Cited by 730Context-Aware Activity Recognition SystemsHuman Pose and Action RecognitionGait Recognition and Analysis

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Sensor-based and vision-based human activity recognition: A comprehensive survey

Minh Dang, L., Min, Kyungbok, Wang, Hanxiang, Jalil Piran, Md., Hee Lee, Cheol, Moon, Hyeonjoon·2020·Pattern Recognition

DOI 10.1016/j.patcog.2020.107561

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“…is classiﬁed into two categories; data comes from vision sensors like camera or wearable sensors; there is a diﬀerence in preprocessing between each category; data from sensors need to reduce noise and enhance the image, while segmentation was required for the data that come from camera [13].”
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Human Action Recognition on Exceptional Movement of Worker Operation
Yang, C.-L., Hsu, S.-C., Hsu, Y.-W., & Kang, Y.-C. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-80462-6_46 · Cited by 4
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de la Cal, E., Fáñez, M., Villar, M. A., Villar, J. R., & González, V. M. (2022) · Logic Journal of IGPL · doi:10.1093/jigpal/jzac025 · Cited by 4
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Room-Level Activity Classification from Contextual Electricity Usage Data in a Residential Home
Farooq, M., Shawky, M. A., Fatima, A., Tahir, A., Khan, M. Z., Abbas, H. T., Imran, M. A., Abbasi, Q. H., & Taha, A. (2023) · International Telecommunications Conference (ITC-Egypt) · doi:10.1109/itc-egypt58155.2023.10206425 · Cited by 4
“Generally, human activity recognition can be categorized into vision-based and sensor-based methods [7], as shown in Fig.”
Background
Accelerating Activity Inference on Edge Devices Through Spatial Redundancy in Coarse-Grained Dynamic Networks
Ye, N., Zhang, L., Xiong, D., Wu, H., & Song, A. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3458441 · Cited by 4
“Until now, there has been a broad range of HAR applications in various ubiquitous computing scenarios, including elderly care, health management, human–computer interaction, assisted living, ﬁtness, sports monitoring, and gesture analysis, etc [6].”
Ambient Intelligence for Next-Generation AR
Scargill, T., Eom, S., Chen, Y., & Gorlatova, M. A. (2026) · Springer eBooks · doi:10.1007/978-3-032-03296-6_1 · Cited by 9
Multitemporal Sampling Module for Real-Time Human Activity Recognition
Park, J., Lim, W.-S., Kim, D., & Lee, J. (2022) · IEEE Access · doi:10.1109/access.2022.3176606 · Cited by 5
“H UMAN Activity Recognition (HAR) aims to identify the daily activities of humans from time-series signals collected by sensors [1].”
Background
Placement Effect of Motion Sensors for Human Activity Recognition using LSTM Network
Mekruksavanich, S., Jitpattanakul, A., & Thongkum, P. (2021) · 2021 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunication Engineering · doi:10.1109/ectidamtncon51128.2021.9425719 · Cited by 3
“One of the top study topics of the past decade is HAR using embedded sensors [6].”
Background
Enhanced federated recognition mechanism based on spatial-temporal model with split learning for multi-view human activity classification in edge intelligent network
Tuan, N. A., Rizwan, A., Moe, S. J. S., & Kim, D. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-20354-0 · Cited by 3
Wearable Spine Tracker vs. Video-Based Pose Estimation for Human Activity Recognition
Walkling, J., Sander, L., Masch, A., & Deserno, T. M. (2025) · Sensors · doi:10.3390/s25123806 · Cited by 4
ProLoRA: Resource-Efficient Personalized Federated Learning for Sensor Based Human Activity Recognition
Ba, A. F., Mao, Y., Djigal, H., & Muhammad, A. U. (2024) · International Conference on Mobility, Sensing and Networking (MSN) · doi:10.1109/msn63567.2024.00144 · Cited by 3
Hash-MMDC: Enhancing human activity recognition with hash-based optimization
Cheng, S., Liu, Q., & Wan, J. (2025) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-025-00297-8 · Cited by 3
“Sensor-based HAR uses devices such as accelerometers and gyroscopes to capture motion data (Minh Dang et al. 2020), while vision-based approaches employ computer vision techniques to interpret video feeds (Haddad et al. 2024).”
