{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T09:02:17Z","timestamp":1758704537183,"version":"3.41.0"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319961323"},{"type":"electronic","value":"9783319961330"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/http\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-319-96133-0_23","type":"book-chapter","created":{"date-parts":[[2018,7,7]],"date-time":"2018-07-07T11:54:57Z","timestamp":1530964497000},"page":"302-315","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A CNN Based Transfer Learning Model for Automatic Activity Recognition from Accelerometer Sensors"],"prefix":"10.1007","author":[{"given":"Belkacem","family":"Chikhaoui","sequence":"first","affiliation":[]},{"given":"Frank","family":"Gouineau","sequence":"additional","affiliation":[]},{"given":"Martin","family":"Sotir","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,7,8]]},"reference":[{"issue":"3","key":"23_CR1","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1007\/s10115-013-0665-3","volume":"36","author":"D Cook","year":"2013","unstructured":"Cook, D., Feuz, K.D., Krishnan, N.C.: Transfer learning for activity recognition: a survey. Knowl. Inf. Syst. 36(3), 537\u2013556 (2013)","journal-title":"Knowl. Inf. Syst."},{"key":"23_CR2","doi-asserted-by":"crossref","unstructured":"Chikhaoui, B., Ye, B., Mihailidis, A.: Aggressive and agitated behavior recognition from accelerometer data using non-negative matrix factorization. J. Ambient Intell. Hum. Comput. 1\u201315 (2017)","DOI":"10.1007\/s12652-017-0537-x"},{"issue":"7","key":"23_CR3","first-page":"897","volume":"29","author":"W-H Chen","year":"2017","unstructured":"Chen, W.-H., Cho, P.-C., Jiang, Y.-L.: Activity recognition using transfer learning. Sens. Mater. 29(7), 897\u2013904 (2017)","journal-title":"Sens. Mater."},{"issue":"10","key":"23_CR4","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345\u20131359 (2010)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"23_CR5","doi-asserted-by":"crossref","unstructured":"Chikhaoui, B., Gouineau, F.: Towards automatic feature extraction for activity recognition from wearable sensors: a deep learning approach. In: 2017 IEEE International Conference on Data Mining Workshops, ICDM Workshops 2017, New Orleans, LA, USA, 18\u201321 November 2017, pp. 693\u2013702 (2017)","DOI":"10.1109\/ICDMW.2017.97"},{"key":"23_CR6","unstructured":"Graves, A., Jaitly, N.: Towards end-to-end speech recognition with recurrent neural networks. In: Proceedings of the 31st International Conference on International Conference on Machine Learning - Volume 32, ICML 2014, pages II\u20131764\u2013II\u20131772. JMLR.org (2014)"},{"key":"23_CR7","unstructured":"Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., Gao, R.X.: Deep learning and its applications to machine health monitoring: a survey. CoRR, abs\/1612.07640 (2016)"},{"issue":"6","key":"23_CR8","first-page":"1","volume":"152","author":"V Gokul","year":"2016","unstructured":"Gokul, V., Kannan, P., Kumar, S., Jacob, S.G.: Deep Q-learning for home automation. Int. J. Comput. Appl. 152(6), 1\u20135 (2016)","journal-title":"Int. J. Comput. Appl."},{"key":"23_CR9","doi-asserted-by":"crossref","unstructured":"Kim, J., Mo, Y.J., Lee, W., Nyang, D.: Dynamic security-level maximization for stabilized parallel deep learning architectures in surveillance applications. In: 2017 IEEE Symposium on Privacy-Aware Computing (PAC), pp. 192\u2013193 (2017)","DOI":"10.1109\/PAC.2017.22"},{"key":"23_CR10","doi-asserted-by":"crossref","unstructured":"Morales, F.J.O., Roggen, D.: Deep convolutional feature transfer across mobile activity recognition domains, sensor modalities and locations. In: Proceedings of the 2016 ACM International Symposium on Wearable Computers, ISWC 2016, Heidelberg, Germany, 12\u201316 September 2016, pp. 92\u201399 (2016)","DOI":"10.1145\/2971763.2971764"},{"issue":"1","key":"23_CR11","doi-asserted-by":"publisher","first-page":"115","DOI":"10.3390\/s16010115","volume":"16","author":"FJO