{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,21]],"date-time":"2026-06-21T14:37:33Z","timestamp":1782052653607,"version":"3.54.5"},"publisher-location":"Singapore","reference-count":39,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819601240","type":"print"},{"value":"9789819601257","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T00:00:00Z","timestamp":1731369600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T00:00:00Z","timestamp":1731369600000},"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\/978-981-96-0125-7_6","type":"book-chapter","created":{"date-parts":[[2024,11,17]],"date-time":"2024-11-17T03:07:18Z","timestamp":1731812838000},"page":"62-75","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Video-Audio Multimodal Fall Detection Method"],"prefix":"10.1007","author":[{"given":"Mahtab","family":"Jamali","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul","family":"Davidsson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Reza","family":"Khoshkangini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Radu-Casian","family":"Mihailescu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elin","family":"Sexton","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Viktor","family":"Johannesson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonas","family":"Tillstr\u00f6m","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,11,12]]},"reference":[{"key":"6_CR1","doi-asserted-by":"publisher","first-page":"118681","DOI":"10.1016\/j.eswa.2022.118681","volume":"212","author":"M Amsaprabhaa","year":"2023","unstructured":"Amsaprabhaa, M., et al.: Multimodal spatiotemporal skeletal kinematic gait feature fusion for vision-based fall detection. Expert Syst. Appl. 212, 118681 (2023)","journal-title":"Expert Syst. Appl."},{"key":"6_CR2","doi-asserted-by":"crossref","unstructured":"Apicella, A., Snidaro, L.: Deep neural networks for real-time remote fall detection. In: Pattern Recognition. ICPR International Workshops and Challenges: Virtual Event, January 10\u201315, 2021, Proceedings, Part II. pp. 188\u2013201. Springer (2021)","DOI":"10.1007\/978-3-030-68790-8_16"},{"key":"6_CR3","unstructured":"Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)"},{"key":"6_CR4","doi-asserted-by":"crossref","unstructured":"Carneiro, S.A., da\u00a0Silva, G.P., Leite, G.V., Moreno, R., Guimaraes, S.J.F., Pedrini, H.: Multi-stream deep convolutional network using high-level features applied to fall detection in video sequences. In: 2019 International Conference on Systems, Signals and Image Processing (IWSSIP), pp. 293\u2013298. IEEE (2019)","DOI":"10.1109\/IWSSIP.2019.8787213"},{"key":"6_CR5","doi-asserted-by":"crossref","unstructured":"Chamle, M., Gunale, K., Warhade, K.: Automated unusual event detection in video surveillance. In: 2016 International Conference on Inventive Computation Technologies (ICICT), vol.\u00a02, pp.\u00a01\u20134. IEEE (2016)","DOI":"10.1109\/INVENTIVE.2016.7824826"},{"key":"6_CR6","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. IEEE (2012)","DOI":"10.1109\/SITIS.2012.155"},{"issue":"4","key":"6_CR7","doi-asserted-by":"publisher","first-page":"1073","DOI":"10.1109\/JBHI.2015.2425932","volume":"20","author":"M Cheffena","year":"2015","unstructured":"Cheffena, M.: Fall detection using smartphone audio features. IEEE J. Biomed. Health Inform. 20(4), 1073\u20131080 (2015)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"6_CR8","doi-asserted-by":"crossref","unstructured":"Dai, B., Yang, D., Ai, L., Zhang, P.: A novel video-surveillance-based algorithm of fall detection. In: 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), pp.\u00a01\u20136. IEEE (2018)","DOI":"10.1109\/CISP-BMEI.2018.8633160"},{"key":"6_CR9","doi-asserted-by":"crossref","unstructured":"Dai, W., Dai, C., Qu, S., Li, J., Das, S.: Very deep convolutional neural networks for raw waveforms. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 421\u2013425. IEEE (2017)","DOI":"10.1109\/ICASSP.2017.7952190"},{"key":"6_CR10","doi-asserted-by":"crossref","unstructured":"D\u2019mello, S.K., Kory, J.: A review and meta-analysis of multimodal affect detection systems. ACM Comput. Surv. (CSUR) 47(3), 1\u201336 (2015)","DOI":"10.1145\/2682899"},{"issue":"6","key":"6_CR11","doi-asserted-by":"publisher","first-page":"2305","DOI":"10.1007\/s00371-022-02416-2","volume":"39","author":"K Fei","year":"2023","unstructured":"Fei, K., Wang, C., Zhang, J., Liu, Y., Xie, X., Tu, Z.: Flow-pose net: an effective two-stream network for fall detection. Vis. Comput. 