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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]
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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
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Items 201–300 of 718
RFAR: A Real-Time Firefighter Activity Recognition System Using Wearable Accelerometer
Dao, T.-H., Tran, D.-N., Bui, V.-H., Nguyen, V. S., Hoa, D. K., Thanh, P. V., & Tran, D. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3593466 · Cited by 14
“HAR is commonly approached through two primary methods: computer vision [21] and wearable sensor signals [22].”
Beaulieu, A., Thullier, F., Bouchard, K., Maítre, J., & Gaboury, S. (2022) · Journal of Ambient Intelligence and Smart Environments · doi:10.3233/ais-210462 · Cited by 17
Mean Teacher-Based Cross-Domain Activity Recognition Using WiFi Signals
Xiao, C., Yue, L., Liu, C., & Wu, J. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3256324 · Cited by 14
“warping path that can detect identical shapes at different time steps [2], [3].”
BackgroundEOG-Based Reading Detection in the Wild Using Spectrograms and Nested Classification Approach
Baray, S. B., Ahmed, M. U., Chowdhury, M. E. H., & Kise, K. (2023) · IEEE Access · doi:10.1109/access.2023.3316032 · Cited by 14
“The movement of the eye can provide a lot of information in recognizing these activities where body movements are not involved that much [5].”
BackgroundFew-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
Optimal Sensor Placement and Multimodal Fusion for Human Activity Recognition in Agricultural Tasks
Benos, L. T., Tsaopoulos, D. E., Tagarakis, A. C., Katerıs, D., & Bochtis, D. (2024) · Applied Sciences · doi:10.3390/app14188520 · Cited by 17
Adams-based hierarchical features fusion network for image dehazing
Yin, S., Hu, S., Wang, Y. N., Wang, W., & Yang, Y. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.03.021 · Cited by 14
Abdel‐Basset, M., Chang, V. I. C., Hawash, H., Chakrabortty, R. K., & Ryan, M. J. (2021) · Annals of Operations Research · doi:10.1007/s10479-021-04164-3 · Cited by 14
“Dang et al. (2020)”
InfluentialTong, Y., & Yu, B. (2022) · Entropy · doi:10.3390/e24060845 · Cited by 15
“The integer hyper-parameters were initialized as num_units = 128, batch_size = 200, and we set the search interval of num_units and batch_size to [1, 256] and [1, 1000], respectively.”
BackgroundMethodAlpine Skiing Activity Recognition Using Smartphone’s IMUs
Azadi, B., Haslgrübler, M., Anzengruber-Tánase, B., Grünberger, S., & Ferscha, A. (2022) · Sensors · doi:10.3390/s22155922 · Cited by 14
Teshima, T., Niitsuma, M., & Nishimura, H. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123153 · Cited by 13
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
“Feature-based WHAR algorithms use traditional machine learning methods to classify activities based on hand-crafted features [6], [7].”
MethodPięta, P., Jegierski, H., Babiuch, P., Jegierski, M., Płaza, M., Łukawski, G., Deniziak, S. R., Jasiński, A., Opałka, J., Wȩgrzyn, P., et al. (2024) · IEEE Access · doi:10.1109/access.2024.3424930 · Cited by 15
“Inthe literature, the keywords human activity recognition and human action recognition (HAR) are also frequently used [48], [49], [50], [51], [52], [53].”
BackgroundYOLO-I3D: Optimizing Inflated 3D Models for Real-Time Human Activity Recognition
Luo, R., Anand, A., Zulkernine, F. H., & Rivest, F. (2024) · Journal of Imaging · doi:10.3390/jimaging10110269 · Cited by 13
A Hybrid Accuracy- and Energy-Aware Human Activity Recognition Model in IoT Environment
Jha, D. N., Chen, Z., Liu, S., Wu, M., Zhang, J., Morgan, G., Ranjan, R. K., & Li, X. (2022) · IEEE Transactions on Sustainable Computing · doi:10.1109/tsusc.2022.3209086 · Cited by 15
“On the other hand, Sensor-based approaches rely on the wireless sensors, e.g., accelerometer and gyroscope, and can easily capture different human activities without violating the users’ privacy [8], [9].”
