{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T19:02:46Z","timestamp":1778785366993,"version":"3.51.4"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030876715","type":"print"},{"value":"9783030876722","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-87672-2_11","type":"book-chapter","created":{"date-parts":[[2021,9,21]],"date-time":"2021-09-21T21:02:46Z","timestamp":1632258166000},"page":"163-177","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Chances of Interpretable Transfer Learning for Human Activity Recognition in Warehousing"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4521-9391","authenticated-orcid":false,"given":"Michael","family":"Kirchhof","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6535-776X","authenticated-orcid":false,"given":"Lena","family":"Schmid","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4915-4070","authenticated-orcid":false,"given":"Christopher","family":"Reining","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael ten","family":"Hompel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0976-7190","authenticated-orcid":false,"given":"Markus","family":"Pauly","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,22]]},"reference":[{"key":"11_CR1","unstructured":"Agresti, A.: An Introduction to Categorical Data Analysis. John Wiley, Hoboken (2018)"},{"key":"11_CR2","unstructured":"Atzmon, Y., Chechik, G.: Probabilistic AND-OR attribute grouping for zero-shot learning. In: Conference on Uncertainty in Artificial Intelligence (2018)"},{"key":"11_CR3","doi-asserted-by":"crossref","unstructured":"Avsar, H., Altermann, E., Reining, C., Rueda, F.M., Fink, G.A., ten Hompel, M.: Benchmarking annotation procedures for multi-channel time series HAR dataset. In: 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, pp. 453\u2013458 (2021)","DOI":"10.1109\/PerComWorkshops51409.2021.9431062"},{"issue":"6","key":"11_CR4","doi-asserted-by":"publisher","first-page":"9995","DOI":"10.3390\/s140609995","volume":"14","author":"O Banos","year":"2014","unstructured":"Banos, O., Toth, M.A., Damas, M., Pomares, H., Rojas, I.: Dealing with the effects of sensor displacement in wearable activity recognition. Sensors 14(6), 9995\u201310023 (2014)","journal-title":"Sensors"},{"key":"11_CR5","doi-asserted-by":"crossref","unstructured":"Calzavara, M., Glock, C.H., Grosse, E.H., Persona, A., Sgarbossa, F.: Analysis of economic and ergonomic performance measures of different rack layouts in an order picking warehouse. Comput. Ind. Eng. 111, 527\u2013536 (2017)","DOI":"10.1016\/j.cie.2016.07.001"},{"issue":"3","key":"11_CR6","doi-asserted-by":"publisher","first-page":"4405","DOI":"10.1007\/s11042-015-3177-1","volume":"76","author":"C Chen","year":"2017","unstructured":"Chen, C., Jafari, R., Kehtarnavaz, N.: A survey of depth and inertial sensor fusion for human action recognition. Multimed. Tools Appl. 76(3), 4405\u20134425 (2017)","journal-title":"Multimed. Tools Appl."},{"key":"11_CR7","doi-asserted-by":"crossref","unstructured":"Cheng, H.T., Sun, F.T., Griss, M., Davis, P., Li, J., You, D.: NuActiv: recognizing unseen new activities using semantic attribute-based learning. In: 11th Annual Conference on Mobile Systems, Applications, and Services, pp. 361\u2013374 (2013)","DOI":"10.1145\/2462456.2464438"},{"key":"11_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1007\/978-3-030-59747-4_30","volume-title":"Computational Logistics","author":"JR Daduna","year":"2020","unstructured":"Daduna, J.R.: Automated and autonomous driving in freight transport - opportunities and limitations. In: Lalla-Ruiz, E., Mes, M., Vo\u00df, S. (eds.) ICCL 2020. LNCS, vol. 12433, pp. 457\u2013475. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59747-4_30"},{"key":"11_CR9","doi-asserted-by":"crossref","unstructured":"Ding, N., Deng, J., Murphy, K.P., Neven, H.: probabilistic label relation graphs with Ising models. In: Proceedings of the 2015 IEEE International Conference on Computer Vision, pp. 1161\u20131169 (2015)","DOI":"10.1109\/ICCV.2015.138"},{"key":"11_CR10","unstructured":"Feldhorst, S., Aniol, S., ten Hompel, M.: Human Activity Recognition in der Kommissionierung - Charakterisierung des Kommissionierprozesses als Ausgangsbasis f\u00fcr die Methodenentwicklung. Logistics J. 2016(10) (2016)"},{"issue":"1","key":"11_CR11","doi-asserted-by":"publisher","first-page":"6900","DOI":"10.1016\/j.ifacol.2017.08.1214","volume":"50","author":"EH