Beyond Falls: A Hybrid CNN–LSTM–Attention Framework for Pre-, Transition-, and Post-Fall Detection With Wearable Inertial Sensors
Bhatti, F. A., Riaz, Q., & Krüger, B. (2025) · IEEE Access · doi:10.1109/access.2025.3641198 · Cited by 3
“Vision-based methods utilize cameras to capture image sequences, while ambient-sensor-based systems rely on various modalities such as floor, infrared, or depth sensors [9].”
A multimodal spatio-temporal graph neural network framework for fatigue detection in tennis serving
Yuan, Y., Wang, L., Hui, J., & Jiao, J. (2026) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2026.1828589 · Cited by 3
Personalized Federated Attention State Space Learning for Wearable Human Activity Recognition
Xiao, Z., Wan, Q., & Tong, H. (2026) · ACM Transactions on Embedded Computing Systems · doi:10.1145/3820371 · Cited by 4
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Nagpal, D., & Kumar, R. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-4960-9_15 · Cited by 3
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Shabbir, M., Ahmad, F., Alanazi, S., Khan, M. H., Li, J., Mahmood, T., Naseem, S., & Anwar, M. (2025) · Healthcare Technology Letters · doi:10.1049/htl2.70013 · Cited by 3
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Abadi, M. H. B., Alashti, M. R. S., Holthaus, P., Menon, C., & Amirabdollahian, F. (2021) · Journal of robotics & autonomous systems · doi:10.31256/bw7kt2n · Cited by 3
“…based on scene properties, such as protagonist (individual or group), activity (daily activities, sports, ... ), environment (indoor or outdoor), or situation (controlled or spontaneous) and camera properties, such as data type (RGB or RGB-D), dynamics (static, moving), perspective, etc. [1], [13].”
Fusion Approaches of Heterogeneous Multichannel CNN and LSTM Models for Human Activity Recognition using Wearable Sensors
Saengthong, P., & Laitrakun, S. (2021) · doi:10.1109/icitee53064.2021.9611824 · Cited by 3
Extended SESIM: A Tool to Support the Generation of Synthetic Datasets for Human Activity Recognition
Musharu, T., & Vogts, D. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-15342-6_12 · Cited by 3
Application of smartphone in recognition of human activities with machine learning
Fayadh, S. M., Alsaadi, E. M. T. A., & Hallawi, H. (2023) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v30.i2.pp860-869 · Cited by 3
Proposed Hybrid Model Recurrent Neural Network for Human Activity Recognition
Errafik, Y., Kenzi, A., & Dhassi, Y. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-29857-8_8 · Cited by 3
Optimization with Label Weighting Extreme Learning Machine for Human Activity Recognition
Kumar, L. M. A., & Murugan, S. (2023) · International Conference on Intelligent Computing and Control Systems (ICICCS) · doi:10.1109/iciccs56967.2023.10142441 · Cited by 3
“Especially, HAR in Smart Homes with the aid of ubiquitous and simple sensors has received considerable interest in the domain of ambient intelligence and assisted living technology to improve the standard of living of the resident within the home environment [2].”
Background
Keypoint Changes for Fast Human Activity Recognition
Reid, S., Coleman, S., Kerr, D., Vance, P. J., & O’Neill, S. M. (2023) · SN Computer Science · doi:10.1007/s42979-023-02063-x · Cited by 3
“The recent emergence of deep learning techniques has enabled the development of methods for fast extraction of skeletal keypoints features, which (alongside continued increases in computing power) has been a breakthrough in enabling the development of real-time methods for human activity recognition that can readily s…”
Background
A Comparative Study of Supervised and Unsupervised Approaches in Human Activity Analysis Based on Skeleton Data
Hossen, M. A., & Abas, P. E. (2023) · International Journal of Computing and Digital Systems · doi:10.12785/ijcds/1401110 · Cited by 3
CamPro: Camera-based Anti-Facial Recognition
Zhu, W., Sun, Y., Liu, J., Cheng, Y., Ji, X., & Xu, W. (2024) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2024.24158 · Cited by 3
Compliant-Control-Based Assisted Walking with Mobile Manipulator
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“In addition, sensor-based human behavior recognition methods [24] have emerged recently, and they can be subdivided into methods based on wearable and non-wearable sensors.”