BackgroundGaussian-guided feature alignment for unsupervised cross-subject adaptation
Zhang, K., Chen, J., Wang, J., Leng, Y., de Silva, C. W., & Fu, C. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108332 · Cited by 13
Human Activity Recognition: A Dynamic Inductive Bias Selection Perspective
Hamidi, M., & Osmani, A. (2021) · Sensors · doi:10.3390/s21217278 · Cited by 13
Data Science for Motion and Time Analysis with Modern Motion Sensor Data
Park, C., Noh, S. D., & Srivastava, A. (2022) · Operations Research · doi:10.1287/opre.2021.2216 · Cited by 12
Chen, D., Yongchareon, S., Lai, E. M.-K., Sheng, Q. Z., & Liesaputra, V. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3139330 · Cited by 12
Sign Language Recognition With Self-Learning Fusion Model
Vu, H. N., Hoang, T., Tran, C., & Pham, C. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3314728 · Cited by 13
Human activity recognition from uav videos using an optimized hybrid deep learning model
Sinha, K. P., & Kumar, P. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17289-3 · Cited by 12
Gu, M., Chen, Z., Chen, K. M., & Pan, H. (2023) · Signal Image and Video Processing · doi:10.1007/s11760-023-02894-4 · Cited by 12
Olugbade, T., Buono, R. A., Potapov, K., Bujorianu, A., Williams, A. C. D. C., García, S. D. O., Gold, N., Holloway, C., & Bianchi‐Berthouze, N. (2024) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2024.3390837 · Cited by 13
“Recognition of such composite activities remains a challenge even in the larger human activity recognition area [41], [48].”
BackgroundInfluentialTian, Y., Hettiarachchi, D., Yu, H. S., & Kamijo, S. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3404777 · Cited by 13
“For the basic application scenarios, there are about six groups of human activities that are often paid close attention to [13]: standing, walking, up and down stairs, cycling, running, and sitting.”
BackgroundYu, J., Zhang, L., Cheng, D., Bu, C., Wu, H., & Song, A. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3412736 · Cited by 13
“This, in turn, enables the provision of assistive services in a wide range of application scenarios, including sports tracking, elderly care, smart homes, healthcare, and human–computer interaction (HCI) [2], [3].”
BackgroundIntegrating human perception in 3D city models and urban digital twins
Lei, B., Liang, X., & Biljecki, F. (2024) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-x-4-w5-2024-211-2024 · Cited by 12
“As the concept of humans as sensors is explored across numerous domains (Jayathissa et al., 2020, Liu et al., 2015, Dang et al., 2020, Wang et al., 2020), it becomes evident that integrating human sensing data has the potential to represent socio-economic activities in the city.”
BackgroundAdvancements and Challenges in Hand Gesture Recognition: A Comprehensive Review
Murad, B. K., & Alasadi, A. H. H. (2024) · Iraqi Journal for Electrical And Electronic Engineering · doi:10.37917/ijeee.20.2.13 · Cited by 15
“Vision-based approaches use cameras or more to obtain human motion, allowing devices to interpret gesture properties like color and texture [17,18].”
HPE-Li: WiFi-Enabled Lightweight Dual Selective Kernel Convolution for Human Pose Estimation
Gian, T., Lai, T. D., Luong, T. V., Wong, K., & Nguyen, V. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72751-1_6 · Cited by 12
“Human activity monitoring-enabled methods primarily rely on video-based sensors and wearable devices [27], showcasing high accuracy in activity identification but facing several obstacles in the practical deployment [2].”
TinyML-Based Fall Detection for Connected Personal Mobility Vehicles
Sanchez‐Iborra, R., Bernal-Escobedo, L., Santa, J., & Skármeta, A. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.022610 · Cited by 12
“Other works in this field focused on video and image processing can be found in the survey work presented in [14].”
