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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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“As a result, contactless sensors are gaining ∗ Author to whom any correspondence should be addressed. popularity for indoor human activity identification [5, 6].”
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“It has received considerable attention due to its real-world applications in surveillance [1], traffic monitoring [2], robotics [3], activity recognition [4], video analysis for understanding human motion [5], medical diagnosis systems [6,7], and unmanned vehicles [8].”
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Applications and Challenges of Radar-Based Human Activity Recognition
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Are Transformers a Useful Tool for Tiny devices in Human Activity Recognition?
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“Scientific literature of the past decade agrees that modern deep classifiers, such as Multilayer Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs), represent the best-performing strategies in terms of recognition capability [7].”
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“W ITH rapid developments [1], [2], Human Activity Recognition (HAR) gains significant interest in smart homes [3], [4], digital healthcare [5], [6], and human-computer interaction [7], [8].”
A Health Perspective Smartphone Application for the Safety of Road Accident Victims
Parasana, K., Sahoo, G. K., Das, S. K., & Singh, P. (2021) · Advanced Communication Technologies and Signal Processing (ACTS) · doi:10.1109/acts53447.2021.9708124 · Cited by 7
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“These traditional approaches rely on either expert-crafted features [8], [27], [28] or some self-learned shallow features to train the classifier.”
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“2) Computer Vision-Based Approaches: Another common approach to human activity recognition involves the use of computer vision techniques [13], [14], where cameras are used to capture and analyze human movements.”
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“Since Zeng et al. [5] and Ronao et al. [6] at the earliest time employed CNN to handle multichannel time series sensor data, a myriad of convolution-based networks have emerged as a promising solution for activity recognition, with each building upon its predecessors [4].”
Chaubey, V. P., Sharma, S., Kumar, A., Malik, A., Malik, P. K., & Batra, I. (2023) · International Conference on Advancement in Electronics & Communication Engineering (AECE) · doi:10.1109/aece59614.2023.10428497 · Cited by 6
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“R ELATED W ORKS Due to obvious advantages, such as automatic feature learning and strong generalization capability, deep convolutional neural networks (CNNs) have been widely employed in the field of HAR, achieving outstanding success [1], [2].”
BackgroundMethodFan, J., Liu, Z., Du, H., Kang, J., Niyato, D. T., & Lam, K. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3501361 · Cited by 7
“…of such an extensive range of sensors and actuators, applications of HAR span from home monitoring and surveillance systems, to video-based information retrieval, behavior analysis, gesture recognition, ambient assisted living (AAL), and extend to healthcare and other sensor systems [3], [4], [5].”
A Systematic Literature Review of Optimization Methods in Skeleton-Based Human Action Recognition
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“[20] used EOG to monitor ocular activity to determine the relationship between human actions and eye motions.”
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Xu, D., Hu, R., Wang, Z., Luo, L., Li, D., & Zeng, W. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3548207 · Cited by 6
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“In this decade, many studies have been on technology in human activity recognition (HAR) [1-3].”
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Eliza, I. J., Al-Nabhan, N., Noor, J., Rahaman, M., & Islam, A. B. M. A. A. (2023) · IEEE Access · doi:10.1109/access.2023.3311261 · Cited by 7
“To recognize the transitions with better accuracy, a viable alternative to ML-based approaches can be template-matching [2].”
BackgroundMethodACTIVA: Innovation in Quality of Care for Nursing Homes Through Activity Recognition
Lendínez, A. M., Ruiz, J. L. L., Nugent, C., & Espinilla, M. (2023) · IEEE Access · doi:10.1109/access.2023.3329748 · Cited by 6
“In the literature, there are a number of works that explore this approach [38], [39].”
BackgroundHuman Activity Recognition in Logistics Using Wearable Sensors and Deep Residual Network
Mekruksavanich, S., Tancharoen, D., & Jitpattanakul, A. (2023) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon58879.2023.10322393 · Cited by 6
“One potential approach to achieving this goal is sensor-based human activity recognition (S-HAR) [2], [3].”
BackgroundAn Unsupervised Method to Recognise Human Activity at Home Using Non-Intrusive Sensors
Ramos, R. G., Domingo, J. D., Zalama, E., & Gómez‐García‐Bermejo, J. (2023) · Electronics · doi:10.3390/electronics12234772 · Cited by 8
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“Human action recognition (HAR) in the healthcare sector, and particularly in diagnostics and rehabilitation of patients has gained significant attention in recent years due to the potential to facilitate more effective and efficient rehabilitation interventions [7, 12, 17, 29, 31].”
