A survey of depth and inertial sensor fusion for human action recognition
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Commonly employed multimodal data fusion methods within HMIP encompass data-level fusion, feature-level fusion, and decision-level fusion.
“As illustrated in Fig. 8, the commonly employed multimodal data fusion methods within HMIP currently encompass data-level fusion, feature-level fusion, and decision-level fusion 154 .”
From Recent advances in intelligent wearable systems: from multiscale biomechanical features towards human motion intent prediction (2026) · cites this paper as [154]
Cites
A Tutorial on Support Vector Machines for Pattern Recognition
Burges, C. J. C. (1998) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009715923555 · Cited by 16,437 · see citing works
A Mathematical Theory of Evidence
Shafer, G. R. (2020) · Princeton University Press eBooks · doi:10.2307/j.ctv10vm1qb · Cited by 11,786 · see citing works
Learning realistic human actions from movies
Ivan Laptev, A., Marszałek, M., Schmid, C., & Rozenfeld, B. (2008) · 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/cvpr.2008.4587756 · Cited by 3,481 · see citing works
A TUTORIAL ON SUPPORT VECTOR MACHINES FOR PATTERN RECOGNITION, DATA MINING AND KNOWLEDGE DISCOVERY
Burger, C. J. C. (1998) · Cited by 3,261 · see citing works
Recognizing human actions: a local SVM approach
Schüldt, C., Laptev, I., & Caputo, B. (2004) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2004.1334462 · Cited by 3,022 · see citing works
Recognizing human actions: A local SVM approach
Schüldt, C., Laptev, I., & Caputo, B. (2004) · Cited by 2,927 · see citing works
The recognition of human movement using temporal templates
Bobick, A., & Davis, J. W. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.910878 · Cited by 2,830 · see citing works
A Survey on Human Activity Recognition using Wearable Sensors
Lara, O. D., & Labrador, M. A. (2013) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2012.110112.00192 · Cited by 2,662 · see citing works
Ivan Laptev, A. (2005) · International Journal of Computer Vision · doi:10.1007/s11263-005-1838-7 · Cited by 2,631 · see citing works
A survey on vision-based human action recognition
Poppe, R. (2010) · Image and Vision Computing · doi:10.1016/j.imavis.2009.11.014 · Cited by 2,242 · see citing works
Aggarwal, J. K., & Ryoo, M. S. (2011) · ACM Computing Surveys · doi:10.1145/1922649.1922653 · Cited by 2,053 · see citing works
Real-time human pose recognition in parts from single depth images
Shotton, J., Sharp, T., Kipman, A., Fitzgibbon, A. W., Finocchio, M., Blake, A., Cook, M., & Moore, R. (2013) · Communications of the ACM · doi:10.1145/2398356.2398381 · Cited by 2,050 · see citing works
Sparse representation or collaborative representation: Which helps face recognition?
Lei Zhang, A., Yang, M., & Feng, X. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126277 · Cited by 2,003 · see citing works
A Spatio-Temporal Descriptor Based on 3D-Gradients
Klaeser, A., Marszałek, M., & Schmid, C. (2008) · British Machine Vision Conference (BMVC) · doi:10.5244/c.22.99 · Cited by 1,842 · see citing works
Visual interpretation of hand gestures for human-computer interaction: a review
Pavlović, V., Sharma, R., & Huang, T. S. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.598226 · Cited by 1,793 · see citing works
A tutorial on human activity recognition using body-worn inertial sensors
Bulling, A., Blanke, U., & Schiele, B. (2014) · ACM Computing Surveys · doi:10.1145/2499621 · Cited by 1,668 · see citing works
Mining actionlet ensemble for action recognition with depth cameras
Wang, J., Liu, Z., Wu, Y. N., & Yuan, J. (2012) · 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/cvpr.2012.6247813 · Cited by 1,556 · see citing works
Human Action Recognition by Representing 3D Skeletons as Points in a Lie Group
Vemulapalli, R., Arrate, F., & Chellappa, R. (2014) · 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/cvpr.2014.82 · Cited by 1,506 · see citing works
Action recognition based on a bag of 3D points
Li, W., Zhang, Z., & Liu, Z. (2010) · 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2010.5543273 · Cited by 1,478 · see citing works
Enhanced Computer Vision With Microsoft Kinect Sensor: A Review
Han, J., Shao, L., Xu, D., & Shotton, J. (2013) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2013.2265378 · Cited by 1,366 · see citing works