BackgroundExploring Eye Movement Biometrics in Real-World Activities: A Case Study of Wayfinding
Liao, H., Zhao, W., Zhang, C., & Dong, W. (2022) · Sensors · doi:10.3390/s22082949 · Cited by 12
“Furthermore, in real-world applications, MWEMB can be implicitly performed and easily combined with other gaze-based techniques, such as human activity recognition [8] and attentive user interfaces [9], to provide a more personalized and tailored experience in human–computer interactions (HCIs) [10].”
BackgroundErfianto, B., Rizal, A., & Hadiyoso, S. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20053879 · Cited by 12
“In recent research, much work has been conducted regarding introducing human activity [1].”
BackgroundInfluentialMAG-Res2Net: a novel deep learning network for human activity recognition
Liu, H., Zhao, B., Dai, C., Sun, B., Li, A., & Wang, Z. (2023) · Physiological Measurement · doi:10.1088/1361-6579/ad0ab8 · Cited by 12
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
“On the other hand, wearable sensor approaches allow continuous sensing during daily activities without spatiotemporal limitations, making it an attractive alternative [5].”
BackgroundDas, J. K., Chakrabarty, A., & Piran, M. J. (2021) · Expert Systems · doi:10.1111/exsy.12804 · Cited by 12
A Review on Vision-based Hand Gesture Recognition Targeting RGB-Depth Sensors
Rawat, P. S., Kane, L., Goswami, M., Jindal, A., & Sehgal, S. (2022) · International Journal of Information Technology & Decision Making · doi:10.1142/s0219622022300026 · Cited by 12
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 13
“HAR is typically formulated as a time series classification problem, where traditional machine learning (ML) algorithms, including random forest, support vector machines (SVMs), and Naïve Bayes, are frequently employed [5].”
Energy–Accuracy Aware Finger Gesture Recognition for Wearable IoT Devices
Jung, W., & Lee, H. G. (2022) · Sensors · doi:10.3390/s22134801 · Cited by 11
“However, it is also known that the performance of vision-based gesture recognition is highly dependent on camera setup such as the angle to the object, the size of the image and the intensity of illumination [9].”
BackgroundAction recognition in compressed domains: A survey
Ming, Y., Zhou, J., Hu, N., Feng, F., Zhao, P., Lyu, B., & Yu, H. L. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127389 · Cited by 14
Zou, H., Chen, Z., Zhang, J., Wang, L., Zhang, F., Li, J., & Pan, Y. (2024) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2024.3378331 · Cited by 11
Smart-watch-based construction worker activity recognition with hand-held power tools
Altheimer, J., & Schneider, J. (2024) · Automation in Construction · doi:10.1016/j.autcon.2024.105684 · Cited by 14
Action assessment in rehabilitation: Leveraging machine learning and vision-based analysis
Kryeem, A., Boutboul, N., Bear, I., Raz, S., Eluz, D., Itah, D., Hel-Or, H. Z., & Shimshoni, I. (2024) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2024.104228 · Cited by 12
Patel, A. D., Jhaveri, R. H., Patel, A. D., Shah, K. A., & Shah, J. H. (2024) · Software Practice and Experience · doi:10.1002/spe.3394 · Cited by 11
“HAR enables computing devices to identify human actions through the analysis of the data, which is referred to as learning from data recorded from various sensors [2, 3].”
Graph-Based Methods for Multimodal Indoor Activity Recognition: A Comprehensive Survey
Javadi, S., Riboni, D., Borzì, L., & Zolfaghari, S. (2025) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2024.3523240 · Cited by 12
“While some papers, including [32] and [33], provide a broad overview of the different approaches, others focus on spe-cific methodologies.”