Grosse","year":"2017","unstructured":"Grosse, E.H., Calzavara, M., Glock, C.H., Sgarbossa, F.: Incorporating human factors into decision support models for production and logistics: current state of research. IFAC-PapersOnLine 50(1), 6900\u20136905 (2017)","journal-title":"IFAC-PapersOnLine"},{"issue":"3","key":"11_CR12","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1016\/j.ifacol.2015.06.101","volume":"48","author":"EH Grosse","year":"2015","unstructured":"Grosse, E.H., Glock, C.H., Neumann, W.P.: Human factors in order picking system design: a content analysis. IFAC-PapersOnLine 48(3), 320\u2013325 (2015)","journal-title":"IFAC-PapersOnLine"},{"issue":"9","key":"11_CR13","doi-asserted-by":"publisher","first-page":"7316","DOI":"10.1109\/TIE.2018.2877090","volume":"66","author":"L Guo","year":"2018","unstructured":"Guo, L., Lei, Y., Xing, S., Yan, T., Li, N.: Deep convolutional transfer learning network: a new method for intelligent fault diagnosis of machines with unlabeled data. IEEE Trans. Industr. Electron. 66(9), 7316\u20137325 (2018)","journal-title":"IEEE Trans. Industr. Electron."},{"key":"11_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"412","DOI":"10.1007\/978-3-030-59747-4_27","volume-title":"Computational Logistics","author":"H Huang","year":"2020","unstructured":"Huang, H., Pouls, M., Meyer, A., Pauly, M.: Travel time prediction using tree-based ensembles. In: Lalla-Ruiz, E., Mes, M., Vo\u00df, S. (eds.) ICCL 2020. LNCS, vol. 12433, pp. 412\u2013427. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59747-4_27"},{"key":"11_CR15","unstructured":"Kirchhof, M., Schmid, L., Reining, C., ten Hompel, M., Pauly, M.: pRSL: interpretable multi-label stacking by learning probabilistic rules. In: Uncertainty in Artificial Intelligence. PMLR (2021). (in press)"},{"key":"11_CR16","unstructured":"Kirchhof, M.: GitHub repository for this article (2021). https:\/\/github.com\/mkirchhof\/rslAppl"},{"issue":"2","key":"11_CR17","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1016\/j.ejor.2006.07.009","volume":"182","author":"R de Koster","year":"2007","unstructured":"de Koster, R., Le-Duc, T., Roodbergen, K.J.: Design and control of warehouse order picking: a literature review. Eur. J. Oper. Res. 182(2), 481\u2013501 (2007)","journal-title":"Eur. J. Oper. Res."},{"key":"11_CR18","unstructured":"Kr\u00fcger, A., Feldmann, F., Pauly, M., ten Hompel, M.: Einsatzm\u00f6glichkeiten maschineller Lernverfahren in einer dezentral organisierten Lagerverwaltung auf Basis intelligenter Beh\u00e4lter. Logistics J. Proc. 2020(12) (2020)"},{"key":"11_CR19","first-page":"12316","volume":"32","author":"M Kull","year":"2019","unstructured":"Kull, M., Perello Nieto, M., K\u00e4ngsepp, M., Silva Filho, T., Song, H., Flach, P.: Beyond temperature scaling: obtaining well-calibrated multi-class probabilities with Dirichlet calibration. Adv. Neural. Inf. Process. Syst. 32, 12316\u201312326 (2019)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"11_CR20","doi-asserted-by":"crossref","unstructured":"Liu, L., Zhou, T., Long, G., Jiang, J., Zhang, C.: Attribute propagation network for graph zero-shot learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34(04), pp. 4868\u20134875 (2020)","DOI":"10.1609\/aaai.v34i04.5923"},{"key":"11_CR21","doi-asserted-by":"crossref","unstructured":"Maurice, P., et al.: Human movement and ergonomics: an industry-oriented dataset for collaborative robotics. Int. J. Robot. Res. 38(14), 1529\u20131537 (2019)","DOI":"10.1177\/0278364919882089"},{"key":"11_CR22","doi-asserted-by":"crossref","unstructured":"Rueda, F.M., Grzeszick, R., Fink, G.A., Feldhorst, S., Ten Hompel, M.: Convolutional neural networks for human activity recognition using body-worn sensors. Informatics 5(2), 26 (2018)","DOI":"10.3390\/informatics5020026"},{"key":"11_CR23","doi-asserted-by":"crossref","unstructured":"Niemann, F., et al.: LARa: creating a dataset for human activity recognition in logistics using semantic attributes. Sensors 20(15), 4083 (2020)","DOI":"10.3390\/s20154083"},{"key":"11_CR24","doi-asserted-by":"crossref","unstructured":"Ord\u00f3\u00f1ez, F., Roggen, D.: Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition. Sensors 16(1), 115 (2016)","DOI":"10.3390\/s16010115"},{"issue":"8","key":"11_CR25","doi-asserted-by":"publisher","first-page":"245","DOI":"10.3390\/info10080245","volume":"10","author":"C Reining","year":"2019","unstructured":"Reining, C., Niemann, F., Rueda, F.M., Fink, G.A., ten Hompel, M.: Human activity recognition for production and logistics - a systematic literature review. Information 10(8), 245 (2019)","journal-title":"Information"},{"key":"11_CR26","doi-asserted-by":"crossref","unstructured":"Reining, C., Rueda, F.M., Niemann, F., Fink, G.A., ten Hompel, M.: Annotation performance for multi-channel time series HAR dataset in logistics. In: 2020 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 1\u20136 (2020)","DOI":"10.1109\/PerComWorkshops48775.2020.9156170"},{"issue":"21","key":"11_CR27","doi-asserted-by":"publisher","first-page":"6383","DOI":"10.3390\/s20216383","volume":"20","author":"PMS Ribeiro","year":"2020","unstructured":"Ribeiro, P.M.S., Matos, A.C., Santos, P.H., Cardoso, J.S.: Machine learning improvements to human motion tracking with IMUs. Sensors 20(21), 6383 (2020)","journal-title":"Sensors"},{"key":"11_CR28","doi-asserted-by":"crossref","unstructured":"Roggen, D., et al.: Collecting complex activity datasets in highly rich networked sensor environments. In: Seventh International Conference on Networked Sensing Systems (INSS), pp. 233\u2013240 (2010)","DOI":"10.1109\/INSS.2010.5573462"},{"key":"11_CR29","doi-asserted-by":"crossref","unstructured":"Rueda, F.M., Fink, G.: From human pose to on-body devices for human-activity recognition. In: 26th International Conference on Pattern Recognition (ICPR), pp. 10066\u201310073 (2021)","DOI":"10.1109\/ICPR48806.2021.9412283"},{"issue":"6","key":"11_CR30","doi-asserted-by":"publisher","first-page":"616","DOI":"10.1080\/1463922X.2012.678283","volume":"14","author":"K Schaub","year":"2013","unstructured":"Schaub, K., Caragnano, G., Britzke, B., Bruder, R.: The European assembly worksheet. Theor. Issues Ergon. Sci. 14(6), 616\u2013639 (2013)","journal-title":"Theor. Issues Ergon. Sci."},{"key":"11_CR31","unstructured":"Vicon: Full Body Modeling with Plug-in Gate (2017). https:\/\/docs.vicon.com\/display\/Nexus26\/Full+body+modeling+with+Plug-in+Gait. Accessed 16 Mar 2021"},{"issue":"9","key":"11_CR32","doi-asserted-by":"publisher","first-page":"2251","DOI":"10.1109\/TPAMI.2018.2857768","volume":"41","author":"Y Xian","year":"2018","unstructured":"Xian, Y., Lampert, C.H., Schiele, B., Akata, Z.: Zero-shot learning - a comprehensive evaluation of the good, the bad and the ugly. IEEE Trans. Pattern Anal. Mach. Intell. 41(9), 2251\u20132265 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"11_CR33","unstructured":"Yordanova, K., et al.: Challenges in Annotation of useR Data for UbiquitOUs Systems: Results from the 1st ARDUOUS Workshop (2018). arXiv:1803.05843"},{"issue":"12","key":"11_CR34","doi-asserted-by":"publisher","first-page":"2416","DOI":"10.3390\/app8122416","volume":"8","author":"A Zhang","year":"2018","unstructured":"Zhang, A., et al.: Transfer learning with deep recurrent neural networks for remaining useful life estimation. Appl. Sci. 8(12), 2416 (2018)","journal-title":"Appl. Sci."},{"key":"11_CR35","unstructured":"Zhang, A., Lipton, Z.C., Li, M., Smola, A.J.: Dive into Deep Learning (2020)"}],"container-title":["Lecture Notes in Computer Science","Computational Logistics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87672-2_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,21]],"date-time":"2021-09-21T21:06:31Z","timestamp":1632258391000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87672-2_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030876715","9783030876722"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87672-2_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"22 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCL","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Logistics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccl22021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iccl2021.nl\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"111","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":"42","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":"38% - 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":"2.5","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":"2","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)"}}]}}