Method
A Novel CNN-SVM Hybrid Model for Human Activity Recognition
Charabi, I., Abidine, M. B., & Fergani, B. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-60629-8_27 · Cited by 3
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Browen, I., Camarillo-Abad, H. M., Cibrian, F. L., & Qi, D. (2024) · Creativity and Cognition · doi:10.1145/3635636.3664256 · Cited by 3
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Calisti, L., & Lattanzi, E. (2024) · IEEE Access · doi:10.1109/access.2024.3430049 · Cited by 3
“In the former, the activities are classified from video and using computer vision, while in the latter the data is extracted from different inertial sensors such as an accelerometer or a gyroscope [6].”
Method
A Review on Time Series Learning-Based Human Action Recognition
Goel, A., & Gupta, B. (2024) · International Conference on Communication, Computer Sciences and Engineering (IC3SE) · doi:10.1109/ic3se62002.2024.10593601 · Cited by 3
Advancing Human Activity Recognition Using Ultra-Wideband Channel Impulse Response Snapshots
Wang, Y., & Basiri, A. (2024) · International Conference on Activity and Behavior Computing (ABC) · doi:10.1109/abc61795.2024.10651886 · Cited by 4
“Another widely used group of approaches for HAR is vision-based approaches [5], [11].”
BackgroundMethod
Recognition of Hand Activities for Automatic Generation of Assembly Instructions
Moriz, A., Bröhl, F., Buxel, M., Wolfschläger, D., Schukat, E., Riesener, M., & Schmitt, R. H. (2024) · Procedia CIRP · doi:10.1016/j.procir.2024.07.025 · Cited by 3
Systematic Comparison of Machine Learning for Activity Recognition in Cross-Subject vs. NonCross-Subject Scenarios: A Preliminary Analysis
Pe, S., Nicora, G., Guerra, B. M. V., Sozzi, S., & Parimbelli, E. (2024) · IEEE International Forum on Research and Technology for Society and Industry (RTSI) · doi:10.1109/rtsi61910.2024.10761630 · Cited by 3
Optimized Edge-Cloud System for Activity Monitoring Using Knowledge Distillation
Deniz, D., Ros, E., Ortigosa, E. M., & Barranco, F. (2024) · Electronics · doi:10.3390/electronics13234786 · Cited by 5
“Human Activity Recognition (HAR) is a powerful example that classifies human actions analyzing data from different sources [21,22]: sensor-based HAR uses smart devices such as smart wristbands, inertial sensors, or radio-frequency-based devices especially for real-world healthcare applications…”
Hierarchical Zero-Shot Approach for Human Activity Recognition in Smart Homes
Fritsch, S. G., Cruciani, F., Rey, V. F., Cleland, I., Nugent, L., Lukowicz, P., & Nugent, C. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-77571-0_17 · Cited by 4
Human Activity Recognition in an Open World
Prijatelj, D. S., Grieggs, S., Huang, J., Du, D., Shringi, A., Funk, C., Kaufman, A., Robertson, E., & Scheirer, W. J. (2024) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.14476 · Cited by 3
Multi-classifier information fusion for human activity recognition in healthcare facilities
Hu, D., Wang, M., & Li, S. (2025) · Frontiers of Engineering Management · doi:10.1007/s42524-024-4074-y · Cited by 3
“There are two primary techniques in activity recognition: visual-based approaches and sensor-based approaches, each utilizing different types of data (Dang et al., 2020).”
DeepFake Detection Using Fine-Tuned CNN Architectures
Çetintaş, D., & Yücel, Z. (2025) · Celal Bayar Üniversitesi Fen Bilimleri Dergisi · doi:10.18466/cbayarfbe.1530209 · Cited by 3
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He, Y., Lv, J., Cheng, L., Meng, L. K., Deng, D., & Wu, Y. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713741 · Cited by 3
“We reviewed 23 studies [1, 3, 9, 11, 15, 16, 18, 19, 26, 27, 31, 43– 45, 55, 63, 65, 68, 70, 72, 76, 79, 81] and identifed two main categories of action-related visual elements.”