Zhou, Y., Wang, Y., Wang, R., Zhang, W., & Yang, H. (2025) · Structures · doi:10.1016/j.istruc.2025.108850 · Cited by 11
Towards an end-to-end isolated and continuous deep gesture recognition process
Mahmoud, R., Belgacem, S., & Omri, M. N. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07165-w · Cited by 11
Zhou, B., Feng, N., Wang, H., Lu, Y., Wei, C., Jiang, D., & Li, Z. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac89b4 · Cited by 11
Self-citationLi, J., Meng, J., Gong, H., & Fan, Z. (2022) · IEEE Access · doi:10.1109/access.2022.3212064 · Cited by 11
“Vision-based gesture recognition mainly captures human gesture sequences through video-capturing devices [7], [8].”
BackgroundMuhander, B. A., Wiese, J., RANA, O. F., & Perera, C. (2023) · ACM Transactions on Internet of Things · doi:10.1145/3600096 · Cited by 11
“These sensors usually share the collection of common data types that enable them to perform multiple tasks [43, 109].”
BackgroundEnhancing 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
“Vision-based HAR utilizes video sensor technologies, such as RGB cameras, to monitor and recognize the actions of the subject, while sensor-based recognition is based on body-worn sensors, such as those found in bands, smartwatches, clothes, and smartphones [4].”
MethodMeng, J. C. S., Yang, L. X., & Hao, L. (2023) · Buildings · doi:10.3390/buildings13092144 · Cited by 12
Phase Randomization: A data augmentation for domain adaptation in human action recognition
Mitsuzumi, Y., Irie, G., Kimura, A., & Nakazawa, A. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.110051 · Cited by 11
Human Activity Recognition Based on Deep Learning Regardless of Sensor Orientation
He, Z., Sun, Y., & Zhang, Z. (2024) · Applied Sciences · doi:10.3390/app14093637 · Cited by 14
GPFS: A Graph-based Human Pose Forecasting System for Smart Home with Online Learning
Li, X., & Li, D. (2021) · ACM Transactions on Sensor Networks · doi:10.1145/3460199 · Cited by 11
Intelligent Deep Learning Enabled Human Activity Recognition for Improved Medical Services
Dhiravidachelvi, E., Kumar, M. S., Anand, L. D. V., Pritima, D., Kadry, S., Kang, B.-G., & Nam, Y. (2022) · Computer Systems Science and Engineering · doi:10.32604/csse.2023.024612 · Cited by 11
Wearable-Gait-Analysis-Based Activity Recognition: A Review
Ansah, S., & Chen, D. (2022) · International Journal on Smart Sensing and Intelligent Systems · doi:10.2478/ijssis-2022-0021 · Cited by 12
Exploiting Security Issues in Human Activity Recognition Systems (HARSs)
Sakka, S., Liagkou, V., & Stylios, C. D. (2023) · Information · doi:10.3390/info14060315 · Cited by 12
A Yolo-based Violence Detection Method in IoT Surveillance Systems
Gao, H. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.0140817 · Cited by 10
“These systems can process and analyze real-time data from sensors and cameras, enabling quick and efficient identification of violent incidents [6].”
BackgroundMethodWang, J., Cui, W., Zhu, T., Ning, H., & Liu, Z. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3390755 · Cited by 11
“This accessibility has sparked significant interest in human activity recognition (HAR) based on wearable devices, propelling the rapid advancement of pervasive computing [1], [2].”
BackgroundA Systematic Literature Review on Human Activity Recognition
Ravuri, A. (2024) · Journal of Electrical Systems · doi:10.52783/jes.2848 · Cited by 10
“Studies on vision-based systems can be categorized according to the type of data utilized, which comprises RGB [12,107,108] as well as RGB-D data [9,25].”
BackgroundWang, P., Huang, H., Zhao, L., Zhu, B., Huang, H., & Wu, H. (2024) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2024.3435881 · Cited by 10
“1 shows, by analyzing patterns in WiFi data, consumer-electronic WiFi-based HAR systems can differentiate between different activities, such as walking, running, cycling, sitting, and standing, or more complex activities, like sign language [2].”
