{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T10:26:35Z","timestamp":1743071195014,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031434266"},{"type":"electronic","value":"9783031434273"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-43427-3_24","type":"book-chapter","created":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T21:01:41Z","timestamp":1694898101000},"page":"394-409","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["MCTN: A Multi-Channel Temporal Network for\u00a0Wearable Fall Prediction"],"prefix":"10.1007","author":[{"given":"Jiawei","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaohu","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guorui","family":"Liao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,17]]},"reference":[{"key":"24_CR1","doi-asserted-by":"publisher","first-page":"193060","DOI":"10.1109\/ACCESS.2020.3032497","volume":"8","author":"LL Blunda","year":"2020","unstructured":"Blunda, L.L., Guti\u00e9rrez-Madro\u00f1al, L., Wagner, M.F., Medina-Bulo, I.: A wearable fall detection system based on body area networks. IEEE Access 8, 193060\u2013193074 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3032497","journal-title":"IEEE Access"},{"key":"24_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00371-021-02283-3","volume":"38","author":"SK Challa","year":"2021","unstructured":"Challa, S.K., Kumar, A., Semwal, V.B.: A multibranch CNN-BiLSTM model for human activity recognition using wearable sensor data. Visual Comput. 38, 1\u201315 (2021). https:\/\/doi.org\/10.1007\/s00371-021-02283-3","journal-title":"Visual Comput."},{"issue":"5","key":"24_CR3","doi-asserted-by":"publisher","first-page":"1911","DOI":"10.3390\/s22051911","volume":"22","author":"I Dirgov\u00e1 Lupt\u00e1kov\u00e1","year":"2022","unstructured":"Dirgov\u00e1 Lupt\u00e1kov\u00e1, I., Kubov\u010d\u00edk, M., Posp\u00edchal, J.: Wearable sensor-based human activity recognition with transformer model. Sensors 22(5), 1911 (2022). https:\/\/doi.org\/10.3390\/s22051911","journal-title":"Sensors"},{"issue":"2","key":"24_CR4","doi-asserted-by":"publisher","first-page":"511","DOI":"10.3233\/JCM-180806","volume":"18","author":"M Hemmatpour","year":"2018","unstructured":"Hemmatpour, M., Ferrero, R., Gandino, F., Montrucchio, B., Rebaudengo, M.: Internet of Things for fall prediction and prevention. J. Comput. Methods Sci. Eng. 18(2), 511\u2013518 (2018). https:\/\/doi.org\/10.3233\/JCM-180806","journal-title":"J. Comput. Methods Sci. Eng."},{"issue":"10","key":"24_CR5","doi-asserted-by":"publisher","first-page":"1812","DOI":"10.1109\/TNSRE.2017.2687100","volume":"25","author":"J Howcroft","year":"2017","unstructured":"Howcroft, J., Kofman, J., Lemaire, E.D.: Prospective fall-risk prediction models for older adults based on wearable sensors. IEEE Trans. Neural Syst. Rehabil. Eng. 25(10), 1812\u20131820 (2017). https:\/\/doi.org\/10.1109\/TNSRE.2017.2687100","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"issue":"5","key":"24_CR6","doi-asserted-by":"publisher","first-page":"1277","DOI":"10.3390\/s20051277","volume":"20","author":"H Jung","year":"2020","unstructured":"Jung, H., Koo, B., Kim, J., Kim, T., Nam, Y., Kim, Y.: Enhanced algorithm for the detection of preimpact fall for wearable airbags. Sensors 20(5), 1277 (2020). https:\/\/doi.org\/10.3390\/s20051277","journal-title":"Sensors"},{"issue":"21","key":"24_CR7","doi-asserted-by":"publisher","first-page":"6126","DOI":"10.3390\/s20216126","volume":"20","author":"TH Kim","year":"2020","unstructured":"Kim, T.H., Choi, A., Heo, H.M., Kim, H., Mun, J.H.: Acceleration magnitude at impact following loss of balance can be estimated using deep learning model. Sensors 20(21), 6126 (2020). https:\/\/doi.org\/10.3390\/s20216126","journal-title":"Sensors"},{"key":"24_CR8","doi-asserted-by":"publisher","unstructured":"Kim, W., Son, B., Kim, I.: ViLT: vision-and-language transformer without convolution or region supervision (2021). https:\/\/doi.org\/10.48550\/arXiv.2102.03334","DOI":"10.48550\/arXiv.2102.03334"},{"issue":"4","key":"24_CR9","doi-asserted-by":"publisher","first-page":"72","DOI":"10.3390\/technologies8040072","volume":"8","author":"D Kraft","year":"2020","unstructured":"Kraft, D., Srinivasan, K., Bieber, G.: Deep learning based fall detection algorithms for embedded systems, smartwatches, and iot devices using accelerometers. Technologies 8(4), 72 (2020). https:\/\/doi.org\/10.3390\/technologies8040072","journal-title":"Technologies"},{"issue":"15","key":"24_CR10","doi-asserted-by":"publisher","first-page":"4192","DOI":"10.3390\/s20154192","volume":"20","author":"L Liu","year":"2020","unstructured":"Liu, L., Hou, Y., He, J., Lungu, J., Dong, R.: An energy-efficient fall detection method based on FD-DNN for elderly people. Sensors 20(15), 4192 (2020). https:\/\/doi.org\/10.3390\/s20154192","journal-title":"Sensors"},{"issue":"3","key":"24_CR11","doi-asserted-by":"publisher","first-page":"1276","DOI":"10.1109\/TETC.2020.3027454","volume":"9","author":"M Musci","year":"2021","unstructured":"Musci, M., De Martini, D., Blago, N., Facchinetti, T., Piastra, M.: Online fall detection using recurrent neural networks on smart wearable devices. IEEE Trans. Emerg. Topics Comput. 