{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T03:23:04Z","timestamp":1742959384407,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":32,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819620531"},{"type":"electronic","value":"9789819620548"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-96-2054-8_4","type":"book-chapter","created":{"date-parts":[[2025,1,2]],"date-time":"2025-01-02T15:48:31Z","timestamp":1735832911000},"page":"44-57","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Novel Human Abnormal Posture Detection Method Based on\u00a0Spatial-Topological Feature Fusion of\u00a0Skeleton"],"prefix":"10.1007","author":[{"given":"Yuefeng","family":"Ma","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiqi","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Deheng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shiying","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,1,3]]},"reference":[{"issue":"11","key":"4_CR1","doi-asserted-by":"publisher","first-page":"12370","DOI":"10.1109\/JSEN.2023.3267300","volume":"23","author":"M Xu","year":"2023","unstructured":"Xu, M., Guo, L., Wu, H.C.: Robust abnormal human-posture recognition using OpenPose and multiview cross-information. IEEE Sens. J. 23(11), 12370\u201312379 (2023)","journal-title":"IEEE Sens. J."},{"issue":"2","key":"4_CR2","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1007\/s00530-024-01293-0","volume":"30","author":"X Yang","year":"2024","unstructured":"Yang, X., Zhang, S., Ji, W., Song, Y., He, L., Xue, H.: SMA-GCN: a fall detection method based on spatio-temporal relationship. Multimedia Syst. 30(2), 90 (2024)","journal-title":"Multimedia Syst."},{"key":"4_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2023.103739","volume":"233","author":"G Garcia-Cobo","year":"2023","unstructured":"Garcia-Cobo, G., SanMiguel, J.C.: Human skeletons and change detection for efficient violence detection in surveillance videos. Comput. Vis. Image Underst. 233, 103739 (2023)","journal-title":"Comput. Vis. Image Underst."},{"key":"4_CR4","doi-asserted-by":"crossref","unstructured":"Elmi, S., Bell, M., Tan, K.L.: Deep-Cogn: skeleton-based human action recognition for cognitive behavior assessment. In: 2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 692\u2013699. IEEE (2022)","DOI":"10.1109\/ICTAI56018.2022.00107"},{"key":"4_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121981","volume":"238","author":"A O\u011fuz","year":"2024","unstructured":"O\u011fuz, A., Ertu\u011frul, \u00d6.F.: Emotion recognition by skeleton-based spatial and temporal analysis. Expert Syst. Appl. 238, 121981 (2024)","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"4_CR6","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1109\/TPAMI.2019.2929257","volume":"43","author":"Z Cao","year":"2020","unstructured":"Cao, Z., Hidalgo, G., Simon, T., Wei, S., Sheikh, Y.: Openpose: realtime multi-person 2D pose estimation using part affinity fields. IEEE Trans. Pattern Anal. Mach. Intell. 43(1), 172\u2013186 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"6","key":"4_CR7","doi-asserted-by":"publisher","first-page":"7157","DOI":"10.1109\/TPAMI.2022.3222784","volume":"45","author":"HS Fang","year":"2022","unstructured":"Fang, H.S., et al.: Alphapose: whole-body regional multi-person pose estimation and tracking in real-time. IEEE Trans. Pattern Anal. Mach. Intell. 45(6), 7157\u20137173 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"19","key":"4_CR8","doi-asserted-by":"publisher","first-page":"9029","DOI":"10.3390\/app11199029","volume":"11","author":"Y Woo","year":"2021","unstructured":"Woo, Y., et al.: Classification of diabetic walking for senior citizens and personal home training system using single RGB camera through machine learning. Appl. Sci. 11(19), 9029 (2021)","journal-title":"Appl. Sci."