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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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InfluentialHuman Activity Recognition via a Categorical Kalman Filter Using Inertial Sensors
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“Data sources commonly include video recordings and inertial measurement units (IMUs) [4].”
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“The semantic feature representations can be summarized in three main methods [8]: pose estimation, appearance-based approach, and 3D approaches.”
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“According to the used underlying sensing units, existing activity recognition methods can be broadly categorized into three groups: vision, wearable-, and ambient sensor-based methods [7,11].”
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“The camera gathers static exercises, which are utilized for training the system for recognition; just a significant dataset is required to accomplish this (Dang et al., 2020).”
BackgroundRetractedSiow, C. Z., Chin, W. H., Zhang, Y., & Kubota, N. (2023) · International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc58545.2023.10327934 · Cited by 1
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“This foundational concept garnered extensive acknowledgment, mostly attributed to the influential work of (Minh Dang et al., 2020).”
BackgroundComputational Intelligence in Smart Cities and Smart Energy Systems
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“Illustrative instances of group activities consist of group study sessions, football matches, volleyball matches and presentations[3].”
BackgroundKnowledge-Driven Approach for Quality Assessment of HAR Data Sets: An Automated Tool
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“Large and diverse HAR data sets have facilitated the development of activity recognition algorithms and models [2].”
BackgroundEnhanced Pet Behavior Prediction via S2GAN-Based Heterogeneous Data Synthesis
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“There are two types of HAR: vision-based and sensor-based [6].”
BackgroundVision Based Fall Detection Model Using Raspberry Pi
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“Several works in scientific literature study human error detection and activity recognition, often treating these tasks separately [6],[7].”
BackgroundAlashti, M. R. S., Abadi, M. H. B., Holthaus, P., Menon, C., & Amirabdollahian, F. (2024) · IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob) · doi:10.1109/biorob60516.2024.10719939 · Cited by 1
“The development of large datasets, multimodal sensor fusion techniques, and deep learning architectures has contributed to the progress and effectiveness of vision-based HAR systems [3], [2].”
BackgroundXAttentionHAR Ensemble: Leveraging Cross-Modal Attention for Enhanced Activity Recognition
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“In order to bring MoCa analysis into three-dimensional space, two cameras, or a single stereoscopic camera, may be implemented in combination with three-dimensional markers.”
Multimodel Lightweight Transformer Framework for Human Activity Recognition
Wen, Q., Lin, C., & Qian, K. (2024) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc53108.2024.10781743 · Cited by 1
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“In such cases, deep-learning models, particularly convolutional neural networks (CNNs), have proven reliable and robust for recognizing and classifying human actions (Dang et al., 2020).”
Enhancing Computational Model for Human Motion Recognition Using Deep Learning
Yadav, R. K., Daniel, A., Kumar, B., Sakhare, A. V., Moriwal, R., & Pawar, R. (2024) · IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies (INSPECT) · doi:10.1109/inspect63485.2024.10896050 · Cited by 2
Enhancing Human Activity Recognition in Smart Surveillance Using Transfer Learning
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“The sampling frequency 12.5 Hz has been previously demonstrated to be sufficient for human activity recognition applications [10].”
InfluentialGrasOpen: Biometric Authentication via Reach-and-Grasp for Smart Door Access Using Smartwatch
Ding, L., Shi, J., Wu, Y. L., Hu, X., Bi, H., & Chen, Y. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3557392 · Cited by 1
“Specifically, the IMU captures vibration changes caused by muscle movements during activities at a sampling rate of 100 Hz [36].”
End‐to‐end reading classification in the wild using 2D EOG signal images and ResNet variants
Jeong, C. Y., Song, Y., Wang, S., Kim, M., & Choi, S.-G. (2025) · ETRI Journal · doi:10.4218/etrij.2025-0051 · Cited by 1
“The analysis of eye movements offers valuable insights into understanding cognitive activities [ 2].”
Machine-Generated Hierarchical Structure of Human Activities to Reveal How Machines Think
Altın, M., Gürsoy, F., & Xu, L. (2021) · IEEE Access · doi:10.1109/access.2021.3053084 · Cited by 5
“We refer interested readers to the recent comprehensive survey paper by Dang et al. [8] that provides a relatively concise review of hundreds of relevant studies among which exist more than 10 other HAR survey papers.”
S-THAD: a framework for sensor-based temporal human activity detection from continuous data streams
Ehatisham-ul-Haq, M., Azam, M. A., Murtaza, F., Amin, Y., & Fu, Q. (2021) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-02931-5 · Cited by 1
Multivariate Beta-Based Hidden Markov Models Applied to Human Activity Recognition
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Office Activity Recognition Using HTN Planning
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Ftmar: A Fusion Transformer Network for Multi-Resident Activity RecognitionPreprint
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“Bulling et al. [19] compiled a seminal tutorial paper that explicitly captures the classical, pre-Deep-Learning era of HAR with wearable sensors.”
Improving state estimation through projection post-processing for activity recognition in football.Preprint
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Accelerometry for Human Activity Recognition: an Overview
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Image Recognition Applied to Security Systems: The Case of Burkina Faso
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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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