9(3), 1276\u20131289 (2021). https:\/\/doi.org\/10.1109\/TETC.2020.3027454","journal-title":"IEEE Trans. Emerg. Topics Comput."},{"issue":"22","key":"24_CR12","doi-asserted-by":"publisher","first-page":"6479","DOI":"10.3390\/s20226479","volume":"20","author":"L Palmerini","year":"2020","unstructured":"Palmerini, L., Klenk, J., Becker, C., Chiari, L.: Accelerometer-based fall detection using machine learning: training and testing on real-world falls. Sensors 20(22), 6479 (2020). https:\/\/doi.org\/10.3390\/s20226479","journal-title":"Sensors"},{"issue":"5","key":"24_CR13","doi-asserted-by":"publisher","first-page":"995","DOI":"10.1109\/TNSRE.2019.2911602","volume":"27","author":"W Saadeh","year":"2019","unstructured":"Saadeh, W., Butt, S.A., Altaf, M.A.B.: A patient-specific single sensor IoT-based wearable fall prediction and detection system. IEEE Trans. Neural Syst. Rehabil. Eng. 27(5), 995\u20131003 (2019). https:\/\/doi.org\/10.1109\/TNSRE.2019.2911602","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"issue":"1","key":"24_CR14","doi-asserted-by":"publisher","first-page":"198","DOI":"10.3390\/s17010198","volume":"17","author":"A Sucerquia","year":"2017","unstructured":"Sucerquia, A., L\u00f3pez, J.D., Vargas-Bonilla, J.F.: SisFall: a fall and movement dataset. Sensors 17(1), 198 (2017). https:\/\/doi.org\/10.3390\/s17010198","journal-title":"Sensors"},{"issue":"7","key":"24_CR15","doi-asserted-by":"publisher","first-page":"1678","DOI":"10.1109\/TNSRE.2020.2999505","volume":"28","author":"T Triwiyanto","year":"2020","unstructured":"Triwiyanto, T., Pawana, I.P.A., Purnomo, M.H.: An improved performance of deep learning based on convolution neural network to classify the hand motion by evaluating hyper parameter. IEEE Trans. Neural Syst. Rehabil. Eng. 28(7), 1678\u20131688 (2020). https:\/\/doi.org\/10.1109\/TNSRE.2020.2999505","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"24_CR16","doi-asserted-by":"publisher","unstructured":"Vaswani, A., et al.: Attention is all you need (2017). https:\/\/doi.org\/10.48550\/arXiv.1706.03762","DOI":"10.48550\/arXiv.1706.03762"},{"key":"24_CR17","doi-asserted-by":"publisher","unstructured":"Vavoulas, G., Chatzaki, C., Malliotakis, T., Pediaditis, M., Tsiknakis, M.: The MobiAct dataset: recognition of activities of daily living using smartphones. In: International Conference on Information and Communication Technologies for Ageing Well and E-Health, vol. 2, pp. 143\u2013151. SCITEPRESS (2016). https:\/\/doi.org\/10.5220\/0005792401430151","DOI":"10.5220\/0005792401430151"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43427-3_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T21:04:29Z","timestamp":1694898269000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43427-3_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031434266","9783031434273"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43427-3_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"17 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"Here is an ethical statement for a fall prediction experiment to ensure that data collection follows ethical principles:\u2013 The privacy of the subjects is required to maintain confidentiality, and all data and information collected during the experiment will not be disclosed to the public or provided to non-experimental personnel.\u2013 Before the experiment, we provided detailed explanations to the individuals participating in the experiment and obtained their informed consent, including informing them of the purpose, methods, and potential risks of the experiment.\u2013 Participants in the experiment received appropriate protection and care, and risks and inconveniences during the experiment were minimized as much as possible. When conducting the experiment, we evaluated the impact of the experiment on the health and safety of the participants and took necessary measures to reduce these risks.\u2013 After data collection is complete, we take full responsibility for data processing and analysis, ensuring that the various relationships within the data are clearly explained. Additionally, when publishing or using experimental data, we consider the sensitivity and privacy of the data and place the protection of participant privacy at the forefront.We hereby confirm that we strictly adhered to the above-mentioned ethical principles in the fall prediction experiment and protected the rights and dignity of the subjects.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Statement"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Turin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2023.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"829","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"196","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.63","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4.5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Applied Data Science Track: 239 submissions, 58 accepted papers; Demo Track: 31 submissions, 16 accepted papers.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}