{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T17:33:56Z","timestamp":1768671236404,"version":"3.49.0"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031490170","type":"print"},{"value":"9783031490187","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T00:00:00Z","timestamp":1701043200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T00:00:00Z","timestamp":1701043200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-49018-7_28","type":"book-chapter","created":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T23:02:21Z","timestamp":1701039741000},"page":"391-405","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Analysis and\u00a0Impact of\u00a0Training Set Size in\u00a0Cross-Subject Human Activity Recognition"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1189-5079","authenticated-orcid":false,"given":"Miguel","family":"Matey-Sanz","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4338-4334","authenticated-orcid":false,"given":"Joaqu\u00edn","family":"Torres-Sospedra","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9016-9357","authenticated-orcid":false,"given":"Alberto","family":"Gonz\u00e1lez-P\u00e9rez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0572-5716","authenticated-orcid":false,"given":"Sven","family":"Casteleyn","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1004-9695","authenticated-orcid":false,"given":"Carlos","family":"Granell","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,27]]},"reference":[{"issue":"4","key":"28_CR1","doi-asserted-by":"publisher","first-page":"6474","DOI":"10.3390\/s140406474","volume":"14","author":"O Banos","year":"2014","unstructured":"Banos, O., et al.: Window size impact in human activity recognition. Sensors 14(4), 6474\u20136499 (2014). https:\/\/doi.org\/10.3390\/s140406474","journal-title":"Sensors"},{"key":"28_CR2","doi-asserted-by":"publisher","unstructured":"Chen, H., et\u00a0al.: Assessing impacts of data volume and data set balance in using deep learning approach to human activity recognition. In: IEEE International Conference on Bioinformatics and Biomedicine, pp. 1160\u20131165. IEEE (2017). https:\/\/doi.org\/10.1109\/BIBM.2017.8217821","DOI":"10.1109\/BIBM.2017.8217821"},{"issue":"3","key":"28_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3478119","volume":"5","author":"W Chen","year":"2021","unstructured":"Chen, W., et al.: Sensecollect: we need efficient ways to collect on-body sensor-based human activity data! Proc. ACM Interact. Mobile Wearable Ubiquitous Technol. 5(3), 1\u201327 (2021). https:\/\/doi.org\/10.1145\/3478119","journal-title":"ACM Interact. Mobile Wearable Ubiquitous Technol."},{"issue":"15","key":"28_CR4","doi-asserted-by":"publisher","first-page":"8771","DOI":"10.1109\/JSEN.2020.2982744","volume":"20","author":"G Coviello","year":"2020","unstructured":"Coviello, G., Avitabile, G.: Multiple synchronized inertial measurement unit sensor boards platform for activity monitoring. IEEE Sens. J. 20(15), 8771\u20138777 (2020). https:\/\/doi.org\/10.1109\/JSEN.2020.2982744","journal-title":"IEEE Sens. J."},{"key":"28_CR5","doi-asserted-by":"publisher","first-page":"59192","DOI":"10.1109\/ACCESS.2018.2873502","volume":"6","author":"E De-La-Hoz-Franco","year":"2018","unstructured":"De-La-Hoz-Franco, E., Ariza-Colpas, P., Quero, J.M., Espinilla, M.: Sensor-based datasets for human activity recognition-a systematic review of literature. IEEE Access 6, 59192\u201359210 (2018). https:\/\/doi.org\/10.1109\/ACCESS.2018.2873502","journal-title":"IEEE Access"},{"key":"28_CR6","doi-asserted-by":"publisher","first-page":"210816","DOI":"10.1109\/ACCESS.2020.3037715","volume":"8","author":"F Demrozi","year":"2020","unstructured":"Demrozi, F., et al.: Human activity recognition using inertial, physiological and environmental sensors: a comprehensive survey. IEEE Access 8, 210816\u2013210836 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3037715","journal-title":"IEEE Access"},{"key":"28_CR7","doi-asserted-by":"publisher","first-page":"133982","DOI":"10.1109\/ACCESS.2020.3010715","volume":"8","author":"D Gholamiangonabadi","year":"2020","unstructured":"Gholamiangonabadi, D., Kiselov, N., Grolinger, K.: Deep neural networks for human activity recognition with wearable