{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:30:22Z","timestamp":1775068222494,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2020,9,11]],"date-time":"2020-09-11T00:00:00Z","timestamp":1599782400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In contemporary research on human action recognition, most methods separately consider the movement features of each joint. However, they ignore that human action is a result of integrally cooperative movement of each joint. Regarding the problem, this paper proposes an action feature representation, called Motion Collaborative Spatio-Temporal Vector (MCSTV) and Motion Spatio-Temporal Map (MSTM). MCSTV comprehensively considers the integral and cooperative between the motion joints. MCSTV weighted accumulates limbs\u2019 motion vector to form a new vector to account for the movement features of human action. To describe the action more comprehensively and accurately, we extract key motion energy by key information extraction based on inter-frame energy fluctuation, project the energy to three orthogonal axes and stitch them in temporal series to construct the MSTM. To combine the advantages of MSTM and MCSTV, we propose Multi-Target Subspace Learning (MTSL). MTSL projects MSTM and MCSTV into a common subspace and makes them complement each other. The results on MSR-Action3D and UTD-MHAD show that our method has higher recognition accuracy than most existing human action recognition algorithms.<\/jats:p>","DOI":"10.3390\/s20185180","type":"journal-article","created":{"date-parts":[[2020,9,11]],"date-time":"2020-09-11T09:05:16Z","timestamp":1599815116000},"page":"5180","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Integrally Cooperative Spatio-Temporal Feature Representation of Motion Joints for Action Recognition"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6595-8573","authenticated-orcid":false,"given":"Xin","family":"Chao","sequence":"first","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3572-1460","authenticated-orcid":false,"given":"Zhenjie","family":"Hou","sequence":"additional","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiuzhen","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianjin","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1109\/TPAMI.2012.59","article-title":"3D Convolutional Neural Networks for Human Action Recognition","volume":"35","author":"Ji","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"25063","DOI":"10.1007\/s11042-019-7593-5","article-title":"Action recognition using weighted fusion of depth images and skeleton\u2019s key frames","volume":"78","author":"Xu","year":"2019","journal-title":"Multimed. Tools Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1109\/THMS.2014.2362520","article-title":"Improving human action recognition using fusion of depth camera and inertial sensors","volume":"45","author":"Chen","year":"2015","journal-title":"IEEE Trans. Hum. Mach. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3601","DOI":"10.1007\/s13042-019-00947-0","article-title":"Multi-level features fusion and selection for human gait recognition: An optimized framework of Bayesian model and binomial distribution","volume":"10","author":"Arshad","year":"2019","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Li, W.Q., Zhang, Z.Y., and Liu, Z.C. (2010, January 13\u201318). Action recognition based on a bag of 3D points. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition\u2014Workshops, San Francisco, CA, USA.","DOI":"10.1109\/CVPRW.2010.5543273"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Oreifej, O., and Liu, Z.C. (2013, January 23\u201328). Histogram of oriented 4D normals for activity recognition from depth sequences. Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.98"},{"key":"ref_7","unstructured":"Yang, X.D., Zhang, C.Y., and Tian, Y.L. (November, January 29). Recognizing actions using depth motion maps-based histograms of oriented gradients. Proceedings of the 20th ACM International Conference on Multimedia, Machinery, New York, NY, USA."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Xia, L., Chen, C.C., and Aggarwal, J.K. (2012, January 16\u201321). View invariant human action recognition using histograms of 3D joints. Proceedings of the 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Providence, RI, USA.","DOI":"10.1109\/CVPRW.2012.6239233"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Vemulapalli, R., Arrate, F., and Chellappa, R. (2014, January 23\u201328). Human action recognition by representing 3D skeletons as points in a lie group. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.82"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.patrec.2017.02.001","article-title":"Learning features combination for human action recognition from skeleton sequences","volume":"99","author":"Luvizon","year":"2017","journal-title":"Pattern Recognit. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2639","DOI":"10.1162\/0899766042321814","article-title":"Canonical correlation analysis: An overview with application to learning methods","volume":"16","author":"Hardoon","year":"2004","journal-title":"Neural Comput."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1984","DOI":"10.1109\/TIFS.2016.2569061","article-title":"Discriminant correlation analysis: Real-time feature level fusion for multimodal biometric recognition","volume":"11","author":"Haghighat","year":"2016","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Xi, X., Tang, M., Miran, S.M., and Luo, Z. (2017). Evaluation of Feature Extraction and Recognition for Activity Monitoring and Fall Detection Based on Wearable sEMG Sensors. Sensors, 17.","DOI":"10.3390\/s17061229"},{"key":"ref_14","first-page":"329","article-title":"Supervised Band Selection Using Local Spatial Information for Hyperspectral Image","volume":"13","author":"Cao","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_15","unstructured":"Hossain, M.R.I., and Little, J.J. (2017, January 21\u201326). Exploiting Temporal Information for 3D Human Pose Estimation. Proceedings of the Computer Vision and Pattern Recognition, Honolulu, HI, USA."