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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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“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
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“Several survey papers about vision based activity recognition have been published [3,4].”
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“The authors in [33] provided a comprehensive survey that discussed both sensor-based and vision-based human activity recognition.”
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“There are comprehensive surveys on traditional and deep learning-based human action and activity recognition methods in the literature [16,17].”
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BackgroundSelf-citationToward human activity recognition: a survey
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“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
“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
Mekruksavanich, S., & Jitpattanakul, A. (2021) · Electronics · doi:10.3390/electronics10141685 · Cited by 138
“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
A survey on deep learning-based image forgery detection
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Boosting Inertial-Based Human Activity Recognition With Transformers
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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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Driver Behavior Modeling Toward Autonomous Vehicles: Comprehensive Review
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“…exist various feature extraction techniques suitable to these visual feature classes, such as histogram-oriented gradients (HOG), shape matrices, tensor subspace analysis (TSA), invariant moments, scale-invariant feature transform (SIFT), speed-up robust feature (SURF), etc. [114], [153], [154].”
BackgroundA Comprehensive Survey on Deep Learning Methods in Human Activity Recognition
Kaseris, M., Kostavelis, I., & Malassiotis, S. (2024) · Machine Learning and Knowledge Extraction · doi:10.3390/make6020040 · Cited by 106
“This review complements the broader discussions by Ramamurthy et al. [17] and Dang et al. [18], by providing specific insights into video and image-based HAR, thereby enriching the understanding of the challenges and potential advancements in the field.”
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Vision-Based Human Pose Estimation via Deep Learning: A Survey
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“[23] provided a comprehensive survey on sensor- or vision-based human activity recognition.”
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“Liu et al. (B. Liu et al. 2019) In a related survey by Dang et al. (Minh Dang et al. 2020), the authors comprehensively presented both the sensor-based and vision-based HAR methods.”
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“In addition, the existing reviews mainly focused on recognition approaches [24], [31], [32], while paying little attention to computation and resource overhead, which can be an important consideration in designing HAR approaches for mobile devices.”
A survey on unsupervised learning for wearable sensor-based activity recognition
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“Hands are detected from both images using two-way detection: first, the skin tone pixels are extracted, and then the saliency map is applied for greater precision.”
MethodFederated Contrastive Learning With Feature-Based Distillation for Human Activity Recognition
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“H UMAN activity recognition (HAR) involves interpreting an individual’s gestures or movements in relation to both the person and their surrounding environment [1].”
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A review of vision-based indoor HAR: state-of-the-art, challenges, and future prospects
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“Previously, there have been some surveys [6, 24, 34, 84, 141] in the field of HAR which have been summarised below in Table 1.”
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The State-of-the-Art Sensing Techniques in Human Activity Recognition: A Survey
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“Dang et al. [31] 2020 Human activity recognition Analyzed sensor-and vision-based behavioral patterns to survey human activity recognition factors for creating context-aware apps for new domains such as Internet of Things (IoT) and healthcare.”
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“Dang et al. presented a survey on human activity recognition using data acquired using sensors including cameras ( 71 ).”
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“HAR can be seen as a typical time series classification problem, which can be tackled by traditional machine learning (ML) algorithms such as Random Forests, Support Vector Machine (SVM), and Naïve Bayes [5].”
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Cross-domain activity recognition via substructural optimal transport
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“, 2020) summed up vision-based HAR and (Wang et al., 2019; Dang et al., 2020) summarized recent work from a different point.”
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“Video classification Activity Recognition [14,56]”
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Yoo, M., Na, Y., Song, H., Kim, G., Yun, J., Kim, S., Moon, C.-J., & Jo, K. (2022) · Sensors · doi:10.3390/s22072513 · Cited by 58
“After that, each approach can be divided in detail according to data collection, pre-processing steps, and training methods, such as machine learning or deep learning [7].”
The impact of technology on sports – A prospective study
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MethodSelf-citationSport-Related Activity Recognition from Wearable Sensors Using Bidirectional GRU Network
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A Review of AIoT-Based Human Activity Recognition: From Application to Technique
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“Applying HAR in surveillance and security deters criminal activities and ensures public safety and property protection [2].”
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How Validation Methodology Influences Human Activity Recognition Mobile Systems
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MethodMultimodal vision-based human action recognition using deep learning: a review
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“When the related works done with this dataset are studied [14], the result which is obtained validates the DNN model for this dataset.”