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Almusawi, A., & Ali, A. H. (2022) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-030-98741-1_7 · Cited by 4
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Herrmann, J.-P., Atanasyan, A., Casser, F., & Tackenberg, S. M. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2022.12.317 · Cited by 3
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Zhang, W., Zhou, Y., Wang, Y., Wang, R., & Yang, H. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4812249 · Cited by 3
Development of Controversial Issues Learning Models in Improving Student's Critical Thinking Skills
Raharjo, R., Solihatin, E., Maiwan, M., & Kardiman, Y. (2023) · Studies in Learning and Teaching · doi:10.46627/silet.v4i3.226 · Cited by 2
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Heravi, M. Y., Jang, Y., & PhD, H. C. (2025) · Journal of Construction Engineering and Management · doi:10.1061/jcemd4.coeng-15888 · Cited by 4
Activities of Daily Living (ADL) and Behavior Recognition
Aleksić, S., Despotović, V., & Cristina, S. (2025) · Intelligent systems reference library · doi:10.1007/978-3-031-84158-3_7 · Cited by 2
Human Activity Recognition in MMH: Pilot Study on Lifting and Lowering Classification
Vyas, S., Vyas, C., Sharotry, A., Jimenez, J. A., Qasem, A., & Mediavilla, F. A. M. (2025) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2025.09.130 · Cited by 2
MKFi: Temporally robust WiFi CSI-based activity recognition under data scarcity
Zhang, L., Kang, H., Lee, J., Cheng, D., Kim, D., & Toh, K. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.112512 · Cited by 3
Evidential association rule learning for semi-supervised activity recognition with soft label derivation
Geng, X., Zhang, J., Yang, Z., Zhou, Z.-J., & Ma, Z. (2025) · Information Sciences · doi:10.1016/j.ins.2025.122811 · Cited by 2
Real-world Classification of Caregiver Sensitivity to Infant Distress
Khante, P., Madden‐Rusnak, A., & de Barbaro, K. (2025) · IEEE International Conference on Development and Learning (ICDL) · doi:10.1109/icdl63968.2025.11204456 · Cited by 2
“Most HAR research focuses on lower-level behaviors, primarily classifying atomic movements (e.g., gestures, poses) and actions (e.g., eating, walking) [10]–[12].”
Beyond 1 × 1 Convolutions: A Dynamic Select-and-Fuse Channel Sampling Strategy for On-Device Human Activity Recognition
Li, Z., Chen, G., Liu, X., Zhang, L., Sun, Q., Wang, K., Wu, H., & Song, A. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3650552 · Cited by 2
“We adopt a 7:1:2 split for the training, validation, and test sets to ensure fair and consistent evaluation across all experiments [45].”
Human activity recognition (HAR): techniques, applications, and future directions
Rani, M., & Kumar, M. (2026) · International Journal of Data Science and Analytics · doi:10.1007/s41060-026-01042-8 · Cited by 2
Descriptor: The WiFi-Based Human Activity Recognition Under Everyday Life Changes Dataset (WiFi-Wild)
Elkelany, A., Ross, R., & McKeever, S. (2026) · IEEE data descriptions. · doi:10.1109/ieeedata.2026.3681245 · Cited by 2
“Vision-based approaches face privacy and environmental constraints, while wearable-based systems suffer from high power consumption, cost, usability, and calibration issues [7].”
Shapelet Based Visual Assessment of Cluster Tendency in Analyzing Complex Upper Limb Motion
Datta, S., Karmakar, C. K., Rathore, P., & Palaniswami, M. S. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414144 · Cited by 2
“Analysis of human motion data has received particular attention for the development of smart health and activity monitoring applications [2, 3].”
Background
Towards Group Activity Recognition and Analysis for Improving the Safety of Automated Vehicles
Abken, A., Loke, S. W., & Zaslavsky, A. (2021) · IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) · doi:10.1109/ithings-greencom-cpscom-smartdata-cybermatics53846.2021.00026 · Cited by 2
Anwendung von Human Activity Recognition im Unternehmenskontext – Ein Konzept für die Zukunft?
