Liu, J., Post, K., Kuchida, R., Zuniga, A., Sarhaddi, F., Flores, H., Nurmi, P., & Nguyen, N. T. (2026) · doi:10.1145/3774906.3802768
AI-Based Wearable Posture and Muscle Fatigue Monitor
Rajasekar, P., Nagul, J., Kumar, N., V, & Neeladhasan, M. (2026) · International Conference on Intelligent Computing and Control Systems (ICICCS) · doi:10.1109/iciccs67901.2026.11502774
Efficient Human Action Recognition Using MobileNetV1 and EfficientNetB3 Based Hybrid Network
Abdullah, M. I., Alkhayyat, A. H., Sudhamsu, G., Rani, P., Shankhyan, A., Chohan, J. S., Ramudu, M. J., & Singh, N. P. (2026) · National Academy Science Letters · doi:10.1007/s40009-026-02264-x
Maayouf, W. T., Al-Sawaff, Z. H., & Azzo, F. A. (2026) · Springer proceedings in earth and environmental sciences · doi:10.1007/978-3-032-18970-7_76
Shahriar, K. A., Ahmed, M., & Imtiaz, H. (2026) · Digital Discovery · doi:10.1039/d6dd00077k · Cited by 1
Transfer Learning Based Recognition of Human Activity Using Wearable Data
Bathala, N. R., & Sahukara, K. V. (2026) · Journal of Electrical Engineering and Technology · doi:10.1007/s42835-026-02812-x
“HAR is divided into two categories: sensor-based and vision-based [3].”
Human Action Recognition Based on Local Micro-Doppler Time-Frequency Map Sequences
Yang, X., & Pan, L. (2026) · Lecture notes in electrical engineering · doi:10.1007/978-981-92-1635-2_29
Federated Active User Participation (FAUP)
Akram, A., Farhan, A. A., & Basharat, A. (2026) · Evolving Systems · doi:10.1007/s12530-026-09850-w
A Novel Approach Based on Deep Learning for Violence Detection in Public Places
Khan, S. F., Shah, S. K., Khan, F. U., Khan, W., & Idrees, F. (2026) · International Journal of Innovations in Science and Technology · doi:10.33411/ijist/1773
Towards Smarter Activity Recognition: Leveraging Semantic Context in HAR Models
Pilatus, S., Strem, N., & Wichert, R. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-30038-6_7
ERARST: Enhanced Resident Activity Recognition in Smart Homes Using Transformer Architecture
Singhai, A., Pandey, S., Agrawal, D., & Upadhyay, K. K. (2026) · Procedia Computer Science · doi:10.1016/j.procs.2026.06.195
Metadata
- Authors
- Minh Dang, L., Min, K., Wang, H., Jalil Piran, M., Hee Lee, C., & Moon, H.
- Affiliations
- Sejong University (KR)
- Corresponding author
- Moon, Hyeonjoon
- Venue
- Pattern Recognition
- Publisher
- Elsevier BV
- Published
- 2020-12
- Volume
- 108
- Pages
- 107561
- ISSN
- 0031-3203 (ISSN-L)
- OpenAlex
- W3044326989
- Cited by
- 730
- References
- 262
- Citations by year
- 2026: 65 · 2025: 126 · 2024: 181 · 2023: 159 · 2022: 119 · 2021: 73 · 2020: 6
- FWCI
- 33.49
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Pattern Recognition (publishedVersion)
- Funders
- Institute for Information and Communications Technology Promotion
- Ministry of Education (2020R1A6A1A03038540)
- Ministry of Science, ICT and Future Planning (2019-0-00136)
- National Research Foundation of Korea
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Keywords
- human activity recognition, sensor-based activity recognition, embedded sensors, Internet of Things, medical applications, context-aware systems, data preprocessing, feature engineering
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