39(6), 2305\u20132320 (2023)","journal-title":"Vis. Comput."},{"key":"6_CR12","doi-asserted-by":"crossref","unstructured":"Golik, P., T\u00fcske, Z., Schl\u00fcter, R., Ney, H.: Convolutional neural networks for acoustic modeling of raw time signal in LVCSR. In: Sixteenth Annual Conference of the International Speech Communication Association (2015)","DOI":"10.21437\/Interspeech.2015-6"},{"key":"6_CR13","doi-asserted-by":"crossref","unstructured":"Gonzalez, R.C.: Digital image processing. Pearson education India (2009)","DOI":"10.1117\/1.3115362"},{"key":"6_CR14","doi-asserted-by":"publisher","first-page":"114966","DOI":"10.1109\/ACCESS.2019.2936320","volume":"7","author":"F Harrou","year":"2019","unstructured":"Harrou, F., Zerrouki, N., Sun, Y., Houacine, A.: An integrated vision-based approach for efficient human fall detection in a home environment. IEEE Access 7, 114966\u2013114974 (2019)","journal-title":"IEEE Access"},{"key":"6_CR15","doi-asserted-by":"crossref","unstructured":"Hasan, M.M., Islam, M.S., 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. IEEE (2019)","DOI":"10.1109\/RAAICON48939.2019.23"},{"issue":"8","key":"6_CR16","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"issue":"2","key":"6_CR17","doi-asserted-by":"publisher","first-page":"1143","DOI":"10.1007\/s13369-022-06684-x","volume":"48","author":"AR Inturi","year":"2023","unstructured":"Inturi, A.R., Manikandan, V., Garrapally, V.: A novel vision-based fall detection scheme using keypoints of human skeleton with long short-term memory network. Arab. J. Sci. Eng. 48(2), 1143\u20131155 (2023)","journal-title":"Arab. J. Sci. Eng."},{"key":"6_CR18","doi-asserted-by":"crossref","unstructured":"Jiao, S., Li, G., Zhang, G., Zhou, J., Li, J.: Multimodal fall detection for solitary individuals based on audio-video decision fusion processing. Heliyon 10(8) (2024)","DOI":"10.1016\/j.heliyon.2024.e29596"},{"key":"6_CR19","doi-asserted-by":"publisher","first-page":"100340","DOI":"10.1016\/j.smhl.2022.100340","volume":"26","author":"P Kaur","year":"2022","unstructured":"Kaur, P., Wang, Q., Shi, W.: Fall detection from audios with audio transformers. Smart Health 26, 100340 (2022)","journal-title":"Smart Health"},{"issue":"3","key":"6_CR20","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.cmpb.2014.09.005","volume":"117","author":"B Kwolek","year":"2014","unstructured":"Kwolek, B., Kepski, M.: Human fall detection on embedded platform using depth maps and wireless accelerometer. Comput. Methods Programs Biomed. 117(3), 489\u2013501 (2014)","journal-title":"Comput. Methods Programs Biomed."},{"key":"6_CR21","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1016\/j.neucom.2015.05.061","volume":"168","author":"B Kwolek","year":"2015","unstructured":"Kwolek, B., Kepski, M.: Improving fall detection by the use of depth sensor and accelerometer. Neurocomputing 168, 637\u2013645 (2015)","journal-title":"Neurocomputing"},{"key":"6_CR22","doi-asserted-by":"crossref","unstructured":"Li, K., et al.: Uniformer: unifying convolution and self-attention for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 45(10), 12581\u201312600 (2023)","DOI":"10.1109\/TPAMI.2023.3282631"},{"issue":"2","key":"6_CR23","doi-asserted-by":"publisher","first-page":"314","DOI":"10.3390\/rs10020314","volume":"10","author":"X Lu","year":"2018","unstructured":"Lu, X., et al.: Three-dimensional physical and optical characteristics of aerosols over central china from long-term calipso and hysplit data. Remote Sens. 10(2), 314 (2018)","journal-title":"Remote Sens."},{"key":"6_CR24","doi-asserted-by":"publisher","first-page":"107937","DOI":"10.1016\/j.engappai.2024.107937","volume":"132","author":"A N\u00fa\u00f1ez-Marcos","year":"2024","unstructured":"N\u00fa\u00f1ez-Marcos, A., Arganda-Carreras, I.: Transformer-based fall detection in videos. Eng. Appl. Artif. Intell. 132, 107937 (2024)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"6_CR25","doi-asserted-by":"crossref","unstructured":"Poonsri, A., Chiracharit, W.: Fall detection using gaussian mixture model and principle component analysis. In: 2017 9th International Conference on Information Technology and Electrical Engineering (ICITEE), pp.\u00a01\u20134. IEEE (2017)","DOI":"10.1109\/ICITEED.2017.8250441"},{"key":"6_CR26","doi-asserted-by":"crossref","unstructured":"Poonsri, A., Chiracharit, W.: Improvement of fall detection using consecutive-frame voting. In: 2018 International Workshop on Advanced Image Technology (IWAIT), pp.