sensors: leave-one-subject-out cross-validation for model selection. IEEE Access 8, 133982\u2013133994 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3010715","journal-title":"IEEE Access"},{"issue":"2","key":"28_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5121\/ijdkp.2015.5201","volume":"5","author":"M Hossin","year":"2015","unstructured":"Hossin, M., Sulaiman, M.N.: A review on evaluation metrics for data classification evaluations. International journal of data mining & knowledge management process 5(2), 1 (2015). https:\/\/doi.org\/10.5121\/ijdkp.2015.5201","journal-title":"International journal of data mining & knowledge management process"},{"key":"28_CR9","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.1052","volume":"8","author":"M Ja\u00e9n-Vargas","year":"2022","unstructured":"Ja\u00e9n-Vargas, M., et al.: Effects of sliding window variation in the performance of acceleration-based human activity recognition using deep learning models. PeerJ Comput. Sci. 8, e1052 (2022). https:\/\/doi.org\/10.7717\/peerj-cs.1052","journal-title":"PeerJ Comput. Sci."},{"issue":"3","key":"28_CR10","doi-asserted-by":"publisher","first-page":"1192","DOI":"10.1109\/SURV.2012.110112.00192","volume":"15","author":"OD Lara","year":"2012","unstructured":"Lara, O.D., Labrador, M.A.: A survey on human activity recognition using wearable sensors. IEEE Commun. Surv. Tutor. 15(3), 1192\u20131209 (2012). https:\/\/doi.org\/10.1109\/SURV.2012.110112.00192","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"28_CR11","doi-asserted-by":"publisher","unstructured":"Leightley, D., Darby, J., Li, B., McPhee, J.S., Yap, M.H.: Human activity recognition for physical rehabilitation. In: IEEE International Conference on Systems, Man, and Cybernetics, pp. 261\u2013266 (2013). https:\/\/doi.org\/10.1109\/SMC.2013.51","DOI":"10.1109\/SMC.2013.51"},{"issue":"3","key":"28_CR12","doi-asserted-by":"publisher","first-page":"1191","DOI":"10.1109\/JSEN.2019.2946095","volume":"20","author":"H Li","year":"2019","unstructured":"Li, H., Shrestha, A., Heidari, H., Le Kernec, J., Fioranelli, F.: Bi-lstm network for multimodal continuous human activity recognition and fall detection. IEEE Sens. J. 20(3), 1191\u20131201 (2019). https:\/\/doi.org\/10.1109\/JSEN.2019.2946095","journal-title":"IEEE Sens. J."},{"key":"28_CR13","doi-asserted-by":"crossref","unstructured":"Mann, H.B., Whitney, D.R.: On a test of whether one of two random variables is stochastically larger than the other. The annals of mathematical statistics, pp. 50\u201360 (1947)","DOI":"10.1214\/aoms\/1177730491"},{"key":"28_CR14","doi-asserted-by":"publisher","unstructured":"Matey-Sanz, M.: Reproducible Package for Analysis and Impact of Training Set Size in Cross-Subject Human Activity Recognition (Jul 2023). https:\/\/doi.org\/10.5281\/zenodo.8163542","DOI":"10.5281\/zenodo.8163542"},{"key":"28_CR15","doi-asserted-by":"publisher","unstructured":"Matey-Sanz, M., et\u00a0al.: Instrumented timed up and go test using inertial sensors from consumer wearable devices. In: 20th International Conference on Artificial Intelligence in Medical, Proceedings, pp. 144\u2013154. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-09342-5_14","DOI":"10.1007\/978-3-031-09342-5_14"},{"issue":"10","key":"28_CR16","doi-asserted-by":"publisher","first-page":"1482","DOI":"10.1109\/26.103043","volume":"39","author":"DL Mills","year":"1991","unstructured":"Mills, D.L.: Internet time synchronization: the network time protocol. IEEE Trans. Commun. 39(10), 1482\u20131493 (1991). https:\/\/doi.org\/10.1109\/26.103043","journal-title":"IEEE Trans. Commun."