},{"issue":"3","key":"4_CR9","doi-asserted-by":"publisher","first-page":"3551","DOI":"10.32604\/cmc.2024.047336","volume":"78","author":"X Li","year":"2024","unstructured":"Li, X., Yu, H., Tian, S., Lin, F., Masood, U.: Multi-branch high-dimensional guided transformer-based 3D human posture estimation. Comput. Mater. Continua 78(3), 3551\u20133564 (2024)","journal-title":"Comput. Mater. Continua"},{"key":"4_CR10","doi-asserted-by":"publisher","first-page":"822","DOI":"10.1016\/j.neucom.2022.06.024","volume":"501","author":"M Wang","year":"2022","unstructured":"Wang, M., Li, X., Zhang, X., Zhang, Y.: Hierarchical graph attention network with pseudo-metapath for skeleton-based action recognition. Neurocomputing 501, 822\u2013833 (2022)","journal-title":"Neurocomputing"},{"issue":"1","key":"4_CR11","doi-asserted-by":"publisher","first-page":"537","DOI":"10.2298\/CSIS220131067G","volume":"20","author":"G Yi","year":"2023","unstructured":"Yi, G., Wu, H., Wu, X., Li, Z., Zhao, X.: Human action recognition based on skeleton features. Comput. Sci. Inf. Syst. 20(1), 537\u2013550 (2023)","journal-title":"Comput. Sci. Inf. Syst."},{"issue":"1","key":"4_CR12","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1109\/TCSVT.2021.3134410","volume":"33","author":"X Zeng","year":"2021","unstructured":"Zeng, X., Jiang, Y., Ding, W., Li, H., Hao, Y., Qiu, Z.: A hierarchical spatio-temporal graph convolutional neural network for anomaly detection in videos. IEEE Trans. Circuits Syst. Video Technol. 33(1), 200\u2013212 (2021)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"4_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109398","volume":"138","author":"N Li","year":"2023","unstructured":"Li, N., Chang, F., Liu, C.: Human-related anomalous event detection via memory-augmented Wasserstein generative adversarial network with gradient penalty. Pattern Recogn. 138, 109398 (2023)","journal-title":"Pattern Recogn."},{"issue":"10","key":"4_CR14","doi-asserted-by":"publisher","first-page":"1133","DOI":"10.3390\/bioengineering10101133","volume":"10","author":"K Jun","year":"2023","unstructured":"Jun, K., Lee, K., Lee, S., Lee, H., Kim, M.S.: Hybrid deep neural network framework combining skeleton and gait features for pathological gait recognition. Bioengineering 10(10), 1133 (2023)","journal-title":"Bioengineering"},{"key":"4_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106374","volume":"123","author":"L Li","year":"2023","unstructured":"Li, L., Yang, G., Li, Y., Zhu, D., He, L.: Abnormal sitting posture recognition based on multi-scale spatiotemporal features of skeleton graph. Eng. Appl. Artif. Intell. 123, 106374 (2023)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"4_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2020.100338","volume":"39","author":"AK Ingale","year":"2021","unstructured":"Ingale, A.K., et al.: Real-time 3D reconstruction techniques applied in dynamic scenes: a systematic literature review. Comput. Sci. Rev. 39, 100338 (2021)","journal-title":"Comput. Sci. Rev."},{"key":"4_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neucom.2020.10.037","volume":"423","author":"H Wang","year":"2021","unstructured":"Wang, H., Yu, B., Xia, K., Li, J., Zuo, X.: Skeleton edge motion networks for human action recognition. Neurocomputing 423, 1\u201312 (2021)","journal-title":"Neurocomputing"},{"key":"4_CR18","doi-asserted-by":"crossref","unstructured":"Sun, K., Xiao, B., Liu, D., Wang, J.: Deep high-resolution representation learning for human pose estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5693\u20135703 (2019)","DOI":"10.1109\/CVPR.2019.00584"},{"key":"4_CR19","doi-asserted-by":"crossref","unstructured":"Xiao, B., Wu, H., Wei, Y.: Simple baselines for human pose estimation and tracking. In: Proceedings of the European conference on computer vision (ECCV), pp. 466\u2013481 (2018)","DOI":"10.1007\/978-3-030-01231-1_29"},{"issue":"1\u20132","key":"4_CR20","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1002\/nav.3800020109","volume":"2","author":"HW Kuhn","year":"1955","unstructured":"Kuhn, H.W.: The Hungarian method for the assignment problem. Naval Res. Logist. Q. 2(1\u20132), 83\u201397 (1955)","journal-title":"Naval Res. Logist. Q."},{"issue":"1","key":"4_CR21","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1137\/0105003","volume":"5","author":"J Munkres","year":"1957","unstructured":"Munkres, J.: Algorithms for the assignment and transportation problems. J. Soc. Ind. Appl. Math. 5(1), 32\u201338 (1957)","journal-title":"J. Soc. Ind. Appl. Math."