\u00a01\u20134. IEEE (2018)","DOI":"10.1109\/IWAIT.2018.8369696"},{"key":"6_CR27","doi-asserted-by":"crossref","unstructured":"Popescu, M., Mahnot, A.: Acoustic fall detection using one-class classifiers. In: 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3505\u20133508. IEEE (2009)","DOI":"10.1109\/IEMBS.2009.5334521"},{"key":"6_CR28","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/j.inffus.2017.02.003","volume":"37","author":"S Poria","year":"2017","unstructured":"Poria, S., Cambria, E., Bajpai, R., Hussain, A.: A review of affective computing: from unimodal analysis to multimodal fusion. Inf. Fusion 37, 98\u2013125 (2017)","journal-title":"Inf. Fusion"},{"key":"6_CR29","doi-asserted-by":"crossref","unstructured":"Pratt, W.K.: Digital Image Processing: PIKS Scientific Inside, vol.\u00a04. Wiley Online Library (2007)","DOI":"10.1002\/0470097434"},{"issue":"5","key":"6_CR30","doi-asserted-by":"publisher","first-page":"1533","DOI":"10.3390\/s24051533","volume":"24","author":"A Shokrollahi","year":"2024","unstructured":"Shokrollahi, A., Persson, J.A., Malekian, R., Sarkheyli-H\u00e4gele, A., Karlsson, F.: Passive infrared sensor-based occupancy monitoring in smart buildings: a review of methodologies and machine learning approaches. Sensors 24(5), 1533 (2024)","journal-title":"Sensors"},{"key":"6_CR31","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818\u20132826 (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"6_CR32","doi-asserted-by":"publisher","first-page":"103443","DOI":"10.1109\/ACCESS.2020.2999503","volume":"8","author":"BH Wang","year":"2020","unstructured":"Wang, B.H., Yu, J., Wang, K., Bao, X.Y., Mao, K.M.: Fall detection based on dual-channel feature integration. IEEE Access 8, 103443\u2013103453 (2020)","journal-title":"IEEE Access"},{"key":"6_CR33","doi-asserted-by":"crossref","unstructured":"Wang, K., Cao, G., Meng, D., Chen, W., Cao, W.: Automatic fall detection of human in video using combination of features. In: 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1228\u20131233. IEEE (2016)","DOI":"10.1109\/BIBM.2016.7822694"},{"key":"6_CR34","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. Front. Robot. AI 7, 71 (2020)","journal-title":"Front. Robot. AI"},{"issue":"8","key":"6_CR35","doi-asserted-by":"publisher","first-page":"2122","DOI":"10.1007\/s11263-023-01784-z","volume":"131","author":"Y Wang","year":"2023","unstructured":"Wang, Y., et al.: Multi-modal 3d object detection in autonomous driving: a survey. Int. J. Comput. Vision 131(8), 2122\u20132152 (2023)","journal-title":"Int. J. Comput. Vision"},{"key":"6_CR36","unstructured":"World Health Organization: Falls. https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/falls (2024). [Accessed 04 June 2024]"},{"key":"6_CR37","doi-asserted-by":"crossref","unstructured":"Youssfi\u00a0Alaoui, A., Tabii, Y., Oulad Haj\u00a0Thami, R., Daoudi, M., Berretti, S., Pala, P.: Fall detection of elderly people using the manifold of positive semidefinite matrices. J. Imaging 7(7), 109 (2021)","DOI":"10.3390\/jimaging7070109"},{"key":"6_CR38","doi-asserted-by":"crossref","unstructured":"Yu, M., Gong, L., Kollias, S.: Computer vision based fall detection by a convolutional neural network. In: Proceedings of the 19th ACM international conference on multimodal interaction, pp. 416\u2013420 (2017)","DOI":"10.1145\/3136755.3136802"},{"key":"6_CR39","doi-asserted-by":"crossref","unstructured":"Zheng, H., et\u00a0al.: Lightweight fall detection algorithm based on Alphapose optimization model and ST-GCN. Math. Probl. Eng. 2022 (2022)","DOI":"10.1155\/2022\/9962666"}],"container-title":["Lecture Notes in Computer Science","PRICAI 2024: Trends in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-0125-7_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,17]],"date-time":"2024-11-17T04:27:59Z","timestamp":1731817679000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-0125-7_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,12]]},"ISBN":["9789819601240","9789819601257"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-0125-7_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,12]]},"assertion":[{"value":"12 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific Rim International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kyoto","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":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 November 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 November 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pricai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.pricai.org\/2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}