Rimbeck, M., Wähling, J., & Stumpf-Wollersheim, J. (2022) · Forum Dienstleistungsmanagement · doi:10.1007/978-3-658-37344-3_15 · Cited by 2
Kinematic Motion Analysis with Volumetric Motion Capture
Zhu, Y., Detig, C., Kane, S. M., & Lourie, G. M. (2022) · International Conference on Information Visualisation (IV) · doi:10.1109/iv56949.2022.00019 · Cited by 2
Static and dynamic human activity recognition with VGG-16 pre-trained CNN model
Harahap, M., Damar, V., Yek, S., Michael, M., & Putra, M. A. P. (2023) · JURNAL INFOTEL · doi:10.20895/infotel.v15i2.916 · Cited by 2
“The VGG-16 model is chosen as the pre-trained model in this study because the VGG-16 model may shorten the training time of the CNN algorithm for classifying human activities and maintaining a good performance even with a limited dataset [8], [21].”
BackgroundMethod
Depth-based human action recognition using histogram of templates
Younsi, M., Yesli, S., & Diaf, M. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16989-0 · Cited by 2
Anomaly Detection Approach for Human Detection in Crowd Based Locations
Srivastava, R., & Chhabra, G. (2023) · International Conference on Sustainable Emerging Innovations in Engineering and Technology (ICSEIET) · doi:10.1109/icseiet58677.2023.10303413 · Cited by 2
Soft Biometrics for Cybersecurity: Ongoing Revolution for Industry 4.0
Cascone, L., Loia, V., Nappi, M., & Narducci, F. (2024) · Computer · doi:10.1109/mc.2023.3292715 · Cited by 2
“18 There are two main categories of liveness detection: › The active category involves prompting users to perform actions that cannot be spoofed, 17 such as analyzing lip movements when asked to utter a specific phrase.”
Background
Automated Smart Home Based on Arduino Applications
Abdulabbas, T. E., Mahmoud, S. M., & Abdulsattar, A. Z. (2022) · International Conference on Advances in Engineering Science and Technology (AEST) · doi:10.1109/aest55805.2022.10412951 · Cited by 2
“It can also help those people who cannot know how to use smart phones and prefer to live in their homes rather than care homes s [24] [25] .”
BackgroundMethod
Ecosystem of smart spaces: An overview review
Ndaguba, E. A., & Arukwe, C. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13462-3.00010-8 · Cited by 2
Video Surveillance System-Based Human Activity Recognition Using Hierarchical Auto-Associative Polynomial Convolutional Neural Network with Garra Rufa Fish Optimization
Thiruvenkatam, P., Thangavel, P., Balasubramanian, K., & Sekar, S. (2024) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s021800142456007x · Cited by 2
Entropy and Memory Aware Active Transfer Learning in Smart Sensing Systems
Alam, U., Farhan, A. A., Kanwal, S., & Allheeib, N. (2024) · IEEE Access · doi:10.1109/access.2024.3412653 · Cited by 2
“These sensor devices [21] have enabled us to record different aspects of HAR [22] by using different data collection methods [23], mainly through the images/videos [24], ambient sensors [25], and wearable sensors [26].”
Method
System Design for Sensing in Manufacturing to Apply AI through Hierarchical Abstraction Levels
Sopidis, G., Haslgrübler, M., Azadi, B., Guiza, O., Schobesberger, M., Anzengruber-Tánase, B., & Ferscha, A. (2024) · Sensors · doi:10.3390/s24144508 · Cited by 2
“Composite activities, as high-lighted in the literature by [40,59], pose particular challenges due to their composition of multiple shorter activities and the associated temporal decomposition required for their recognition.”
Background
Detecting Human–Human Interactions Using Smartphones
Kitamura, N., & Yokoyama, S. (2024) · International Conference on Computer Communication and Information Systems (CCCIS) · doi:10.1109/cccis63483.2024.00009 · Cited by 2
“Human activities can be recognized using various sensors, such as cameras, wearable sensors, beacons, and other intelligent sensors [7].”
