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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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“Recent vision-based technologies for human pose estimation and action classification can provide extensive information about human behaviors ( 6 ).”
BackgroundA Close Look into Human Activity Recognition Models using Deep Learning
Tee, W. Z., Dave, R., Seliya, J., & Vanamala, M. (2022) · International Conference on Computing, Networks and Internet of Things (CNIOT) · doi:10.1109/cniot55862.2022.00043 · Cited by 33
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“Bulling et al. [19] compiled a seminal tutorial paper that explicitly captures the classical, pre-Deep-Learning era of HAR with wearable sensors.”
Human Activity Recognition Based on Hybrid Learning Algorithm for Wearable Sensor Data
Athota, R. K., & Sumathi, D. (2022) · Measurement Sensors · doi:10.1016/j.measen.2022.100512 · Cited by 36
“HAR systems can be categorized according to the modality of sensory data used [7,8] since the sorts of functionality, algorithms, designs, and methodologies used for research are greatly impacted by the type of sensory data used.”
A novel two-level interactive action recognition model based on inertial data fusion
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“Vision systems utilize cameras (RGB-D, infrared, color, webcams, etc.) to recognize and track the activities [11].”
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“Obtaining a high-quality set of robust features is important for the improvement of accuracy and reduction of computational cost as has been recognized in research studies [21, 22].”
BackgroundWang, X., Zhang, L., Huang, W., Wang, S., Wu, H., He, J., & Song, A. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3132088 · Cited by 30
“Traditional recognition algorithms such as decision tree, support vector machine and naive Bayes have been devoted to the design of shallow handcrafted features[8], which heavily rely on specific domain knowledge or human experience.”
BackgroundHuman Activity Recognition with an HMM-Based Generative Model
Manouchehri, N., & Bouguila, N. (2023) · Sensors · doi:10.3390/s23031390 · Cited by 33
Liang, J., Zhang, L., Bu, C., Cheng, D., Wu, H., & Song, A. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122180 · Cited by 32
A Smart Home Digital Twin to Support the Recognition of Activities of Daily Living
Bouchabou, D., Grosset, J., Nguyen, S. M., Lohr, C., & Puig, X. (2023) · Sensors · doi:10.3390/s23177586 · Cited by 38
“While HAR based on video data has made significant strides in performance [1], HAR in smart homes continues to encounter specific challenges, as highlighted in the survey by Bouchabou et al.”
MethodSmartphone-based bulky waste classification using convolutional neural networks
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“Recently, activity recognition has been underpinned for several IoT empowered applications, such as smart cities, surveillance systems, and smart farming [61].”
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“HAR is becoming a topic of great interest in the field of computer vision [21].”
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“These categories have been explored and discussed in the literature [4,5].”
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“This paper concentrates on sensor-based HAR, which mainly relies on data from IMUs typically embedded in wearable devices and smart-phones [70], [71], [72].”
MethodOpen Datasets in Human Activity Recognition Research—Issues and Challenges: A Review
Alam, G., McChesney, I., Nicholl, P., & Rafferty, J. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3317645 · Cited by 26
PatchHAR: A MLP-Like Architecture for Efficient Activity Recognition Using Wearables
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“Recently, the emergence of deep learning and increased computational power have provided an effective solution in HAR scenario [4].”
BackgroundTransformer-Based Recognition of Activities of Daily Living from Wearable Sensor Data
Augustinov, G., Nisar, M. A., Li, F., Tabatabaei, A., Grzegorzek, M., Sohrabi, K., & Fudickar, S. (2022) · International Workshop on Sensor-based Activity Recognition and Interaction (iWOAR) · doi:10.1145/3558884.3558895 · Cited by 26
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“These systems typically use sensors, cameras, or wearable devices to collect data and employ machine learning algorithms to recognize and interpret the patterns of daily living [44–46].”
BackgroundHADE: Exploiting Human Action Recognition Through Fine-Tuned Deep Learning Methods
Karim, M. F. A., Khalid, S., Aleryani, A., Tairan, N. M., Ali, Z., & Ali, F. (2024) · IEEE Access · doi:10.1109/access.2024.3378515 · Cited by 25
“This is particularly valuable in the security and healthcare fields [16], where the demand for immediate action is high, such as in secure monitoring systems aimed at thwarting potential security threats [17], [18], [19], [20], [21].”
BackgroundRecognizing Full-Body Exercise Execution Errors Using the Teslasuit
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“Human physical activity recognition (HPAR) is a subject of research that focuses on developing and experimenting with novel techniques for automatically recognizing activities via signals acquired by wearable or ambient sensors [1].”
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“On the other hand, DL models can work directly on raw data, but consist of several processing layers that include thousands of parameters [44].”