},{"key":"28_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1007\/3-540-36592-3_7","volume-title":"Computer Vision Systems","author":"N Mo\u00ebnne-Loccoz","year":"2003","unstructured":"Mo\u00ebnne-Loccoz, N., Br\u00e9mond, F., Thonnat, M.: Recurrent bayesian network for the recognition of human behaviors from video. In: Crowley, J.L., Piater, J.H., Vincze, M., Paletta, L. (eds.) ICVS 2003. LNCS, vol. 2626, pp. 68\u201377. Springer, Heidelberg (2003). https:\/\/doi.org\/10.1007\/3-540-36592-3_7"},{"issue":"5","key":"28_CR18","doi-asserted-by":"publisher","first-page":"1487","DOI":"10.1002\/jmri.26959","volume":"51","author":"PA Narayana","year":"2020","unstructured":"Narayana, P.A., et al.: Deep-learning-based neural tissue segmentation of mri in multiple sclerosis: effect of training set size. J. Magn. Reson. Imaging 51(5), 1487\u20131496 (2020). https:\/\/doi.org\/10.1002\/jmri.26959","journal-title":"J. Magn. Reson. Imaging"},{"key":"28_CR19","doi-asserted-by":"publisher","unstructured":"Oluwalade., B., et\u00a0al.: Human activity recognition using deep learning models on smartphones and smartwatches sensor data. In: Proc. of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies, HEALTHINF, pp. 645\u2013650. INSTICC, SciTePress (2021). https:\/\/doi.org\/10.5220\/0010325906450650","DOI":"10.5220\/0010325906450650"},{"issue":"3","key":"28_CR20","doi-asserted-by":"publisher","first-page":"368","DOI":"10.3390\/rs13030368","volume":"13","author":"CA Ramezan","year":"2021","unstructured":"Ramezan, C.A., et al.: Effects of training set size on supervised machine-learning land-cover classification of large-area high-resolution remotely sensed data. Remote Sensing 13(3), 368 (2021). https:\/\/doi.org\/10.3390\/rs13030368","journal-title":"Remote Sensing"},{"issue":"1","key":"28_CR21","doi-asserted-by":"publisher","first-page":"66","DOI":"10.3390\/s17010066","volume":"17","author":"Y Saez","year":"2016","unstructured":"Saez, Y., Baldominos, A., Isasi, P.: A comparison study of classifier algorithms for cross-person physical activity recognition. Sensors 17(1), 66 (2016). https:\/\/doi.org\/10.3390\/s17010066","journal-title":"Sensors"},{"key":"28_CR22","doi-asserted-by":"publisher","unstructured":"Sandha, S.S., et\u00a0al.: Time awareness in deep learning-based multimodal fusion across smartphone platforms. In: IEEE\/ACM Fifth International Conference on IoT Design and Implementation, pp. 149\u2013156. IEEE (2020). https:\/\/doi.org\/10.1109\/IOTDI49375.2020.00022","DOI":"10.1109\/IOTDI49375.2020.00022"},{"issue":"3","key":"28_CR23","doi-asserted-by":"publisher","first-page":"1113","DOI":"10.1111\/coin.12318","volume":"36","author":"E Sansano","year":"2020","unstructured":"Sansano, E., et al.: A study of deep neural networks for human activity recognition. Comput. Intell. 36(3), 1113\u20131139 (2020). https:\/\/doi.org\/10.1111\/coin.12318","journal-title":"Comput. Intell."},{"key":"28_CR24","doi-asserted-by":"publisher","unstructured":"Vallat, R.: Pingouin: statistics in python. J. Open Source Soft. 3(31), 1026 (2018). https:\/\/doi.org\/10.21105\/joss.01026","DOI":"10.21105\/joss.01026"},{"key":"28_CR25","doi-asserted-by":"publisher","unstructured":"Yazdansepas, D., et\u00a0al.: A multi-featured approach for wearable sensor-based human activity recognition. In: IEEE International Conference on Healthcare Informatics, pp. 423\u2013431 (2016). https:\/\/doi.org\/10.1109\/ICHI.2016.81","DOI":"10.1109\/ICHI.2016.81"}],"container-title":["Lecture Notes in Computer Science","Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-49018-7_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T23:11:51Z","timestamp":1701040311000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-49018-7_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,27]]},"ISBN":["9783031490170","9783031490187"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-49018-7_28","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,27]]},"assertion":[{"value":"27 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CIARP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Iberoamerican Congress on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Coimbra","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","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":"27 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ciarp2023","order":10,"name":"conference_id","label":"Conference ID","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":"Conftool","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"106","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":"61","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":"58% - 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":"2","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":"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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}