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Human Activity Recognition Using an IoT-based Posture Corrector and Machine Learning
Walee, R., Phumekham, N., Laitrakun, S., & Sueayan, T. (2022) · International Conference on Business and Industrial Research (ICBIR) · doi:10.1109/icbir54589.2022.9786396 · Cited by 6
A dataset of ambient sensors in a meeting room for activity recognition
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“Sensor-based recognition methods find applications in fields from healthcare to human-machine interaction 35 , and DOO-RE, by focusing on public spaces–a domain not extensively covered by existing datasets–plays a crucial role in broadening these applications.”
DS-MS-TCN: Otago Exercises Recognition With a Dual-Scale Multi-Stage Temporal Convolutional Network
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“To date, much of the research up to now has annotated the daily activities and repetitive exercises as a whole [1], [8].”
BackgroundSheng, T., & Huber, M. (2025) · Sensors · doi:10.3390/s25134032 · Cited by 5
“For a comprehensive understanding of various aspects and methodologies in HAR, we refer readers to survey papers by Minh Dang et al. [17], Wang et al. [18], and Zhang et al. [19], which offer in-depth discussions on different techniques in HAR research.”
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Behavior capture guided engagement recognition
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“During recent years, the widespread use of portable wearable devices, such as smartwatches, fitness trackers, and smartphones, has made sensor-based human activity recognition (HAR) a hot research topic in ubiquitous computing community [12, 59, 60].”
A Real-Time Human Action Recognition Model for Assisted Living
Wang, Y., Muntean, C. H., Pathak, P., & Stynes, P. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-96196-0_1 · Cited by 5
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“However, these methods often incur significant costs and require wearable devices, which can lead to user discomfort and inconvenience [1–5].”
The human activity recognition using radio frequency signals
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“In the realm of rehabilitation activity recognition, these diverse technologies converge to provide comprehensive insights into patients’ movements and functional abilities [2].”
Retracted: Gesture and Voice-Based Automation System for Home Surveillance
Santhosh, B., Oli, S., Jayanth, C., Musali, S. K., Raj, P. N., & Kandpal, A. (2025) · International Conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT) · doi:10.1109/ce2ct64011.2025.10941253 · Cited by 5
RetractedSensor-Based Human Activity Recognition Using Adaptive Class Hierarchy
Kondo, K., & Hasegawa, T. (2021) · Sensors · doi:10.3390/s21227743 · Cited by 5
“The solutions for human activity recognition are categorized into video-based methods [8,9] and sensor-based methods [1,2,8] based on the different sensing devices.”
Classifying Sport-Related Human Activity from Thermal Vision Sensors Using CNN and LSTM
Polo-Rodríguez, A., Lendínez, A. M., Espinilla, M., & Medina-Quero, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-13321-3_4 · Cited by 5
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“[12] used EOG to monitor ocular activity to determine the relationship between human activities and eye movements.”
BackgroundMethodSubtransfer Learning in Human Activity Recognition: Boosting the Outlier User Accuracy
Kanjilal, R., Küçük, M. F., & Uysal, I. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3312146 · Cited by 5
“…and efficient signals from multiple sensors placed in the body and reduces the overall cost and complexity of the processes required for meaningful representation of the raw data to correctly distinguish between similar types of activities of daily living (ADLs) such as jogging and running [10].”
BackgroundMemory-Efficient High-Accuracy Food Intake Activity Recognition with 3D mmWave Radars
Chiang, H.-C., Wu, Y., Shirmohammadi, S., & Hsu, C.-H. (2023) · International Workshop on Multimedia Assisted Dietary Management (MADiMa) · doi:10.1145/3607828.3617791 · Cited by 5
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Hand gesture recognition via deep data optimization and 3D reconstruction
Mustafa, Z., Nsour, H., & Tahir, S. B. U. D. (2023) · PeerJ Computer Science · doi:10.7717/peerj-cs.1619 · Cited by 5
Vision-based Human Activity Recognition Using Local Phase Quantization
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Blockchain-Based Smart Monitoring Framework for Defense Industry
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“Dang et al. [45] proposed a real-time model for automatically calibrating multiple cameras to monitor a person’s actions, with encouraging results from real-world testing.”
BackgroundA novel rehabilitation action recognition approach using AF-LiteFormer from millimeter wave radar
Yi, J., Zou, H., Yuan, R. B., Jun, G., & Fu, S. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad7b60 · Cited by 5
“Machine learning has made great progress in the field of human activity recognition [18].”
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Sensor-Based Human Activity and Behavior Computing
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Transfer Learning Model Knowledge Across Multi-Sensors Locations Over Body Sensor Network
Qian, X., Chen, H., Cai, Y. M., Chu, K., Xu, W., & Huang, M.-C. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3166187 · Cited by 5
“The models include deep belief network (DBN) [38] and stacked auto-encoder (SAE) [39].”