Sensor-Based Activity Recognition
Chen, L., Hoey, J., Nugent, C. D., Cook, D. J., & Yu, Z. (2012) · IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) · doi:10.1109/tsmcc.2012.2198883 · Cited by 1,074 · see citing works
Jovanov, E., Milenković, A., Otto, C., & de Groen, P. C. (2005) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-2-6 · Cited by 1,013 · see citing works
A survey of vision-based methods for action representation, segmentation and recognition
Weinland, D., Ronfard, R. P., & Boyer, E. (2010) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2010.10.002 · Cited by 987 · see citing works
HON4D: Histogram of Oriented 4D Normals for Activity Recognition from Depth Sequences
Oreifej, O., & Liu, Z. (2013) · 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/cvpr.2013.98 · Cited by 950 · see citing works
Chen, C., Jafari, R., & Kehtarnavaz, N. (2015) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2015.7350781 · Cited by 801 · see citing works
Self-citationOn feature combination for multiclass object classification
Gehler, P., & Nowozin, S. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459169 · Cited by 797 · see citing works
Incremental support vector learning for ordinal regression
Gu, B., Sheng, V. S., Tay, K. Y., Romano, W., & Li, S. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2014.2342533 · Cited by 707 · see citing works
Ermes, M., Pärkkä, J., Mäntyjärvi, J., & Korhonen, I. (2008) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2007.899496 · Cited by 707 · see citing works
Human fall detection on embedded platform using depth maps and wireless accelerometer
Kwolek, B., & Kepski, M. (2014) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2014.09.005 · Cited by 650 · see citing works
Accurate, Fast Fall Detection Using Gyroscopes and Accelerometer-Derived Posture Information
Li, Q., Stankovic, J. A., Hanson, M., Barth, A. T., Lach, J., & Zhou, G. (2009) · Wearable and Implantable Body Sensor Networks · doi:10.1109/bsn.2009.46 · Cited by 588 · see citing works
Human activity recognition from 3D data: A review
Aggarwal, J. K., & Xia, L. (2014) · Pattern Recognition Letters · doi:10.1016/j.patrec.2014.04.011 · Cited by 578 · see citing works
What is a hidden Markov model?
Eddy, S. R. (2004) · Nature Biotechnology · doi:10.1038/nbt1004-1315 · Cited by 563 · see citing works
Recognizing actions using depth motion maps-based histograms of oriented gradients
Yang, X., Zhang, C., & Tian, Y. (2012) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2393347.2396382 · Cited by 561 · see citing works
Berkeley MHAD: A comprehensive Multimodal Human Action Database
Ofli, F., Chaudhry, R., Kurillo, G., Vidal, R., & Bajcsy, R. K. (2013) · Proceedings · doi:10.1109/wacv.2013.6474999 · Cited by 490 · see citing works
Robust 3D Action Recognition with Random Occupancy Patterns
Wang, J., Liu, Z., Chorowski, J., Chen, Z., & Wu, Y. N. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33709-3_62 · Cited by 471 · see citing works
EigenJoints-based action recognition using Naïve-Bayes-Nearest-Neighbor
Yang, X., & Tian, Y. (2012) · 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2012.6239232 · Cited by 450 · see citing works
Combining embedded accelerometers with computer vision for recognizing food preparation activities
Stein, S., & McKenna, S. J. (2013) · Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing · doi:10.1145/2493432.2493482 · Cited by 448 · see citing works
Avci, A., Bosch, S., Marin-Perianu, M., Marin-Perianu, R., & Havinga, P. (2010) · University of Twente Research Information · Cited by 443 · see citing works
Spatio-temporal Depth Cuboid Similarity Feature for Activity Recognition Using Depth Camera
Xia, L., & Aggarwal, J. K. (2013) · 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/cvpr.2013.365 · Cited by 436 · see citing works
A survey of human motion analysis using depth imagery
Chen, L., Wei, H., & Ferryman, J. (2013) · Pattern Recognition Letters · doi:10.1016/j.patrec.2013.02.006 · Cited by 400 · see citing works
Super Normal Vector for Activity Recognition Using Depth Sequences
Yang, X., & Tian, Y. (2014) · 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/cvpr.2014.108 · Cited by 393 · see citing works
Construction of Tree Network with Limited Delivery Latency in Homogeneous Wireless Sensor Networks
Xie, S., & Wang, Y.-X. (2014) · Wireless Personal Communications · doi:10.1007/s11277-014-1748-5 · Cited by 353 · see citing works