Leveraging Large Language Models for Activity Recognition in Smart Environments
Cleland, I., Nugent, L., Cruciani, F., & Nugent, C. (2024) · International Conference on Activity and Behavior Computing (ABC) · doi:10.1109/abc61795.2024.10651718 · Cited by 12
“Video-based systems and Sensor-based systems [6].”
BackgroundYu, J., Zhang, L., Cheng, D., Huang, W., Wu, H., & Song, A. (2024) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2024.3457830 · Cited by 11
“As previously summarized in several survey literatures [1], [2], [3], [4], according to the deployment type of sensors, HAR could be roughly classified into two main aspects: ambient sensor-based HAR (e.g., images or videos obtained from camera, WIFI signals) and wearable sensor-based HAR.”
BackgroundEnhanced human lower-limb motion recognition using flexible sensor array and relative position image
Lian, C., Li, W. J., Kang, Y., Li, W. J., Zhou, D., Zhan, Z., Chen, M., Suo, J., & Zhao, Y. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.112142 · Cited by 11
Vision Based Human Activity Recognition: A Comprehensive Review of Methods & Techniques
Girdhar, E. A. P. (2021) · Türk bilgisayar ve matematik eğitimi dergisi · doi:10.17762/turcomat.v12i10.5631 · Cited by 11
“It gave a remarkable result in the area of human activity recognition applications (Dang, Min, Wang, Piran, Lee & Moon, 2020).”
BackgroundHu, Z., Zhang, Y., Yu, T., & Pan, S. (2022) · ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) · doi:10.1109/ipsn54338.2022.00028 · Cited by 10
Multivariate CNN Model for Human Locomotion Activity Recognition with a Wearable Exoskeleton Robot
Son, C.-S., & Kang, W. (2023) · Bioengineering · doi:10.3390/bioengineering10091082 · Cited by 10
“Deep learning (DL) technology has risen in popularity as a tool to autonomously detect users’ locomotion activities or intents in the field of human activity recognition (HAR) [18,23,24].”
BackgroundEfficient Human Gait Activity Recognition Based on Sensor Fusion and Intelligent Stacking Framework
Tarekegn, A. N., Sajjad, M., Cheikh, F. A., Ullah, M., & Muhammad, K. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3319353 · Cited by 12
“In vision-based HGAR, images or video frames are used to monitor and recognize the actions of the subject, whereas sensor-based recognition is used to analyze and process the data collected by sensors such as accelerometers and gyroscopes [4].”
MethodYang, G., Zhang, L., Bu, C., Wang, S., Wu, H., & Song, A. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3321639 · Cited by 11
“…humanactivitiesthroughsuchsmartsensingdevicesplacedat differentbodypositions,whichhasattractedgreatinterestin pervasivecomputingscenarioandmotivatedalargevarietyof context-awareapplications(e.g.,human-computerinteraction, sportsmonitoring,healthcare,andgaitanalysis)[3].”
Anomaly Detection From Low-Dimensional Latent Manifolds With Home Environmental Sensors
Melgarejo-Meseguer, F.-M., Bleda, A. L., Abbenante, S. E., Gimeno-Blanes, F.-J., Everss, E., Muñoz-Romero, S., Rojo‐Álvarez, J. L., & Maestre, R. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3338697 · Cited by 10
“Recent reviews on DL techniques for HAR with sensors suggest that efficient methods are emerging [9], [10].”
BackgroundAlternative Deep Learning Architectures for Feature-Level Fusion in Human Activity Recognition
Maítre, J., Bouchard, K., & Gaboury, S. (2021) · Mobile Networks and Applications · doi:10.1007/s11036-021-01741-5 · Cited by 10
On-Device Lumbar-Pelvic Movement Detection Using Dual-IMU: A DNN-Based Approach
Zhang, Y., Haghighi, P. D., Burstein, F., Yao, L., & Cicuttini, F. (2021) · IEEE Access · doi:10.1109/access.2021.3074755 · Cited by 10
“The two most widely used DNN models are CNN (convolutional neural network) and RNN (recurrent neural network) [22].”
