Sensor-based and vision-based human activity recognition: A comprehensive survey
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The sliding window technique segments input sensor data into equal-size slices and classifies each slice as a certain class.
“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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A Survey on Wearable Technology: History, State-of-the-Art and Current Challenges
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LSTM Networks Using Smartphone Data for Sensor-Based Human Activity Recognition in Smart Homes
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“The HAR methods can be categorized into two categories according to data sources: visual-based and sensor-based [12].”
MethodA federated learning system with enhanced feature extraction for human activity recognition
Xiao, Z., Xu, X., Xing, H., Song, F., Wang, X., & Zhao, B. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107338 · Cited by 251 · see citing works
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“Several survey papers about vision based activity recognition have been published [3,4].”
BackgroundDeep Learning-Based Anomaly Detection in Video Surveillance: A Survey
Duong, H.-T., Le, V.-T., & Hoang, V. T. (2023) · Sensors · doi:10.3390/s23115024 · Cited by 178 · see citing works
“The authors in [33] provided a comprehensive survey that discussed both sensor-based and vision-based human activity recognition.”
BackgroundUllah, A., Muhammad, K., Ding, W., Palade, V., Haq, I. U., & Baik, S. W. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107102 · Cited by 169 · see citing works
“There are comprehensive surveys on traditional and deep learning-based human action and activity recognition methods in the literature [16,17].”
BackgroundA Deep Learning-Based Hybrid Framework for Object Detection and Recognition in Autonomous Driving
Li, Y.-F., Wang, H.-X., Dang, L., Nguyen, T. N., Han, D., Lee, A., Jang, I., & Moon, H. (2020) · IEEE Access · doi:10.1109/access.2020.3033289 · Cited by 128 · see citing works
“4 (a), there are 18 key points of human body parts, including nose, neck, right shoulder, left shoulder, right elbow, left elbow, right twist, left twist, right hip, left hip, right knee, left knee, right ankle, left ankle, right eye, left eye, right ear, and left ear [30].”
BackgroundSelf-citationToward human activity recognition: a survey
Saleem, G., Bajwa, U. I., & Raza, R. H. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07937-4 · Cited by 159 · see citing works
Human Activity Recognition: Review, Taxonomy and Open Challenges
Arshad, M. H., Bilal, M., & Gani, A. (2022) · Sensors · doi:10.3390/s22176463 · Cited by 162 · see citing works
“Currently, HAR has been utilized in diverse application domains including healthcare, surveillance, sports and event analysis, elderly care, and Human-Computer Interaction (HCI) [4].”
BackgroundMethodWearable Sensor-Based Human Activity Recognition in the Smart Healthcare System
Serpush, F., Menhaj, M. B., Masoumi, B., & Karasfi, B. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/1391906 · Cited by 174 · see citing works
“After the data acquisition tool collects its information, it must be sent to the HAR system using Bluetooth,Wi-Fi [20], etc.(en, after recognizing the activity, using these technologies, the information related to the recognized”
MethodInfluentialHuman Activity Recognition Based on Residual Network and BiLSTM
Li, Y., & Wang, L. (2022) · Sensors · doi:10.3390/s22020635 · Cited by 156 · see citing works
Mekruksavanich, S., & Jitpattanakul, A. (2021) · Electronics · doi:10.3390/electronics10141685 · Cited by 138 · see citing works
“In [1,8], a fundamental formulation for the HAR challenge was acquired, as well as an overview of previous strategies for solving it.”
Human activity recognition using wearable sensors by heterogeneous convolutional neural networks
Han, C., Zhang, L., Tang, Y., Huang, W., Min, F., & He, J. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.116764 · Cited by 130 · see citing works
A survey on deep learning-based image forgery detection
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Boosting Inertial-Based Human Activity Recognition With Transformers
Shavit, Y., & Klein, I. (2021) · IEEE Access · doi:10.1109/access.2021.3070646 · Cited by 116 · see citing works
Human Fall Detection in Surveillance Videos Using Fall Motion Vector Modeling
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“Also, various sensor [2–4] and vision-based methods for human activity recognition are presented in [5].”
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- 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 · 2012: 1
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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