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“WBANs can be further categorized into wearable-based (e.g., Shimmer,BioSensing,G-Walk 1 ),andhybrid(i.e.,wearablesand camera-based, e.g., Vicon Nexus 2 ) [14] systems.”
BackgroundHuman Activity Recognition Using Machine Learning Algorithms Based on IMU Data
Nia, N. G., Kaplanoğlu, E., Nasab, A., & Qin, H. H. (2023) · International Conference on Bio-engineering for Smart Technologies (BioSMART) · doi:10.1109/biosmart58455.2023.10162095 · Cited by 25
“For example, they may not accurately capture certain activities involving subtle or complex movements, such as handwriting or playing musical instruments [4].”
BackgroundHuman Activity Recognition based on Transformer in Smart Home
Huang, X., & Zhang, S. (2023) · Asia Conference on Algorithms, Computing and Machine Learning (CACML) · doi:10.1145/3590003.3590100 · Cited by 24
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Light Residual Network for Human Activity Recognition using Wearable Sensor Data
Calatrava-Nicolás, F. M., & Mozos, O. M. (2023) · IEEE Sensors Letters · doi:10.1109/lsens.2023.3311623 · Cited by 22
“Human activity recognition (HAR) is the problem of identifying the activities carried out by a person by collecting and analyzing data from different cues [1], [2], such as wearable [3], [4] or environmental sensors [5], [6], [7].”
BackgroundMulti-modal lifelog data fusion for improved human activity recognition: A hybrid approach
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A Survey of Video Action Recognition Based on Deep Learning
Gong, P., & Luo, X. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113594 · Cited by 34
Karim, M. F. A., Khalid, S., Lee, S., Almutairi, S. S., Namoun, A., & Abohashrh, M. (2025) · IEEE Access · doi:10.1109/access.2025.3590073 · Cited by 23
“Hybrid systems combine multiple modalities (e.g., inertial + RF, capacitive sensing) for enhanced robustness and non-intrusive monitoring [26], [44], [45], [154], [155], [156], [157], [159].”
Yan, Y., Liu, Y.-S., Li, C., Wang, J., Ma, L., Xiong, J., Zhao, X., & Wang, L. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3142750 · Cited by 23
Label Flipping Data Poisoning Attack Against Wearable Human Activity Recognition System
Shahid, A. R., Imteaj, A., Wu, P. Y., Igoche, D., & Alam, T. (2022) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci51031.2022.10022015 · Cited by 22
“HAR systems [1] include accelerometer [2], magnetometer, gyroscopes, inertial measurement unit (IMU), electromyogram (EMG) [3], force-sensitive resistors (FSR) [4] and wearable wrist camera [5].”
MethodCai, W., Gao, M., Ding, Y., Ning, X., Bai, X. P., & Qian, P. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3320044 · Cited by 22
“In the past, both cloud and artificial intelligence technologies attempted to accelerate innovation by deriving actionable knowledge from data [2]; however, the volume and complexity of data generated by connected devices are unprecedented and already exceed the capacity of existing networks and…”
BackgroundYe, X., & Wang, K. I. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110811 · Cited by 22
MobileNet for human activity recognition in smart surveillance using transfer learning
Rani, M., & Kumar, M. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10882-z · Cited by 21
Thukral, M., Dhekane, S. G., Hiremath, S. K., Haresamudram, H., & Plötz, T. (2025) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3712278 · Cited by 27
Human activity recognition from UAV videos using a novel DMLC-CNN model
Sinha, K. P., & Kumar, P. (2023) · Image and Vision Computing · doi:10.1016/j.imavis.2023.104674 · Cited by 21
Mardanpour, M., Sepahvand, M., Abdali-Mohammadi, F., Nikouei, M., & Sarabi, H. (2023) · Information Sciences · doi:10.1016/j.ins.2023.119073 · Cited by 20
Xu, Q. Y., Wu, M., Li, X., Mao, K., & Chen, Z. (2023) · IEEE Transactions on Industrial Cyber-Physical Systems · doi:10.1109/ticps.2023.3320630 · Cited by 21
Self-Attention-Based Deep Convolution LSTM Framework for Sensor-Based Badminton Activity Recognition
Deng, J., Zhang, S., & Ma, J. (2023) · Sensors · doi:10.3390/s23208373 · Cited by 21
An eXplainable Self-Attention-Based Spatial–Temporal Analysis for Human Activity Recognition
Meena, T., & Sarawadekar, K. P. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3335449 · Cited by 22
“The existing research [4], [5] uses the two techniques: 1) manual feature extraction and 2) automatic feature extraction.”
MethodAn Empirical Comparison of Machine Learning Models for Student’s Mental Health Illness Assessment
Muzumdar, P., Basyal, G. P., & Vyas, P. (2022) · Asian Journal of Computer and Information Systems · doi:10.24203/ajcis.v10i1.6882 · Cited by 24
“In previous literature, tree-based algorithms like XGBoost have been found very effective in classifying sensor data in the field of human activity recognition (HAR) data [17].”