MethodAutomatic Scaffolding Workface Assessment for Activity Analysis through Machine Learning
Ying, W., Shou, W., Wang, J., Shi, W., Sun, Y., Ji, D., Gai, H., Wang, X., & Chen, M. (2021) · Applied Sciences · doi:10.3390/app11094143 · Cited by 10
Multi-Framework Evidential Association Rule Fusion for Wearable Human Activity Recognition
Geng, X., Chen, X., Ma, Z., & Song, L. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3363908 · Cited by 9
“…area [9] and the environmental sensor-based techniques monitoring the surroundings by the installation of sensor devices in home [10], the wearable sensor-based techniques enable automatically detecting and tracking some daily activities that are suitable for both outdoor and indoor scenarios [11].”
BackgroundView-agnostic Human Exercise Cataloging with Single MmWave Radar
Liu, A., Lin, Y.-T., & Sundaresan, K. (2024) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3678512 · Cited by 9
Improving Human Activity Recognition in Smart Healthcare with Ensemble Deep Learning
Soni, V., Yadav, H., Semwal, V. B., & Tripathi, S. (2024) · IETE Journal of Research · doi:10.1080/03772063.2024.2434579 · Cited by 9
“Although VB-HAR concentrates on human motion in clips and picture format captured from a camera [5].”
Arrotta, L., Civitarese, G., Chen, X., Cumin, J., & Bettini, C. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.126178 · Cited by 9
A Comprehensive Review on Group Re-identification in Surveillance Videos
Nayak, K., & Dogra, D. P. (2025) · ACM Computing Surveys · doi:10.1145/3711126 · Cited by 11
“The literature on crowd analysis contains surveys on person re-identification [ 61 , 76 , 78 , 97 , 114 ], single-person activity recognition [ 39 , 57 , 77 , 82 ], and group activity recognition [ 30 , 65 , 104 ].”
Sajid, M., Khan, A. H., Malik, K. R., Khan, J. A., & Alwadain, A. S. (2025) · PeerJ Computer Science · doi:10.7717/peerj-cs.2944 · Cited by 9
“The retrieved frames serve as input for feature extraction, which entails deriving signi fi cant properties to detect anomalies ( Dang et al., 2020 ).”
Machine Learning Algorithms and Sensor Technologies for Aging-In-Place Applications: A Review
Habibovic, I., Almisreb, A. A., Hodžić, M. I., Turaev, S., & Cantelli‐Forti, A. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3591931 · Cited by 9
“…the sensor data types described in this section, whether from wearable sensors or ambient sensors, the table offers insights into selecting ML models that align with the [14], [48], [82], [83], [84], [85], [86] specific needs of sensor-driven systems for elderly care (e.g. data type, ease of use).”
Deep Learning for Human Activity Recognition
(2021) · Communications in computer and information science · doi:10.1007/978-981-16-0575-8 · Cited by 10
Skeleton-Based Human Action Recognition on Large-Scale Datasets
Hossain, T., Sarker, S. C., Rahman, S., & Ahad, M. A. R. (2021) · Intelligent systems reference library · doi:10.1007/978-3-030-75490-7_5 · Cited by 9
Human Activity Recognition Using Time Series Pattern Recognition Model-Based on Tsfresh Features
Luqian, S., & Yuyuan, Z. (2021) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc51323.2021.9498859 · Cited by 9
Artificial intelligence technologies for more flexible recommendation in uniforms
Wen, C.-H., Cheng, C.-C., & Shih, Y.-C. (2022) · Data Technologies and Applications · doi:10.1108/dta-09-2021-0230 · Cited by 9
Caramaschi, S., Papini, G. B., & Caiani, E. G. (2023) · Applied Sciences · doi:10.3390/app13074175 · Cited by 9
Development of YOLO-based Model for Fall Detection in IoT Smart Home Applications
Gao, P. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.01410117 · Cited by 9
“By observing their posture, it becomes possible to monitor the health of elders, and if high-risk postures such as falling are detected, timely warnings can be sent [4], [5].”