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lv, F.J., and Nevatia, R. (2006, January 7\u201313). Recognition and Segmentation of 3-D Human Action Using HMM and Multi-class AdaBoost. Proceedings of the European Conference on Computer Vision, Graz, Austria.","DOI":"10.1007\/11744085_28"},{"key":"ref_17","unstructured":"Hussein, M.E., Torki, M., Gowayyed, M.A., and El-Saban, M. (2013, January 3\u20139). Human action recognition using a temporal hierarchy of covariance descriptors on 3D joint locations. Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, Beijing, China."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zanfir, M., Leordeanu, M., and Sminchisescu, C. (2013, January 1\u20138). The moving pose: An efficient 3d kinematics descriptor for low-latency action recognition and detection. Proceedings of the 2013 IEEE International Conference on Computer Vision, Sydney, NSW, Australia.","DOI":"10.1109\/ICCV.2013.342"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/34.910878","article-title":"The recognition of human movement using temporal templates","volume":"23","author":"Bobick","year":"2001","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Chen, C., Jafari, R., and Kehtarnavaz, N. (2015, January 5\u20139). Action recognition from depth sequences using depth motion maps-based local binary patterns. Proceedings of the 2015 IEEE Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV.2015.150"},{"key":"ref_21","first-page":"613","article-title":"Gradient local auto-correlations and extreme learning machine for depth-based activity recognition","volume":"9474","author":"Chen","year":"2015","journal-title":"Int. Symp. Vis. Comput."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4648","DOI":"10.1109\/TIP.2017.2718189","article-title":"Action recognition using 3d histograms of texture and a multi-class boosting classifier","volume":"26","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wang, J., Liu, Z.C., Chorowski, J., Chen, Z.Y., and Wu, Y. (2012, January 7\u201313). Robust 3D Action Recognition with Random Occupancy Patterns. Proceedings of the European Conference on Computer Vision, Florence, Italy.","DOI":"10.1007\/978-3-642-33709-3_62"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Xia, L., and Aggarwal, J.K. (2013, January 23\u201328). Spatio-temporal depth cuboid similarity feature for activity recognition using depth camera. Proceedings of the Computer Vision and Pattern Recognition (CVPR), Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.365"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Vieira, A.W., Nascimento, E.R., Oliveira, G.L., Liu, Z.C., and Campos, M.F.M. (2012, January 3\u20136). STOP: Space-time occupancy patterns for 3D action recognition from depth map sequences. Proceedings of the CIARP 2012: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, Buenos Aires, Argentina.","DOI":"10.1007\/978-3-642-33275-3_31"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2192","DOI":"10.1109\/TKDE.2012.217","article-title":"Binary and multi-class group sparse canonical correlation analysis for feature extraction and classification","volume":"25","author":"Zhang","year":"2013","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2010","DOI":"10.1109\/TPAMI.2015.2505311","article-title":"Joint feature selection and subspace learning for cross-modal retrieval","volume":"38","author":"Wang","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1049\/iet-ipr.2014.0986","article-title":"Robust medical image watermarking technique for accurate detection of tampers inside region of interest and recovering original region of interest","volume":"9","author":"Eswaraiah","year":"2015","journal-title":"IET Image Process."},{"key":"ref_29","unstructured":"He, R., Tan, T.N., Wang, L., and Zheng, W.S. (2012, January 16\u201321). \u211321 Regularized correntropy for robust feature selection. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Chen, C., Jafari, R., and Kehtarnavaz, N. (2015, January 27\u201330). Utd-mhad: A multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor. Proceedings of the 2015 IEEE International Conference on Image Processing (ICIP), Quebec City, QC, Canada.","DOI":"10.1109\/ICIP.2015.7350781"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3052","DOI":"10.1109\/TIP.2019.2955561","article-title":"Tangent Fisher Vector on Matrix Manifolds for Action Recognition","volume":"29","author":"Luo","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3407","DOI":"10.1016\/j.patcog.2015.04.025","article-title":"A spatial-temporal framework based on histogram of gradients and optical flow for facial expression recognition in video sequences","volume":"48","author":"Fan","year":"2015","journal-title":"Pattern Recognit."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1109\/LSP.2014.2326399","article-title":"Blind Image Quality Assessment Using the Joint Statistics of Generalized Local Binary Pattern","volume":"22","author":"Zhang","year":"2015","journal-title":"IEEE Signal Process. Lett."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/18\/5180\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:08:57Z","timestamp":1760177337000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/18\/5180"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,11]]},"references-count":33,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["s20185180"],"URL":"https:\/\/doi.org\/10.3390\/s20185180","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,11]]}}}