Impact of Wireless Sensor Data Mining with Hybrid Deep Learning for Human Activity Recognition
Nair, R., Ragab, M., Mujallid, O. A., Mohammad, K. A., Mansour, R. F., & Viju, G. K. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/9457536 · Cited by 20
“Actions in HAR are divided into two categories: primary and sophisticated [4].”
BackgroundDomingo, J. D., Gómez‐García‐Bermejo, J., & Zalama, E. (2022) · IEEE Access · doi:10.1109/access.2022.3186465 · Cited by 25
“HAR processing follows a sequence of steps: Pre-processing, including Background construction and Fore-ground extraction; Feature extraction, including Global and Local descriptors (e.g., SIFT) or Semantic representations (e.g., Pose estimation); and Learning process [14].”
MethodBlock-Wise Training Residual Networks on Multi-Channel Time Series for Human Activity Recognition
Teng, Q., Zhang, L., Tang, Y., Song, S., Wang, X., & He, J. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3085360 · Cited by 20
“SEnsor based human activity recognition (HAR) [1] [2] is a hotspot where various human activities are analyzed via embedded inertial sensors such as accelerometer and gyroscope.”
BackgroundPareek, P., & Thakkar, A. (2021) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03348-w · Cited by 20
Advancements in Context Recognition for Edge Devices and Smart Eyewear: Sensors and Applications
Palermo, F., Casciano, L., Demagh, L., Teliti, A., Antonello, N., Gervasoni, G., Shalby, H. H. Y., Paracchini, M. B. M., Mentasti, S., Quan, H., et al. (2025) · IEEE Access · doi:10.1109/access.2025.3555426 · Cited by 22
“On the other hand, sensor-based HAR uses data captured by wearable and environmental sensors [72], [73], such as IMUs (Section III-A1).”
Self-Attention Pooling-Based Long-Term Temporal Network for Action Recognition
Li, H., Huang, J., Zhou, M., Shi, Q., & Fei, Q. (2022) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2022.3145839 · Cited by 19
“With the rapid development of DL, especially CNN for image understanding, several DL approaches have been proposed for action recognition [12], [13].”
MethodBandX : An Intelligent IoT-band for Human Activity Recognition based on TinyML
Saha, B., Samanta, R., Ghosh, S. K., & Roy, R. B. (2023) · International Conference on Distributed Computing and Networking (ICDCN) · doi:10.1145/3571306.3571415 · Cited by 19
“In the survey [1], the authors cover a variety of”
BackgroundStojchevska, M., Brouwer, M. D., Courteaux, M., Ongenae, F., & Hoecke, S. V. (2023) · Sensors · doi:10.3390/s23104606 · Cited by 23
“Introduction Human activity recognition (HAR) is the task of automatically recognizing human activities from signals collected from different devices, such as wearable sensors, sensors embedded in the environment, or vision devices [1].”
BackgroundMulticlass autoencoder-based active learning for sensor-based human activity recognition
Park, H., Lee, G. H., Han, J., & Choi, J. K. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.09.029 · Cited by 22
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Shaik, T., Tao, X., Li, L., Xie, H., Dai, H., Zhao, F., & Yong, J. (2025) · Brain Informatics · doi:10.1186/s40708-025-00262-1 · Cited by 18
Human Fall Detection for Smart Home Caring using Yolo Networks
Bo, L. U. O. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.0140409 · Cited by 18
EdgeActNet: Edge Intelligence-Enabled Human Activity Recognition Using Radar Point Cloud
Luo, F., Khan, S., Li, A., Huang, Y., & Wu, K. (2023) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2023.3309938 · Cited by 20
“Current HAR techniques are categorized into two types: vision-and sensor-based techniques [1].”
MethodReal-Time Machine Learning for Human Activities Recognition Based on Wrist-Worn Wearable Devices
Alexan, A. I., Alexan, A. R., & Oniga, S. (2023) · Applied Sciences · doi:10.3390/app14010329 · Cited by 18
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“However, RNNs can be computationally demanding and may not fully capture long-range sequential dependencies [21], [24].”
BackgroundInfluentialA Review of State-of-the-Art Methodologies and Applications in Action Recognition
Zhao, L., Lin, Z., Sun, R., & Wang, A. (2024) · Electronics · doi:10.3390/electronics13234733 · Cited by 22
Knowledge memorization and generation for action recognition in still images
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Human-Machine Interaction for Autonomous Vehicles: A Review
Zhang, J., Shu, Y., & Yu, H. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-77626-8_13 · Cited by 23
Multiple-instance domain adaptation for cost-effective sensor-based human activity recognition
Prabono, A. G., Yahya, B. N., & Lee, S. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2022.03.006 · Cited by 17
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“As a result, alternative solutions have been studied in recent years, such as those based on the use of sensors, which can be positioned in the environment surrounding the user or directly worn by the same [10].”