BackgroundHuman Activity Recognition in a Free-Living Environment Using an Ear-Worn Motion Sensor
Boborzi, L., Decker, J., Rezaei, R., Schniepp, R., & Wuehr, M. (2024) · Sensors · doi:10.3390/s24092665 · Cited by 9
Patient Identification based on Physical Rehabilitation Movements using Skeleton Data
Kim, J.-K., Lee, K. B., Kim, J., & Hong, S. G. (2021) · International Conference on Information and Communication Technology Convergence (ICTC) · doi:10.1109/ictc52510.2021.9621049 · Cited by 9
Madokoro, H., Nix, S., Woo, H., & Sato, K. (2021) · Applied Sciences · doi:10.3390/app112411807 · Cited by 11
Segmentation and Recognition Model for Complex Action Sequences
Geng, H., Huan, Z., Liang, J., Hou, Z., Lv, S., & Wang, Y. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3144157 · Cited by 9
Human Activity Recognition: A review
Pereira, J. G., & Gonçalves, J. J. (2022) · International Symposium on Digital Forensics and Security (ISDFS) · doi:10.1109/isdfs55398.2022.9800781 · Cited by 9
Tian, Y., Zhang, J., Chen, Q., & Liu, Z. (2022) · IEEE Access · doi:10.1109/access.2022.3225652 · Cited by 9
“According to the sensing technologies used, the HAR approaches can be divided into two categories: 1) computer vision-based HAR and 2) Inertial sensor-based HAR [8].”
MethodNoorani, S. H., Raheel, A., Khan, S., Arsalan, A., & Ehatisham-ul-Haq, M. (2023) · International Conference on Communication, Computing and Digital Systems (C-CODE) · doi:10.1109/c-code58145.2023.10139909 · Cited by 9
“Whereas, on the other hand, the non-visual approach to activity recognition relies mostly on various wearable sensors [4].”
BackgroundHuang, Q., Xie, W., Li, C., Wang, Y., & Liu, Y. (2023) · Applied Sciences · doi:10.3390/app131910560 · Cited by 8
Anomaly Detection in Smart Homes Based on Kitchen Activities and Machine Learning
Alnaqbi, S., & Tawfik, H. (2023) · International Conference on Developments in eSystems Engineering (DeSE) · doi:10.1109/dese60595.2023.10469231 · Cited by 8
“Both vision-based and sensor-based sensor types are diverse [6].”
BackgroundAn optimization method of human skeleton keyframes selection for action recognition
Chen, H., Pan, Y., & Wang, C. (2024) · Complex & Intelligent Systems · doi:10.1007/s40747-024-01403-5 · Cited by 8
“In recent years, this direction has gradually become a research hotspot in the field of computer vision [1].”
Overview of Human Activity Recognition Using Sensor Data
Hamad, R. A., Woo, W. L., Wei, B. W., & Yang, L. (2024) · Advances in intelligent systems and computing · doi:10.1007/978-3-031-55568-8_32 · Cited by 9
Ruan, G., Xia, L., Wen, X., & Dong, Y. (2024) · Journal of the Knowledge Economy · doi:10.1007/s13132-024-02021-9 · Cited by 8
RetractedA hybrid TCN-GRU model for classifying human activities using smartphone inertial signals
Sekaran, S. R., Pang, Y. H., You, L. Z., & Yin, O. S. (2024) · PLoS ONE · doi:10.1371/journal.pone.0304655 · Cited by 8
“Handcrafted feature-based methods require feature extraction, ranking, and selection processes, which are tedious and time-consuming [25, 26]. iii.”
MethodCDFL: Efficient Federated Human Activity Recognition Using Contrastive Learning and Deep Clustering
Khazaei, E., Esmaeilzehi, A., Taha, B., & Hatzinakos, D. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3464416 · Cited by 9
Yao, M., Cheng, D., Zhang, L., Liu, L., Song, S. J., Wu, H., & Song, A. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.112444 · Cited by 8
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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