{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T08:00:53Z","timestamp":1776412853336,"version":"3.51.2"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030698720","type":"print"},{"value":"9783030698737","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":"http:\/\/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":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-69873-7_19","type":"book-chapter","created":{"date-parts":[[2021,2,27]],"date-time":"2021-02-27T14:03:28Z","timestamp":1614434608000},"page":"261-270","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["A Deep Learning Method for Complex Human Activity Recognition Using Virtual Wearable Sensors"],"prefix":"10.1007","author":[{"given":"Fanyi","family":"Xiao","sequence":"first","affiliation":[]},{"given":"Ling","family":"Pei","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Chu","sequence":"additional","affiliation":[]},{"given":"Danping","family":"Zou","sequence":"additional","affiliation":[]},{"given":"Wenxian","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Yifan","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Tao","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,2,28]]},"reference":[{"key":"19_CR1","unstructured":"Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.L.: A public domain dataset for human activity recognition using smartphones. In: ESANN (2013)"},{"issue":"3","key":"19_CR2","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1109\/MRA.2015.2448279","volume":"22","author":"B Bruno","year":"2015","unstructured":"Bruno, B., Mastrogiovanni, F., Sgorbissa, A.: Wearable inertial sensors: applications, challenges, and public test benches. IEEE Robot. Autom. Mag. 22(3), 116\u2013124 (2015)","journal-title":"IEEE Robot. Autom. Mag."},{"key":"19_CR3","unstructured":"Chevalier, G.: LSTMs for human activity recognition (2016)"},{"key":"19_CR4","doi-asserted-by":"crossref","unstructured":"Chu, L., Li, H., Qiu, R.C.: LEMO: learn to equalize for MIMO-OFDM systems with low-resolution ADCs. arXiv preprint arXiv:1905.06329 (2019)","DOI":"10.1109\/ICCT50939.2020.9295693"},{"issue":"3","key":"19_CR5","first-page":"625","volume":"11","author":"D Erhan","year":"2010","unstructured":"Erhan, D., Bengio, Y., Courville, A.C., Manzagol, P.A., Bengio, S.: Why does unsupervised pre-training help deep learning? J. Mach. Learn. Res. 11(3), 625\u2013660 (2010)","journal-title":"J. Mach. Learn. Res."},{"issue":"6","key":"19_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3272127.3275108","volume":"37","author":"Y Huang","year":"2019","unstructured":"Huang, Y., Kaufmann, M., Aksan, E., Black, M.J., Hilliges, O., Pons-Moll, G.: Deep inertial poser: learning to reconstruct human pose from sparse inertial measurements in real time. ACM Trans. Graph. (TOG) 37(6), 1\u201315 (2019). Article no. 185","journal-title":"ACM Trans. Graph. (TOG)"},{"issue":"7553","key":"19_CR7","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y Lecun","year":"2015","unstructured":"Lecun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)","journal-title":"Nature"},{"key":"19_CR8","doi-asserted-by":"crossref","unstructured":"Lockhart, J.W., Weiss, G.M., Xue, J.C., Gallagher, S.T., Grosner, A.B., Pulickal, T.T.: Design considerations for the WISDM smart phone-based sensor mining architecture. In: Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data, pp. 25\u201333. ACM (2011)","DOI":"10.1145\/2003653.2003656"},{"issue":"6","key":"19_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2816795.2818013","volume":"34","author":"M Loper","year":"2015","unstructured":"Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., Black, M.J.: SMPL: a skinned multi-person linear model. ACM Trans. Graph. (TOG) 34(6), 1\u201316 (2015). Article no. 248","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"19_CR10","doi-asserted-by":"crossref","unstructured":"Mahmood, N., Ghorbani, N., Troje, N.F., Pons-Moll, G., Black, M.J.: AMASS: archive of motion capture as surface shapes. arXiv preprint arXiv:1904.03278 (2019)","DOI":"10.1109\/ICCV.2019.00554"},{"key":"19_CR11","first-page":"55","volume":"30","author":"A Maurer","year":"2013","unstructured":"Maurer, A., Pontil, M.: Excess risk bounds for multitask learning with trace norm regularization. J. Mach. Learn. Res. 30, 55\u201376 (2013)","journal-title":"J. Mach. Learn. Res."