Background
A General Multistage Deep Learning Framework for Sensor-Based Human Activity Recognition Under Bounded Computational Budget
Wang, X., Zhang, L., Cheng, D., Tang, Y., Wang, S., Wu, H., & Song, A. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3481549 · Cited by 2
“Various prior works explore data segmentation in the context of HAR [18].”
Background
Harnessing IoT and Robotics for Comprehensive Human Activity Recognition in Smart Buildings
Toupas, P., Tsamis, G., Zotou, D., Giakoumis, D., Votis, K., & Tzovaras, D. K. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-69344-1_12 · Cited by 2
A New Model for Assembly Task Recognition: A Case Study of Seru Production System
Torkul, O., Selvi, İ. H., Şişci, M., & Diren, D. D. (2024) · IEEE Access · doi:10.1109/access.2024.3484955 · Cited by 2
An Investigation of Frequency-Domain Pruning Algorithms for Accelerating Human Activity Recognition Tasks Based on Sensor Data
Su, J., Shao, H., Deng, X., & Jiang, Y. (2024) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.057604 · Cited by 2
An Intermediate Deep Feature Fusion Approach for Understanding Human Activities from Image Sequences
Garg, A., Nigam, S., & Singh, R. K. (2024) · SN Computer Science · doi:10.1007/s42979-024-03345-8 · Cited by 2
“Smart surveillance, CCTV cameras, intelligent driving, pedestrian detection, healthcare, and patient monitoring are a few applications of HAR [2, 3].”
Improving human physical activities classification via feature selection with multi-class support vector machines and grey wolf optimization
Khalaf, H., & Riyadh, M. (2024) · AIP conference proceedings · doi:10.1063/5.0236952 · Cited by 2
Video-Based Plastic Bag Grabbing Action Recognition: A New Video Dataset and a Comparative Study of Baseline Models
Low, P. J., Ng, B. Y., Mahzan, N. I., Tian, J., & Leung, C.-C. (2025) · Sensors · doi:10.3390/s25010255 · Cited by 2
A Review of Advances in Bio-Inspired Visual Models Using Event-and Frame-Based Sensors
Salim, A. Z., & Abdul-Kreem, L. I. (2025) · Advances in Technology Innovation · doi:10.46604/aiti.2024.14121 · Cited by 2
Smart sensor-based activity recognition for patients in healthcare
Gharsi, N., Mohammadi, K., & Shahbazi, M. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-24790-3.00029-6 · Cited by 2
Dendron: Enhancing Human Activity Recognition With on-Device Tinyml Learning
Shalby, H. H. Y., & Roveri, M. (2025) · doi:10.1109/cies64955.2025.11007628 · Cited by 2
“In this paper, we focus on sensor-based HAR, and the related literature will explore this specific approach.”
Optimizing Human Action Recognition in Still Images Using Deep Learning Models and Grad-CAM++ for Visualization
Alam, M. T., Dasgupta, S., & Roy, K. (2024) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccit64611.2024.11022452 · Cited by 3
Proto-CSNet: A Prototype Network Model Integrating CNN and Self-Attention for Enhanced Human Activity Recognition
Hu, S., Liu, J., Zhang, C., Zhu, X., & Li, L. (2024) · International Conference on Mobility, Sensing and Networking (MSN) · doi:10.1109/msn63567.2024.00018 · Cited by 2
“Environmental sensors collect data from microphones and vibration sensors to monitor and analyze activity-induced changes [11], [12].”
Cross-attention multi branch for Vietnamese sign language recognition: CrossViViT
Chu, M. H., Nguyen, H. D., Nguyễn, T. N. A., & Vu, H. N. (2025) · Discover Computing · doi:10.1007/s10791-025-09669-0 · Cited by 2
“The current research on human activity recognition predominantly employs two main methods: computer vision and sensor-based approaches [2].”
Representation-centric survey of supervised skeletal action recognition and the new benchmark
(4829), Y. L., Yang, J., Perera, M., Ji, P., Kim, D., Xu, M., Wang, T., Anwar, S., Gedeon, T., (6656), L. W., et al. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.114140 · Cited by 2
Recent Trends in Emotion Analysis: A Big Data Analysis Perspective
Özyer, T., Selin, A. D., & Alhajj, R. (2020) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam49781.2020.9381441 · Cited by 2
“[27] is to compare the methods using data from different input types.”