},{"issue":"4","key":"4_CR22","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1145\/263867.263872","volume":"44","author":"M Stoer","year":"1997","unstructured":"Stoer, M., Wagner, F.: A simple min-cut algorithm. J. ACM (JACM) 44(4), 585\u2013591 (1997)","journal-title":"J. ACM (JACM)"},{"issue":"6","key":"4_CR23","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1037\/h0042519","volume":"65","author":"F Rosenblatt","year":"1958","unstructured":"Rosenblatt, F.: The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65(6), 386 (1958)","journal-title":"Psychol. Rev."},{"key":"4_CR24","doi-asserted-by":"crossref","unstructured":"Cortes, C.: Support-vector networks. Machine Learning (1995)","DOI":"10.1007\/BF00994018"},{"key":"4_CR25","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1007\/BF00116251","volume":"1","author":"JR Quinlan","year":"1986","unstructured":"Quinlan, J.R.: Induction of decision trees. Machine Learn. 1, 81\u2013106 (1986)","journal-title":"Machine Learn."},{"key":"4_CR26","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Machine Learn. 45, 5\u201332 (2001)","journal-title":"Machine Learn."},{"issue":"11","key":"4_CR27","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"issue":"2","key":"4_CR28","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1207\/s15516709cog1402_1","volume":"14","author":"JL Elman","year":"1990","unstructured":"Elman, J.L.: Finding structure in time. Cogn. Sci. 14(2), 179\u2013211 (1990)","journal-title":"Cogn. Sci."},{"key":"4_CR29","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1109\/TIP.2021.3130545","volume":"31","author":"J Li","year":"2021","unstructured":"Li, J., Huang, Q., Du, Y., Zhen, X., Chen, S., Shao, L.: Variational abnormal behavior detection with motion consistency. IEEE Trans. Image Process. 31, 275\u2013286 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"4_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.116168","volume":"190","author":"S Chandrakala","year":"2022","unstructured":"Chandrakala, S., Deepak, K., Vignesh, L.: Bag-of-event-models based embeddings for detecting anomalies in surveillance videos. Expert Syst. Appl. 190, 116168 (2022)","journal-title":"Expert Syst. Appl."},{"issue":"8","key":"4_CR31","doi-asserted-by":"publisher","first-page":"8066","DOI":"10.1109\/TITS.2023.3263586","volume":"24","author":"R Zhao","year":"2023","unstructured":"Zhao, R., et al.: Abnormal behavior detection based on dynamic pedestrian centroid model: case study on u-turn and fall-down. IEEE Trans. Intell. Transp. Syst. 24(8), 8066\u20138078 (2023)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"4_CR32","doi-asserted-by":"publisher","first-page":"332","DOI":"10.1016\/j.neucom.2019.12.148","volume":"444","author":"W Luo","year":"2021","unstructured":"Luo, W., Liu, W., Gao, S.: Normal graph: spatial temporal graph convolutional networks based prediction network for skeleton based video anomaly detection. Neurocomputing 444, 332\u2013337 (2021)","journal-title":"Neurocomputing"}],"container-title":["Lecture Notes in Computer Science","MultiMedia Modeling"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-2054-8_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,23]],"date-time":"2025-03-23T01:41:11Z","timestamp":1742694071000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-2054-8_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819620531","9789819620548"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-2054-8_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"3 January 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MMM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Multimedia Modeling","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nara","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 January 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 January 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mmm2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/mmm2025.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}