BackgroundHuman Activity Recognition Using Single Frame CNN
Aruna, V., Deepthi, S. A., & Rudraksha, L. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-4831-2_17 · Cited by 5
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“Therefore, there is an ongoing need to combine sensing and communication functionalities on the same wireless framework, popularly known as integrated sensing and communication (ISAC) [12]–[15].”
BackgroundThe Review of Human Activity Recognition SurveyPreprint
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“H UMAN activity recognition (HAR) can recognize activities based on human activity data and has become an important tool in health monitoring, motion tracking, and smart manufacturing [1], [2], [3].”
Ensemble early exit network on human activity recognition using wearable sensors
Yu, J., Zhang, L., Cheng, D., Bu, C., Liu, L., Wu, H., & Song, A. (2025) · Computer Networks · doi:10.1016/j.comnet.2025.111409 · Cited by 6
Nanduri, S. K., & Delhi, V. S. K. (2025) · Journal of Information Technology in Construction · doi:10.36680/j.itcon.2025.038 · Cited by 4
Eff-WHAR: A Lightweight Design for Efficient Wearable Sensor-Based Human Activity Recognition
Chen, Z., Zou, H., Wang, L., Wang, B., Zhang, F., Ma, S., Pan, Y., & Li, J. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3509961 · Cited by 5
A deep learning behavior analysis model for efficient video surveillance using multi pose features
Shana, L., & Christopher, C. S. (2024) · Ain Shams Engineering Journal · doi:10.1016/j.asej.2024.103245 · Cited by 4
Ground Reaction Force Estimation via Time-Aware Knowledge Distillation
Jeon, E. S., Mitra, S., Lee, J., Save, O. M., Shukla, A., Lee, H., & Turaga, P. K. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3578012 · Cited by 4
“T HE STUDY of human Internet of Things with wear-ables has been receiving increasing attention for various applications such as daily life analysis for healthcare, physical activity, and smart homes [1], [2], [3], [4], [5], [6].”
HAR-AttenNet: Multi-Head Transformer for Precise Human Activity Recognition Using Wearable Devices
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Invited Paper: Hierarchical Activity Recognition with Smartwatch IMU
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“All smartphone and sensor approaches use secure authentication and Human Activity Recognition (HAR) [11, 12].”
Spatial-temporal episodic memory modeling for ADLs: encoding, retrieval, and prediction
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Sensor-Based Human Activity Recognition Using a Hybrid CNN-SVM Approach
Charabi, I., Abidine, M. B., & Fergani, B. (2024) · International Conference on Image and Signal Processing and their Applications (ISPA) · doi:10.1109/ispa59904.2024.10536787 · Cited by 4
“HAR is commonly categorized into two primary types: vision-based HAR and sensor-based HAR [1].”
BackgroundReal-Time Driver Depression Monitoring for Accident Prevention in Smart Vehicles
Ahmed, M. H., Saeed, Y., Mehmood, A., Saeed, M., Ahmed, N., Ilyas, Q. M., Iqbal, S., & Abid, N. (2024) · IEEE Access · doi:10.1109/access.2024.3407361 · Cited by 4
“Understanding driver state, including potential depression, is crucial for ADAS to develop optimal vehicle control strategies [14].”
BackgroundInfluentialPolo-Rodríguez, A., Valera, J. C., Peral, J., Gil, D., & Medina-Quero, J. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19914-1 · Cited by 4
Noorani, S. H., Arsalan, A., Frnda, J., Khan, S., Raheel, A., & Ehatisham-ul-Haq, M. (2024) · IEEE Access · doi:10.1109/access.2024.3498852 · Cited by 4
“Vision-based approaches utilize video and image processing techniques to detect activities, as they analyze sequences of participant behaviors and environmental interactions [4], [5], [6], [7], [8], [9].”
Wang, C., Chen, L., Zhou, B., Xian, Y., Zhao, Y., & Huan, Z. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.126893 · Cited by 5
Hyex-net: hybrid efficient Xception network for human activity recognition using multimodalities
Gupta, S., & Mahapatra, R. P. (2025) · Cluster Computing · doi:10.1007/s10586-024-04998-z · Cited by 4
Human Activity Recognition Based on the GRU with Augmented Wi-Fi CSI Signals
Kang, H., Kim, D., & Toh, K. (2024) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon61640.2024.10902832 · Cited by 4
Natarajan, A., Vijayakumar, K., & Singh, M. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3576244 · Cited by 4
Classification of Hand-Based and Non-Hand-Based Physical Activities Using Wearable Sensors
DAŞ, D. B., & Birant, D. (2020) · International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) · doi:10.1109/ismsit50672.2020.9255171 · Cited by 4
“Human activity recognition (HAR) can be defined as the ability of machines to detect recent or former activities on the basis of data collected from various sensors [1].”
Background
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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