BackgroundSelf-citationThe Lifespan of Human Activity Recognition Systems for Smart Homes
Hiremath, S. K., & Plötz, T. (2023) · Sensors · doi:10.3390/s23187729 · Cited by 17
“Recordings that capture the actions and interactions of the resident are then used to analyze the activities of daily living of the resident [39,43,48].”
BackgroundMethodApplication of Smart Insoles for Recognition of Activities of Daily Living: A Systematic Review
D’Arco, L., McCalmont, G., Wang, H., & Zheng, H. (2023) · ACM Transactions on Computing for Healthcare · doi:10.1145/3633785 · Cited by 18
“The approaches used can be classified into two main categories in terms of the devices used, i.e., vision-based HAR and sensor-based HAR [15].”
MethodSelf-citationA Review of Digital Pixel Sensors
Udoy, M. R. I., Alam, S., Islam, M. M., Jaiswal, A., & Aziz, A. (2025) · IEEE Access · doi:10.1109/access.2025.3526879 · Cited by 17
“Using vision sensors, robots can capture visual data and employ advanced algorithms, which enable them to do complex tasks and enhance safety [7].”
Complex Human Activity Recognition Using a Local Weighted Approach
Aşuroğlu, T. (2022) · IEEE Access · doi:10.1109/access.2022.3208686 · Cited by 16
Long-Term Human Trajectory Prediction Using 3D Dynamic Scene Graphs
Gorlo, N., Schmid, L., & Carlone, L. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3482169 · Cited by 20
“We note that when engineering an end-to-end system, a DSG, past trajectories and interactions could be estimated using approaches like [15], [47], [48], [49], but engineering such a system is currently out of scope for this work.”
MethodExploring Trends and Clusters in Human Posture Recognition Research: An Analysis Using CiteSpace
Yan, L., & Du, Y. R. (2025) · Sensors · doi:10.3390/s25030632 · Cited by 18
Skeleton-Based Human Pose Recognition Using Channel State Information: A Survey
Wang, Z., Ma, M., Feng, X., Li, X., Liu, F., Guo, Y., & Chen, D. (2022) · Sensors · doi:10.3390/s22228738 · Cited by 15
The Next Giant Leap for Space Human Factors: The Opportunities
Márquez, J. J., Landon, L. B., & Salas, E. (2023) · Human Factors The Journal of the Human Factors and Ergonomics Society · doi:10.1177/00187208231174955 · Cited by 19
Oubre, B., & Lee, S. I. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3337156 · Cited by 18
“Given the existence of various human activity recognition (HAR) algorithms [19], [20], one could detect a comprehensive set of activities of interest.”
BackgroundKhajuria, R., & Sarwar, A. (2023) · Micron · doi:10.1016/j.micron.2023.103583 · Cited by 15
Robust Human Activity Recognition via Wearable Sensors Using Dynamic Gaussian Kernel Learning
Wang, S., Sun, Y., Sha, Y., Yang, G., Cheng, D., Zhang, L., Wu, H., & Song, A. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3355704 · Cited by 16
“Sensor data often contains noise due to improper calibration, malfunctions, incorrect placement, noisy surrounding environments, and various activities [50].”
BackgroundCui, W., Wu, K., Wu, M., Li, X., & Chen, Z. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3398094 · Cited by 15
“As a result, HAR has garnered significant attention in recent years [1].”
BackgroundAdvancing Activity Recognition With Multimodal Fusion and Transformer Techniques
Aidarova, S., Nurmakhan, T., Myrzakhan, R., Fazli, S., & Yazıcı, A. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3555663 · Cited by 18
“One of the challenges of traditional RNNs is the vanishing gradient problem, which has been addressed by introducing long short-term memory (LSTM) networks [17], [18].”
Abdel‐Basset, M., Hawash, H., Moustafa, N., & Mohammad, N. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3117909 · Cited by 15
An Information Gain-Based Model and an Attention-Based RNN for Wearable Human Activity Recognition
Liu, L., He, J., Ren, K., Lungu, J., Hou, Y., & Dong, R. (2021) · Entropy · doi:10.3390/e23121635 · Cited by 16
“Traditionally, various classical machine learning algorithms [21], such as random forest [22], Bayesian network [23], Markov model [24], and support vector machine (SVM) [25], were used for analyzing wearable HAR data.”
MethodHarrison, A., Jester, A., Mouli, S., Fratini, A., & Jabran, A. (2024) · Sensors · doi:10.3390/s25010002 · Cited by 14
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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