Real-time human action recognition based on depth motion maps
Chen, C., Liu, K., & Kehtarnavaz, N. (2013) · Journal of Real-Time Image Processing · doi:10.1007/s11554-013-0370-1 · Cited by 315 · see citing works
Self-citationSTOP: Space-Time Occupancy Patterns for 3D Action Recognition from Depth Map Sequences
Vieira, A. W., Nascimento, E. R., Oliveira, G. L., et al. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33275-3_31 · Cited by 291 · see citing works
Human Activity Recognition Using Inertial/Magnetic Sensor Units
Altun, K., & Barshan, B. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-14715-9_5 · Cited by 283 · see citing works
Skeletal Quads: Human Action Recognition Using Joint Quadruples
Evangelidis, G., Singh, G., & Horaud, R. (2014) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2014.772 · Cited by 279 · see citing works
Improving Human Action Recognition Using Fusion of Depth Camera and Inertial Sensors
Chen, C., Jafari, R., & Kehtarnavaz, N. (2014) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2014.2362520 · Cited by 269 · see citing works
Self-citationTemporal segmentation and activity classification from first-person sensing
Spriggs, E., la Torre, F. D., & Hebert, M. (2009) · 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2009.5204354 · Cited by 234 · see citing works
A Survey on Human Motion Analysis from Depth Data
Ye, M., Zhang, Q., Wang, L., Zhu, J., Yang, R., & Gall, J. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-44964-2_8 · Cited by 216 · see citing works
Distributed recognition of human actions using wearable motion sensor networks
Yang, A. Y., Jafari, R., Sastry, S., & Bajcsy, R. (2009) · Journal of Ambient Intelligence and Smart Environments · doi:10.3233/ais-2009-0016 · Cited by 210 · see citing works
Self-citationAction Recognition from Depth Sequences Using Depth Motion Maps-Based Local Binary Patterns
Chen, C., Jafari, R., & Kehtarnavaz, N. (2015) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2015.150 · Cited by 207 · see citing works
Self-citationA Depth-Based Fall Detection System Using a Kinect® Sensor
Gasparrini, S., Cippitelli, E., Spinsante, S., & Gambi, E. (2014) · Sensors · doi:10.3390/s140202756 · Cited by 199 · see citing works
A Real-Time Human Action Recognition System Using Depth and Inertial Sensor Fusion
Chen, C., Jafari, R., & Kehtarnavaz, N. (2015) · IEEE Sensors Journal · doi:10.1109/jsen.2015.2487358 · Cited by 188 · see citing works
Self-citationFusion of Inertial and Depth Sensor Data for Robust Hand Gesture Recognition
Liu, K., Chen, C., Jafari, R., & Kehtarnavaz, N. (2014) · IEEE Sensors Journal · doi:10.1109/jsen.2014.2306094 · Cited by 163 · see citing works
Self-citationImproving fall detection by the use of depth sensor and accelerometer
Kwolek, B., & Kępski, M. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.05.061 · Cited by 150 · see citing works
Upper limb motion tracking with the integration of IMU and Kinect
Tian, Y., Meng, X., Tao, D., Liu, D., & Feng, C. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.01.071 · Cited by 120 · see citing works
Human Action Recognition With Video Data: Research and Evaluation Challenges
Ramanathan, M., Yau, W., & Teoh, E. K. (2014) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2014.2325871 · Cited by 111 · see citing works
RGBD-HuDaAct: A Color-Depth Video Database for Human Daily Activity Recognition
Ni, B., Wang, G., & Moulin, P. (2012) · Advances in computer vision and pattern recognition · doi:10.1007/978-1-4471-4640-7_10 · Cited by 109 · see citing works
Real-Time Body Tracking with One Depth Camera and Inertial Sensors
Helten, T., Müller, M., Seidel, H., & Theobalt, C. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.141 · Cited by 107 · see citing works
Gasparrini, S., Cippitelli, E., Gambi, E., et al. (2015) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-25733-4_11 · Cited by 104 · see citing works
3D human action segmentation and recognition using pose kinetic energy
Shan, J., & Akella, S. (2014) · doi:10.1109/arso.2014.7020983 · Cited by 97 · see citing works
A medication adherence monitoring system for pill bottles based on a wearable inertial sensor
Chen, C., Kehtarnavaz, N., & Jafari, R. (2014) · doi:10.1109/embc.2014.6944743 · Cited by 79 · see citing works
Self-citationLow-cost accurate skeleton tracking based on fusion of kinect and wearable inertial sensors.