},{"issue":"1","key":"19_CR12","doi-asserted-by":"publisher","first-page":"115","DOI":"10.3390\/s16010115","volume":"16","author":"F Ord\u00f3\u00f1ez","year":"2016","unstructured":"Ord\u00f3\u00f1ez, F., Roggen, D.: Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition. Sensors 16(1), 115 (2016)","journal-title":"Sensors"},{"issue":"2","key":"19_CR13","doi-asserted-by":"publisher","first-page":"1402","DOI":"10.3390\/s130201402","volume":"13","author":"L Pei","year":"2013","unstructured":"Pei, L., et al.: Human behavior cognition using smartphone sensors. Sensors 13(2), 1402\u20131424 (2013)","journal-title":"Sensors"},{"issue":"5","key":"19_CR14","doi-asserted-by":"publisher","first-page":"6155","DOI":"10.3390\/s120506155","volume":"12","author":"L Pei","year":"2012","unstructured":"Pei, L., Liu, J., Guinness, R., Chen, Y., Kuusniemi, H., Chen, R.: Using LS-SVM based motion recognition for smartphone indoor wireless positioning. Sensors 12(5), 6155\u20136175 (2012)","journal-title":"Sensors"},{"key":"19_CR15","doi-asserted-by":"crossref","unstructured":"Reiss, A., Stricker, D.: Introducing a new benchmarked dataset for activity monitoring. In: 2012 16th International Symposium on Wearable Computers, pp. 108\u2013109. IEEE (2012)","DOI":"10.1109\/ISWC.2012.13"},{"key":"19_CR16","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1016\/j.eswa.2016.04.032","volume":"59","author":"CA Ronao","year":"2016","unstructured":"Ronao, C.A., Cho, S.-B.: Human activity recognition with smartphone sensors using deep learning neural networks. Expert Syst. Appl. 59, 235\u2013244 (2016)","journal-title":"Expert Syst. Appl."},{"issue":"14","key":"19_CR17","doi-asserted-by":"publisher","first-page":"3213","DOI":"10.3390\/s19143213","volume":"19","author":"W Sousa Lima","year":"2019","unstructured":"Sousa Lima, W., Souto, E., El-Khatib, K., Jalali, R., Gama, J.: Human activity recognition using inertial sensors in a smartphone: an overview. Sensors 19(14), 3213 (2019)","journal-title":"Sensors"},{"issue":"5","key":"19_CR18","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1167\/2.5.2","volume":"2","author":"NF Troje","year":"2002","unstructured":"Troje, N.F.: Decomposing biological motion: a framework for analysis and synthesis of human gait patterns. J. Vis. 2(5), 2 (2002)","journal-title":"J. Vis."},{"key":"19_CR19","first-page":"1","volume":"2017","author":"S Zhang","year":"2017","unstructured":"Zhang, S., Wei, Z., Nie, J., Huang, L., Wang, S., Li, Z.: A review on human activity recognition using vision-based method. J. Healthc. Eng. 2017, 1\u201331 (2017)","journal-title":"J. Healthc. Eng."}],"container-title":["Lecture Notes in Computer Science","Spatial Data and Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-69873-7_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,2,27]],"date-time":"2021-02-27T14:12:03Z","timestamp":1614435123000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-69873-7_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030698720","9783030698737"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-69873-7_19","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":"28 February 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SpatialDI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Spatial Data and Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 May 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 May 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"spatialdi2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/geospatial.szu.edu.cn\/notice_details?id=51","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":"50","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":"21","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":"42% - 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":"4","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":"3","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)"}}]}}