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Smartphone-Based Human Activities Recognition System using Random Forest Algorithm
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“There are two main types of methods for HAR: 1) sensor based and 2) video based [6].”
MethodRecent Advances in Pedestrian Navigation Activity Recognition: A Review
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“All data collection devices have certain limitations [12].”
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Plastic-Optical-Fiber-Enabled Smart Glove for Machine-Learning-Based Gesture Recognition
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“It has stable acquisition data and simple signal processing, which overcomes the shortcomings of the above-mentioned visual recognition technology [24], [25], [26].”
Deep learning-based activity-aware 3D human motion trajectory prediction in construction
Heravi, M. Y., Jang, Y., Jeong, I., & Sarkar, S. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122423 · Cited by 44
BASAR:Black-box Attack on Skeletal Action Recognition
Diao, Y., Shao, T., Yang, Y., Zhou, K., & Wang, H. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00751 · Cited by 42
“Skeletal motion plays a vital role in human activity recognition as either an independent data source or a complement [33].”
BackgroundSelf-citationUsing Language Model to Bootstrap Human Activity Recognition Ambient Sensors Based in Smart Homes
Bouchabou, D., Nguyen, S. M., Lohr, C., Leduc, B., & Kanellos, I. (2021) · Electronics · doi:10.3390/electronics10202498 · Cited by 44
“To recognize ADLs based on sensor traces, researchers used various machine learning algorithms, as reviewed in Reference [13].”
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“…and artificial intelligence (AI) techniques to perform classification: at first, the signals acquired from sensors are processed to reduce noise [41], cope with missing values and remove possible artifacts [9,42]; secondly, data are segmented to identify the portion of the preprocessed signals…”
BackgroundCapsGaNet: Deep Neural Network Based on Capsule and GRU for Human Activity Recognition
Sun, X., Xu, H., Dong, Z., Shi, L., Liu, Q., Li, J., Li, T., Fan, S., & Wang, Y. (2022) · IEEE Systems Journal · doi:10.1109/jsyst.2022.3153503 · Cited by 38
“H UMAN activity recognition (HAR) is a kind of methods to classify and recognize the specific types of human actions and movement modes [1].”
MethodDog Behavior Recognition Based on Multimodal Data from a Camera and Wearable Device
Kim, J., & Moon, N. (2022) · Applied Sciences · doi:10.3390/app12063199 · Cited by 37
RepHAR: Decoupling Networks With Accuracy-Speed Tradeoff for Sensor-Based Human Activity Recognition
Teng, Q., Tang, Y., & Hu, G. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3240198 · Cited by 36
“C ONTINUOUS improvements in sensing technology and wearable devices are pushing the process of human activity recognition (HAR) [1], [2] based on inertial measurement units (IMUs) from concept to implementation in the smart health care field.”
BackgroundTransfer learning and its extensive appositeness in human activity recognition: A survey
Ray, A., & Kolekar, M. H. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122538 · Cited by 36
Local Domain Adaptation for Cross-Domain Activity Recognition
Zhao, J., Deng, F. Y., He, H., & Chen, J. (2020) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2020.3039196 · Cited by 36
“A considerable number of researchers have investigated sensor-based HAR [13].”
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Educational Innovation Faced with COVID-19: Deep Learning for Online Exam Cheating Detection
Yulita, I. N., Hariz, F. A., Suryana, I., & Prabuwono, A. S. (2023) · Education Sciences · doi:10.3390/educsci13020194 · Cited by 44
A Survey on Human Activity Recognition using Sensors and Deep Learning Methods
Banjarey, K., Sahu, S. P., & Dewangan, D. K. (2021) · International Conference on Computing Methodologies and Communication (ICCMC) · doi:10.1109/iccmc51019.2021.9418255 · Cited by 34
Yanan, P., Jilong, Y., & Heng, Z. (2021) · Sensors · doi:10.3390/s21196685 · Cited by 36
“According to the different types of data used for human action recognition, human action recognition is usually divided into the following three main categories: human action recognition based on vision, human action recognition based on acoustics, and human action recognition based on inertial sensors [13].”
BackgroundZheng, W., Yan, L., Gou, C., & Wang, F. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.10.009 · Cited by 36
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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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