Destelle, F., Ahmadi, A., O'Connor, N. E., Moran, K., Chatzitofis, A., Zarpalas, D., & Daras, P. (2014) · Arrow@dit (Dublin Institute of Technology) · Cited by 51 · see citing works
Ruffieux, S., Lalanne, D., & Mugellini, E. (2013) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2522848.2532590 · Cited by 51 · see citing works
Home-based Senior Fitness Test measurement system using collaborative inertial and depth sensors
Chen, C., Liu, K., Jafari, R., et al. (2014) · doi:10.1109/embc.2014.6944534 · Cited by 45 · see citing works
Self-citationReview of Sensor-based Activity Recognition Systems
Guan, D., Yuan, W., Sarkar, A. J., et al. (2011) · IETE Technical Review · doi:10.4103/0256-4602.85975 · Cited by 43 · see citing works
Recognizing Human Action at a Distance in Video by Key Poses
Mukherjee, S., Biswas, S. K., & Mukherjee, D. P. (2011) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2011.2135290 · Cited by 41 · see citing works
Time synchronization and data fusion for RGB-Depth cameras and inertial sensors in AAL applications
Cippitelli, E., Gasparrini, S., Gambi, E., et al. (2015) · doi:10.1109/iccw.2015.7247189 · Cited by 36 · see citing works
Multi-HMM classification for hand gesture recognition using two differing modality sensors
Liu, K., Chen, C., Jafari, R., et al. (2014) · IEEE Dallas Circuits and Systems Conference (DCAS) · doi:10.1109/dcas.2014.6965338 · Cited by 34 · see citing works
Self-citationGesture spotting and recognition using salience detection and concatenated hidden markov models
Yin, Y., & Davis, R. (2013) · ACM International Conference on Multimodal Interaction (ICMI) · doi:10.1145/2522848.2532588 · Cited by 31 · see citing works
Delachaux, B., Rebetez, J., Pérez-Uribe, A., & Satizábal, H. F. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-38682-4_25 · Cited by 30 · see citing works
Photometric stereo with an arbitrary number of illuminants
Argyriou, V., Petrou, M., & Barsky, S. (2010) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2010.05.002 · Cited by 25 · see citing works
Ubiquitous robotics in physical human action recognition: A comparison between dynamic ANNs and GP
Theodoridis, T., Agapitos, A., Hu, H., & Lucas, S. M. (2008) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2008.4543676 · Cited by 25 · see citing works
Bayesian Co-Boosting for Multi-modal Gesture Recognition
Wu, J., & Cheng, J. (2017) · The Springer series on challenges in machine learning · doi:10.1007/978-3-319-57021-1_14 · Cited by 24 · see citing works
Low power programmable architecture for periodic activity monitoring
Bidmeshki, M.-M., & Jafari, R. (2013) · ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS) · doi:10.1145/2502524.2502536 · Cited by 15 · see citing works
Self-citationMulti-modal learning for gesture recognition
Cao, C., Zhang, Y., & Lu, H. (2015) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2015.7177460 · Cited by 13 · see citing works
Enhanced Classification of Abnormal Gait Using BSN and Depth
Wong, C., McKeague, S., Correa, J., et al. (2012) · doi:10.1109/bsn.2012.26 · Cited by 12 · see citing works
Action recognition using invariant features under unexampled viewing conditions
Sun, L., & Aizawa, K. (2013) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2502081.2508126 · Cited by 12 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Sensor-based and vision-based human activity recognition: A comprehensive survey
Minh Dang, L., Min, K., Wang, H., Jalil Piran, M., Hee Lee, C., & Moon, H. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107561 · Cited by 730 · see citing works