Morales","year":"2016","unstructured":"Morales, F.J.O., Roggen, D.: Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition. Sensors 16(1), 115 (2016)","journal-title":"Sensors"},{"key":"23_CR12","unstructured":"Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? In: Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 8\u201313 December 2014, Montreal, Quebec, Canada, pp. 3320\u20133328 (2014)"},{"key":"23_CR13","unstructured":"Wang, J., Chen, Y., Hao, S., Peng, X., Hu, L.: Deep learning for sensor-based activity recognition: a survey. CoRR, abs\/1707.03502 (2017)"},{"key":"23_CR14","unstructured":"Hammerla, N.Y., Halloran, S., Pl\u00f6tz, T.: Deep, convolutional, and recurrent models for human activity recognition using wearables. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, pp. 1533\u20131540 (2016)"},{"key":"23_CR15","doi-asserted-by":"crossref","unstructured":"Sargano, A.B., Wang, X., Angelov, P., Habib, Z.: Human action recognition using transfer learning with deep representations. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 463\u2013469 (2017)","DOI":"10.1109\/IJCNN.2017.7965890"},{"key":"23_CR16","doi-asserted-by":"crossref","unstructured":"Kunze, J., Kirsch, L., Kurenkov, I., Krug, A., Johannsmeier, J., Stober, S.: Transfer learning for speech recognition on a budget. In: Proceedings of the 2nd Workshop on Representation Learning for NLP, Rep4NLP@ACL 2017, Vancouver, Canada, 3 August 2017, pp. 168\u2013177 (2017)","DOI":"10.18653\/v1\/W17-2620"},{"key":"23_CR17","doi-asserted-by":"crossref","unstructured":"Zheng, V.W., Hu, D.H., Yang, Q.: Cross-domain activity recognition. In: Proceedings of the 11th International Conference on Ubiquitous Computing, UbiComp 2009, pp. 61\u201370 (2009)","DOI":"10.1145\/1620545.1620554"},{"issue":"3","key":"23_CR18","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1109\/MPRV.2014.52","volume":"13","author":"FJ Ord\u00f3\u00f1ez","year":"2014","unstructured":"Ord\u00f3\u00f1ez, F.J., Englebienne, G., de Toledo, P., van Kasteren, T., Sanchis, A., Kr\u00f6se, B.: In-home activity recognition: Bayesian inference for hidden Markov models. IEEE Pervasive Comput. 13(3), 67\u201375 (2014)","journal-title":"IEEE Pervasive Comput."},{"key":"23_CR19","unstructured":"Rashidi, P., Cook, D.J.: Activity recognition based on home to home transfer learning. In: Proceedings of the 5th AAAI Conference on Plan, Activity, and Intent Recognition, AAAIWS 2010\u201305, pp. 45\u201352 (2010)"},{"key":"23_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1007\/978-3-642-12654-3_17","volume-title":"Pervasive Computing","author":"TLM van Kasteren","year":"2010","unstructured":"van Kasteren, T.L.M., Englebienne, G., Kr\u00f6se, B.J.A.: Transferring knowledge of activity recognition across sensor networks. In: Flor\u00e9en, P., Kr\u00fcger, A., Spasojevic, M. (eds.) Pervasive 2010. LNCS, vol. 6030, pp. 283\u2013300. Springer, Heidelberg (2010). https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1007\/978-3-642-12654-3_17"},{"key":"23_CR21","unstructured":"Kurz, M., H\u00f6lzl, G., Ferscha, A., Calatroni, A., Roggen, D., Tr\u00f6ster, G.: Real-time transfer and evaluation of activity recognition capabilities in an opportunistic system. In: Adaptive, pp. 73\u201378 (2011)"},{"key":"23_CR22","unstructured":"Calatroni, A., Roggen, D., Tr\u00f6ster, G.: Automatic transfer of activity recognition capabilities between body-worn motion sensors: training newcomers to recognize locomotion. In: Eighth International Conference on Networked Sensing Systems (INSS 2011) (2011)"},{"issue":"1","key":"23_CR23","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1186\/s40537-016-0043-6","volume":"3","author":"K Weiss","year":"2016","unstructured":"Weiss, K., Khoshgoftaar, T.M., Wang, D.D.: A survey of transfer learning. J. Big Data 3(1), 3\u20139 (2016)","journal-title":"J. Big Data"},{"key":"23_CR24","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1016\/j.pmcj.2014.05.006","volume":"15","author":"Sourav Bhattacharya","year":"2014","unstructured":"Bhattacharya, S., Nurmi, P., Hammerla, N., Pl\u00f6tz, T.: Using unlabeled data in a sparse-coding framework for human activity recognition. Perv. Mob. Comput. 