{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T17:25:41Z","timestamp":1742923541993,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031199578"},{"type":"electronic","value":"9783031199585"}],"license":[{"start":{"date-parts":[[2022,10,21]],"date-time":"2022-10-21T00:00:00Z","timestamp":1666310400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,10,21]],"date-time":"2022-10-21T00:00:00Z","timestamp":1666310400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-19958-5_16","type":"book-chapter","created":{"date-parts":[[2022,10,20]],"date-time":"2022-10-20T11:06:59Z","timestamp":1666264019000},"page":"162-172","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Fall Detection System Based on Pose Estimation in Videos"],"prefix":"10.1007","author":[{"given":"Nguyen Tan","family":"Cam","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nguyen","family":"Van Nhinh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tran Huyen","family":"Trang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,10,21]]},"reference":[{"key":"16_CR1","unstructured":"\u201cFalls\u201d, Who.int, 2021. [Online]. Available: https:\/\/www.who.int\/newsroom\/fact-sheets\/detail\/falls. Accessed 01 Jul 2022"},{"key":"16_CR2","unstructured":"\u201cAgeing and health\u201d, Who.int, 2021. [Online]. Available: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/ageing-and-health. Accessed: 01 Jul 2022"},{"key":"16_CR3","doi-asserted-by":"publisher","first-page":"71","DOI":"10.3389\/frobt.2020.00071","volume":"7","author":"X Wang","year":"2020","unstructured":"Wang, X., Ellul, J., Azzopardi, G.: Elderly fall detection systems: a literature survey. Frontiers in Robotics and AI 7, 71 (2020)","journal-title":"Frontiers in Robotics and AI"},{"issue":"5","key":"16_CR4","doi-asserted-by":"publisher","first-page":"744","DOI":"10.3390\/sym12050744","volume":"12","author":"W Chen","year":"2020","unstructured":"Chen, W., Jiang, Z., Guo, H., Ni, X.: Fall detection based on key points of human-skeleton using OpenPose. Symmetry 12(5), 744 (2020)","journal-title":"Symmetry"},{"key":"16_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2019.123205","volume":"540","author":"Q Xu","year":"2020","unstructured":"Xu, Q., Huang, G., Yu, M., Guo, Y.: Fall prediction based on key points of human bones. Physica A 540, 123205 (2020)","journal-title":"Physica A"},{"key":"16_CR6","doi-asserted-by":"publisher","first-page":"103443","DOI":"10.1109\/ACCESS.2020.2999503","volume":"8","author":"B Wang","year":"2020","unstructured":"Wang, B., Yu, J., Wang, K., Bao, X., Mao, K.: Fall detection based on dual-channel feature integration. IEEE Access 8, 103443\u2013103453 (2020)","journal-title":"IEEE Access"},{"key":"16_CR7","doi-asserted-by":"crossref","unstructured":"Hasan, M., Islam, M., Abdullah, S.: Robust pose-based human fall detection using recurrent neural network. In: 2019 IEEE International Conference on Robotics, Automation, Artificial-intelligence and Internet-of-Things (RAAICON), pp. 48\u201351","DOI":"10.1109\/RAAICON48939.2019.23"},{"key":"16_CR8","doi-asserted-by":"crossref","unstructured":"Sun, G., Wang, Z.: Fall detection algorithm for the elderly based on human posture estimation. In: 2020 Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC), pp. 172\u2013176 (2020)","DOI":"10.1109\/IPEC49694.2020.9114962"},{"key":"16_CR9","doi-asserted-by":"crossref","unstructured":"Hua, M., Nan, Y., Lian, S.: Falls prediction based on body keypoints and seq2seq architecture. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops, p. 0 (2019)","DOI":"10.1109\/ICCVW.2019.00158"},{"key":"16_CR10","doi-asserted-by":"publisher","first-page":"129965","DOI":"10.1109\/ACCESS.2021.3113824","volume":"9","author":"W Chang","year":"2021","unstructured":"Chang, W., Hsu, C., Chen, L.: A pose estimation-based fall detection methodology using artificial intelligence edge computing. IEEE Access 9, 129965\u2013129976 (2021)","journal-title":"IEEE Access"},{"key":"16_CR11","doi-asserted-by":"crossref","unstructured":"Ramirez, H., Velastin, S., Fabregas, E., Meza, I., Makris, D., Farias, G.: Fall detection using human skeleton features. In: 11th International Conference of Pattern Recognition Systems (ICPRS 2021), pp.73\u201378 (2021)","DOI":"10.1049\/icp.2021.1465"},{"key":"16_CR12","doi-asserted-by":"crossref","unstructured":"Xu, C., et al.: Fall detection in elevator cages based on XGBoost and LSTM. In: 2021 26th International Conference on Automation and Computing (ICAC), pp. 1\u20136 (2021)","DOI":"10.23919\/ICAC50006.2021.9594123"},{"key":"16_CR13","doi-asserted-by":"crossref","unstructured":"Ren, X., Zhang, Y., Yang, Y.: Human fall detection model with lightweight network and tracking in video. In: 5th International Conference on Computer Science and Artificial Intelligence, pp. 1\u20137 (2021)","DOI":"10.1145\/3507548.3507549"},{"key":"16_CR14","first-page":"1","volume":"2021","author":"M Fatima","year":"2021","unstructured":"Fatima, M., Yousaf, M., Yasin, A., Velastin, S.: Unsupervised fall detection approach using human skeletons. International Conference on Robotics and Automation in Industry 2021, 1\u20136 (2021)","journal-title":"International Conference on Robotics and Automation in Industry"},{"key":"16_CR15","doi-asserted-by":"crossref","unstructured":"Apicella, A., Snidaro, L.: Deep neural networks for real-time remote fall detection. In: International Conference on Pattern Recognition, pp. 188\u2013201 (2021)","DOI":"10.1007\/978-3-030-68790-8_16"},{"key":"16_CR16","doi-asserted-by":"crossref","unstructured":"Mamchur, N., Shakhovska, N., Gregus ml, M.: Person Fall detection system based on video stream analysis. Procedia Computer Science 198, 676\u2013681 (2022)","DOI":"10.1016\/j.procs.2021.12.305"},{"key":"16_CR17","doi-asserted-by":"crossref","unstructured":"Inturi, A., Manikandan, V., Garrapally, V.: A novel vision-BasedFall detection scheme using keypoints of human skeleton with long short-term memory network. Arabian Journal for Science and Engineering 1\u201313 (2022)","DOI":"10.1007\/s13369-022-06684-x"},{"issue":"2","key":"16_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4018\/IJSI.289600","volume":"10","author":"H Kang","year":"2022","unstructured":"Kang, H., Kang, Y., Kim, J.: Improved fall detection model on GRU using PoseNet. Int. J. Softw. Innova. 10(2), 1\u201311 (2022)","journal-title":"Int. J. Softw. Innova."},{"issue":"9","key":"16_CR19","doi-asserted-by":"publisher","first-page":"870","DOI":"10.1016\/j.medengphy.2015.06.009","volume":"37","author":"R Igual","year":"2015","unstructured":"Igual, R., Medrano, C., Plaza, I.: A comparison of public datasets for acceleration-based fall detection. Medical Engineering Physics 37(9), 870\u2013878 (2015)","journal-title":"Medical Engineering Physics"},{"key":"16_CR20","doi-asserted-by":"crossref","unstructured":"Casilari, E., Santoyo-Ra\u00f3m, J., Cano-Garc\u00eda, J.: Analysis of public datasets for wearable fall detection systems. Sensors 17(7), 1513 (2017)","DOI":"10.3390\/s17071513"},{"key":"16_CR21","doi-asserted-by":"crossref","unstructured":"Guti\u00e9rrez, J., Rodr\u00edguez, V., Martin, S.: Comprehensive review of vision-based fall detection systems. Sensors 21(3), 947 (2021)","DOI":"10.3390\/s21030947"},{"key":"16_CR22","doi-asserted-by":"crossref","unstructured":"Charfi, I., Miteran, J., Dubois, J., Atri, M., Tourki, R.: Definition and performance evaluation of a robust SVM based fall detection solution. In: 2012 eighth international conference on signal image technology and internet based systems, pp. 218\u2013224 (2012)","DOI":"10.1109\/SITIS.2012.155"},{"key":"16_CR23","doi-asserted-by":"crossref","unstructured":"Kepski, M., Kwolek, B.: Embedded system for fall detection using body-worn accelerometer and depth sensor. In: 8th International Conference on Intelligen Data Acquisition and Advanced Computing Systems: Technology and Applications, vol. 2, pp. 755\u2013759 (2015)","DOI":"10.1109\/IDAACS.2015.7341404"},{"key":"16_CR24","unstructured":"MoveNet.SinglePose: Storage.googleapis.com. [Online]. Available: https:\/\/storage.googleapis.com\/movenet\/MoveNet.SinglePose%20Model%20Card.pdf. Accessed: 01 Jul 2022"}],"container-title":["Lecture Notes in Networks and Systems","Intelligent Computing &amp; Optimization"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-19958-5_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,20]],"date-time":"2022-10-20T11:50:52Z","timestamp":1666266652000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-19958-5_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,21]]},"ISBN":["9783031199578","9783031199585"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-19958-5_16","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2022,10,21]]},"assertion":[{"value":"21 October 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICO","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing & Optimization","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hua Hin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Thailand","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ico2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.icico.info\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}