Qiu, S., Zhao, H., Jiang, N., Wang, Z., Liu, L., An, Y., Zhao, H., Miao, X., Liu, R., & Fortino, G. (2022) · Information Fusion · doi:10.1016/j.inffus.2021.11.006 · Cited by 671 · see citing works
Demrozi, F., Pravadelli, G., Bihorac, A., & Rashidi, P. (2020) · IEEE Access · doi:10.1109/access.2020.3037715 · Cited by 368 · see citing works
A multi-sensor data fusion enabled ensemble approach for medical data from body sensor networks
Muzammal, M., Talat, R., Sodhro, A. H., & Pirbhulal, S. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.06.021 · Cited by 311 · see citing works
Steven Eyobu, O., & Han, D. S. (2018) · Sensors · doi:10.3390/s18092892 · Cited by 259 · see citing works
Panwar, M., Biswas, D., Bajaj, H., Jobges, M., Turk, R., Maharatna, K., & Acharyya, A. (2019) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2019.2899927 · Cited by 158 · see citing works
Action Recognition in Manufacturing Assembly using Multimodal Sensor Fusion
Al-Amin, M., Tao, W., Doell, D., Lingard, R., Yin, Z., Leu, M. C., & Qin, R. (2019) · Procedia Manufacturing · doi:10.1016/j.promfg.2020.01.288 · Cited by 85 · see citing works
Motion capture sensing techniques used in human upper limb motion: a review
Yahya, M., Shah, J. A., Kadir, K. A., Yusof, Z. M., Khan, S., & Warsi, A. (2019) · Sensor Review · doi:10.1108/sr-10-2018-0270 · Cited by 82 · see citing works
A Survey of Sensors in Healthcare Workflow Monitoring
Antunes, R. S., Seewald, L. A., Rodrigues, V. F., Costa, C. A. D., Jr., L. G., Righi, R. R., Maier, A., Eskofier, B., Ollenschläger, M., Naderi, F., et al. (2019) · ACM Computing Surveys · doi:10.1145/3177852 · Cited by 48 · see citing works
Data Fusion for Intelligent Crowd Monitoring and Management Systems: A Survey
Li, X., Yu, Q., Alzahrani, B., Barnawi, A., Alhindi, A., Alghazzawi, D., & Miao, Y. (2021) · IEEE Access · doi:10.1109/access.2021.3060631 · Cited by 49 · see citing works
Recognising complex activities with histograms of relative tracklets
Stein, S., & McKenna, S. J. (2017) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2016.08.012 · Cited by 11 · see citing works
Improved Accuracy in Predicting the Best Sensor Fusion Architecture for Multiple Domains
Molino-Minero-Re, E., Aguileta, A. A., Brena, R. F., & Garcia-Ceja, E. (2021) · Sensors · doi:10.3390/s21217007 · Cited by 11 · see citing works
Chen, S., Peng, C., Yang, B., Lin, J., Zhou, L., Jiang, Z., Liu, Z., Liu, Y., & Tang, L. (2026) · npj Artificial Intelligence · doi:10.1038/s44387-026-00083-5 · Cited by 9 · see citing works
- Authors
- Chen, C., Jafari, R., & Kehtarnavaz, N.
- Affiliations
- The University of Texas at Dallas (US)
- Texas A&M University (US)
- Corresponding author
- Chen, Chen
- Venue
- Multimedia Tools and Applications
- Published
- 2015-12-23
- Volume
- 76
- Issue
- 3
- Pages
- 4405-4425
- ISSN
- 1380-7501 (ISSN-L)
- 1573-7721
- OpenAlex
- W2274499208
- Cited by
- 356
- References
- 79
- Citations by year
- 2026: 9 · 2025: 24 · 2024: 24 · 2023: 27 · 2022: 33 · 2021: 51 · 2020: 54 · 2019: 46 · 2018: 58 · 2017: 19 · 2016: 11
- FWCI
- 23.38
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Multimedia Tools and Applications (publishedVersion)
- Funders
- University of Texas at Dallas
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
- Keywords
- human action recognition, depth camera, inertial sensors, sensor fusion, vision-inertial fusion, wearable sensors
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 ›
Are you an author, or do you see a problem with this page? Request a review