Method
Privacy preserving getup detection
Lumetzberger, J., Raoofpour, A., & Kampel, M. (2021) · International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) · doi:10.1145/3453892.3453905 · Cited by 2
ActivityExplorer: A semi-supervised approach to discover unknown activity classes in HAR systems
Brighi, M., Franco, A., & Maio, D. (2021) · Pattern Recognition Letters · doi:10.1016/j.patrec.2021.08.029 · Cited by 2
Discrete HMM for Visualizing Domiciliary Human Activity Perception and Comprehension
Kuan, T.-W., Tseng, S.-P., Chen, C.-W., Wang, J.-F., & Sun, C.-A. (2022) · Applied Sciences · doi:10.3390/app12063070 · Cited by 2
“Human activity recognition [1] has generally been categorized into two approaches, i.”
Background
An Adaptive Neuro-Fuzzy Approach for Activity Recognition in Situation-aware Wearable Systems
Apicella, G., D’Aniello, G., Fortino, G., Gaeta, M., Gravina, R., & Tramuto, L. G. (2022) · IEEE International Conference on Human-Machine Systems (ICHMS) · doi:10.1109/ichms56717.2022.9980773 · Cited by 2
“of human activities such as walking and standing [15].”
Background
Human Activity Recognition using 1D-CNN and Stacked LSTM
Waghchaware, S., & Joshi, R. D. (2022) · International Conference on Signal and Information Processing · doi:10.1109/iconsip49665.2022.10007450 · Cited by 2
“Initially, there was a vision-based approach for HAR which generates data using a camera, but it has some limitations regarding light dependency and privacy of the person [2].”
Method
Gaussian Mixture Model in Dynamic Background of Video Sequences for Human Detection
Setyoko, B. H., Noersasongko, E., Shidik, G. F., Budiman, F., Soeleman, M. A., Andono, P. N., & Pujiono, P. (2022) · International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) · doi:10.1109/isriti56927.2022.10052825 · Cited by 3
“Background subtraction works by isolating and extracting the foreground for further analyzing the video sequence [12].”
Background
A Survey on Different Application Areas based on RSS (Received Signal Strength) and Possible Hardware and Software Tools for the Collection of RSS
Sarsodia, T., Bhatt, U. R., Upadhyay, R., & Bhat, V. (2022) · IEEE International Conference on Current Development in Engineering and Technology (CCET) · doi:10.1109/ccet56606.2022.10080843 · Cited by 2
“is referred to as activity recognition [30].”
Human Activity Recognition Using Inertial Sensors in a Smartphone: Technical Background(Review)
Khalaf, H., & Riyadh, M. (2024) · Journal of Al-Nahrain University-Science · doi:10.22401/anjs.27.1.10 · Cited by 2
Interpretability in Video-based Human Action Recognition: Saliency Maps and GradCAM in 3D Convolutional Neural Networks
García-Torres, J., & Meinich-Bache, Ø. (2024) · Nordic Machine Intelligence · doi:10.5617/nmi.10587 · Cited by 2
“Despite HAR being a widely explored area in computer vision, there is limited research on interpretable HAR models [14].”
Background
Sussex-Huawei Locomotion Recognition Using Machine Learning and Deep Learning with Multi-sensor data
Wang, H., Huang, H., Wang, J., & Sun, F. (2024) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3675094.3678457 · Cited by 2
Self-citation

Authors: Minh Dang, L., Min, K., Wang, H., Jalil Piran, M., Hee Lee, C., & Moon, H.
Affiliations: Sejong University (KR)
Corresponding author: Moon, Hyeonjoon
ORCID: Wang, Hanxiang
Moon, Hyeonjoon
Venue: Pattern Recognition
Publisher: Elsevier BV
Published: 2020-12
Volume: 108
Pages: 107561
ISSN: 0031-3203 (ISSN-L)
DOI: 10.1016/j.patcog.2020.107561
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
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
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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