The DOI matches its Crossref record; 260 of the 262 references it cites were found in a registry or an index.
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
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Balam, V. P., & Sah, S. M. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-7488-8.ch013
Time-Series Imaging for Air-Writing Chinese Character Recognition
Yang, R., & Yang, D. (2026) · Communications in computer and information science · doi:10.1007/978-981-92-3560-5_18
XAI for Transformer-based Fall Detection using Skeleton Time-series Data Analysis
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“Vision-based systems have become dominant [2] as they exploit algorithms that capture both spatial postures and temporal dynamics [3].”
Islam, A., Rahman, A., Salgado, A. A. V., Scharf, R. J., Blemker, S. S., & Barnes, L. E. (2026) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi69079.2026.00045
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“Wear-able sensors are designed to be worn on the body of a person or integrated into wearable devices such as smartwatches, smartphones, smart clothing, or other accessories (Dang et al. 2020).”
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“Camera-based systems provide rich visual information but raise serious privacy concerns in domestic and clinical environments, and their performance degrades under poor lighting or occlusion [4].”
길인호, Ha, S., Oh, J., Lee, D., Hwang, S., No, J. K., & Kim, W. (2026) · Sensors · doi:10.3390/s26175678
“Human Activity Recognition (HAR) refers to the automatic identification of human activities from sensor signals, including the classification, segmentation, and detection of simple and complex human behaviors [1].”
An Enhanced Sustainable Human Activity Recognition Using Convolutional Neural Networks
Geetha, C., Anilkumar, P., Dhanasekhar, D., Chandana, S., Padma, M., & Saraswathi, K. (2026) · International Conference on Computing, Intelligence, and Application (CIACON) · doi:10.1109/ciacon70148.2026.11689054
“In 2020, Dang et al. [15] performed a thorough assessment of human activity recognition (HAR) utilizing both sensor-based and vision-based techniques.”
Machine Learning‐Assisted Soft Sensors: From Material Design to Motion Recognition
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Only Speaking FACTs: Foundation Models for Activity-Centric Timeline Description
Lee, D. Y., Patil, T., Pan, S., & Weakley, A. M. (2026) · International Workshop on Human-Centered Sensing, Modeling, and Intelligent Systems · doi:10.1145/3842436.3843810
Parvizi, A., Jamshidi, K., & Shahbazi, H. (2026) · Multimedia Tools and Applications · doi:10.1007/s11042-026-21947-7
Explainable Multimodal CNN vs ViT for Human Posture Recognition: Fusing RGB and Depth Imagery
Goyal, N., Goraya, M. S., & Singh, T. (2026) · IEEE World Conference on Applied Intelligence and Computing (AIC) · doi:10.1109/aic70896.2026.11717378
Gorce, P., & Jacquier-Bret, J. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1933096
“…provide a comprehensive representation of posture and perform particularly well when acquisition conditions are controlled for posture recognition (Dang et al., 2020), whereas wear-ables remain advantageous due to their robustness to environmental conditions and their ability to provide…”
Recent innovation in vision-based human activity recognition: a systematic survey
Yadav, D., Rani, D., Verma, O. P., & Sharma, T. K. (2026) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2026.105004
Lee, J., & Yoo, M. (2026) · Buildings · doi:10.3390/buildings16204111
AI-Orchestrated Cloud–Edge Multi-Agent Framework for Emergency Vital Monitoring
Dubey, R., Dash, S., Khan, A. U., & Sahoo, P. (2026) · International Conference on Intelligent Computing and Sustainable Innovations in Technology (IC-SIT) · doi:10.1109/ic-sit69723.2026.11735924
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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