{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T15:31:02Z","timestamp":1772811062828,"version":"3.50.1"},"publisher-location":"Cham","reference-count":10,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031202322","type":"print"},{"value":"9783031202339","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-20233-9_49","type":"book-chapter","created":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T00:02:48Z","timestamp":1667433768000},"page":"484-492","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Gait Recognition by Sensing Insole Using a Hybrid CNN-Attention-LSTM Network"],"prefix":"10.1007","author":[{"given":"Jing","family":"Yue","sequence":"first","affiliation":[]},{"given":"Zhanyong","family":"Mei","sequence":"additional","affiliation":[]},{"given":"Kamen","family":"Ivanov","sequence":"additional","affiliation":[]},{"given":"Yingyi","family":"Li","sequence":"additional","affiliation":[]},{"given":"Tong","family":"He","sequence":"additional","affiliation":[]},{"given":"Hui","family":"Zeng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,3]]},"reference":[{"issue":"7","key":"49_CR1","doi-asserted-by":"publisher","first-page":"4917","DOI":"10.1007\/s11831-021-09560-3","volume":"28","author":"K Shaheed","year":"2021","unstructured":"Shaheed, K., et al.: A systematic review on physiological-based biometric recognition systems: current and future trends. Archives of Computational Methods in Engineering 28(7), 4917\u20134960 (2021). https:\/\/doi.org\/10.1007\/s11831-021-09560-3","journal-title":"Archives of Computational Methods in Engineering"},{"key":"49_CR2","doi-asserted-by":"crossref","unstructured":"Wan, C., Wang, L., Phoha, V.V. (eds.).: A Survey on Gait Recognition. ACM Computing Surveys (CSUR) 51(5), 1\u201335 (2018)","DOI":"10.1145\/3230633"},{"issue":"4","key":"49_CR3","doi-asserted-by":"publisher","first-page":"223","DOI":"10.26599\/BDMA.2021.9020006","volume":"4","author":"C Wang","year":"2021","unstructured":"Wang, C., Li, Z., Sarpong, B.: Multimodal adaptive identity-recognition algorithm fused with gait perception. Big Data Mining and Analytics. 4(4), 223\u2013232 (2021)","journal-title":"Big Data Mining and Analytics."},{"issue":"13","key":"49_CR4","doi-asserted-by":"publisher","first-page":"4592","DOI":"10.3390\/s21134592","volume":"21","author":"X Zeng","year":"2021","unstructured":"Zeng, X., Zhang, X., Yang, S., Shi, Z., Chi, C.: Gait-based implicit authentication using edge computing and deep learning for mobile devices. Sensors. 21(13), 4592 (2021)","journal-title":"Sensors."},{"key":"49_CR5","doi-asserted-by":"crossref","unstructured":"Zou, Y., Libanori, A., Xu, J., Nashalian, A., Chen, J.: Triboelectric Nanogenerator Enabled Smart Shoes for Wearable Electricity Generation. Research. 2020 (2020)","DOI":"10.34133\/2020\/7158953"},{"key":"49_CR6","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1007\/978-3-030-27538-9_37","volume-title":"Intelligent Robotics and Applications","author":"K Ivanov","year":"2019","unstructured":"Ivanov, K., et al.: Design of a Sensor Insole for Gait Analysis. In: Yu, H., Liu, J., Liu, L., Ju, Z., Liu, Y., Zhou, D. (eds.) ICIRA 2019. LNCS (LNAI), vol. 11743, pp. 433\u2013444. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-27538-9_37"},{"key":"49_CR7","doi-asserted-by":"publisher","first-page":"150797","DOI":"10.1109\/ACCESS.2020.3016970","volume":"8","author":"K Ivanov","year":"2020","unstructured":"Ivanov, K., et al.: Identity recognition by walking outdoors using multimodal sensor insoles. IEEE Access. 8, 150797\u2013150807 (2020)","journal-title":"IEEE Access."},{"key":"49_CR8","unstructured":"Guo, X.X., Yang, H.Z.: An improved compromise for soft\/hard thresholds in wavelet denoising. CAAI Transactions on Intelligent Systems. 222\u2013225 (2008)"},{"key":"49_CR9","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"issue":"07","key":"49_CR10","first-page":"1027","volume":"32","author":"T Wang","year":"2019","unstructured":"Wang, T., Xia, Y., Zhang, D.: Human gait recognition based on convolutional neural network and attention model. Chinese Journal of Sensors and Actuators. 32(07), 1027\u20131033 (2019)","journal-title":"Chinese Journal of Sensors and Actuators."}],"container-title":["Lecture Notes in Computer Science","Biometric Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20233-9_49","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T00:32:52Z","timestamp":1667435572000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20233-9_49"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031202322","9783031202339"],"references-count":10,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20233-9_49","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"3 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CCBR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Biometric Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Beijing","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccbr2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ccbr99.cn\/","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":"115","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":"70","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":"61% - 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","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}