15(C), 242\u2013262 (2014)","journal-title":"Pervasive and Mobile Computing"},{"key":"23_CR25","doi-asserted-by":"crossref","unstructured":"Ha, S., Yun, J.M., Choi, S.: Multi-modal convolutional neural networks for activity recognition. In: 2015 IEEE International Conference on Systems, Man, and Cybernetics, pp. 3017\u20133022 (2015)","DOI":"10.1109\/SMC.2015.525"},{"key":"23_CR26","doi-asserted-by":"crossref","unstructured":"Pourbabaee, B., Roshtkhari, M.J., Khorasani, K.: Deep convolutional neural networks and learning ECG features for screening paroxysmal atrial fibrillation patients. IEEE Trans. Syst. Man Cybern.: Syst. PP(99), 1\u201310 (2017)","DOI":"10.1109\/IJCNN.2016.7727866"},{"key":"23_CR27","doi-asserted-by":"crossref","unstructured":"Stisen, A., Blunck, H., Bhattacharya, S., Prentow, T.S., Kj\u00e6rgaard, M.B., Dey, A., Sonne, T., Jensen, M.M.: Smart devices are different: assessing and mitigatingmobile sensing heterogeneities for activity recognition. In: Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems, SenSys 2015, pp. 127\u2013140 (2015)","DOI":"10.1145\/2809695.2809718"},{"key":"23_CR28","doi-asserted-by":"crossref","unstructured":"Sztyler, T., Stuckenschmidt, H.: On-body localization of wearable devices: an investigation of position-aware activity recognition. In: 2016 IEEE International Conference on Pervasive Computing and Communications (PerCom), pp. 1\u20139 (2016)","DOI":"10.1109\/PERCOM.2016.7456521"},{"key":"23_CR29","unstructured":"Vavoulas, G., Chatzaki, C., Malliotakis, T., Pediaditis, M., Tsiknakis, M.: The mobiact dataset: recognition of activities of daily living using smartphones. In: Proceedings of the 2nd International Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AgeingWell 2016, Rome, Italy, 21\u201322 April 2016, pp. 143\u2013151 (2016)"},{"key":"23_CR30","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.patrec.2016.01.001","volume":"73","author":"A Khan","year":"2016","unstructured":"Khan, A., Hammerla, N., Mellor, S., Pl\u00f6tz, T.: Optimising sampling rates for accelerometer-based human activity recognition. Pattern Recogn. Lett. 73, 33\u201340 (2016)","journal-title":"Pattern Recogn. Lett."},{"key":"23_CR31","doi-asserted-by":"crossref","unstructured":"Qi, X., Keally, M., Zhou, G., Li, Y., Ren, Z.: Adasense: adapting sampling rates for activity recognition in body sensor networks. In: 2013 IEEE 19th Real-Time and Embedded Technology and Applications Symposium (RTAS), pp. 163\u2013172, April 2013","DOI":"10.1109\/RTAS.2013.6531089"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Data Mining in Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-96133-0_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,5]],"date-time":"2025-07-05T16:51:37Z","timestamp":1751734297000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/link.springer.com\/10.1007\/978-3-319-96133-0_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783319961323","9783319961330"],"references-count":31,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1007\/978-3-319-96133-0_23","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"8 July 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MLDM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Learning and Data Mining in Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New York, NY","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 July 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 July 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mldm2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2.zoppoz.workers.dev:443\/http\/www.mldm.de\/index.php","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}