{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T14:03:50Z","timestamp":1743084230815,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031138348"},{"type":"electronic","value":"9783031138355"}],"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\/978-3-031-13835-5_5","type":"book-chapter","created":{"date-parts":[[2022,8,3]],"date-time":"2022-08-03T18:18:24Z","timestamp":1659550704000},"page":"49-58","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Gait Phase Detection Based on Time Sequence Adapting to Various Walking Posture and Frequency"],"prefix":"10.1007","author":[{"given":"Siyu","family":"Liu","sequence":"first","affiliation":[]},{"given":"Zhiyong","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Linjun","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Xiaohui","family":"Xiao","sequence":"additional","affiliation":[]},{"given":"Zhao","family":"Guo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,8,4]]},"reference":[{"unstructured":"Ashutosh, K., et al.: A review of gait cycle and its parameters. IJCEM Int. J. Comput. Eng. Manage. 13, 78\u201383 (2011)","key":"5_CR1"},{"key":"5_CR2","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.smhl.2017.03.001","volume":"1","author":"I Papavasileiou","year":"2017","unstructured":"Papavasileiou, I., Zhang, W., Han, S.: Real-time data-driven gait phase detection using ground contact force measurements: algorithms, platform design and performance. Smart Health 1, 34\u201349 (2017)","journal-title":"Smart Health"},{"doi-asserted-by":"crossref","unstructured":"Xianta, J., et al.: A wearable gait phase detection system based on force myography techniques. Sensors 18.4, 1279 (2018)","key":"5_CR3","DOI":"10.3390\/s18041279"},{"doi-asserted-by":"crossref","unstructured":"Chiew, H.Y., Wong, K.I., Murray, I.: Gait phase detection for normal and abnormal gaits using IMU. IEEE Sensors J. 19.9, 3439\u20133448 (2019)","key":"5_CR4","DOI":"10.1109\/JSEN.2019.2894143"},{"issue":"12","key":"5_CR5","doi-asserted-by":"publisher","first-page":"253","DOI":"10.3390\/a12120253","volume":"12","author":"T Zhen","year":"2019","unstructured":"Zhen, T., Yan, L., Yuan, P.: Walking gait phase detection based on acceleration signals using LSTM-DNN algorithm. Algorithms 12(12), 253 (2019)","journal-title":"Algorithms"},{"doi-asserted-by":"crossref","unstructured":"Lingyun, Y., et al.: Low-cost multisensor integrated system for online walking gait detection. J. Sensors 2021 (2021)","key":"5_CR6","DOI":"10.1155\/2021\/6378514"},{"doi-asserted-by":"crossref","unstructured":"Julia, C., et al.: Deep learning for freezing of gait detection in Parkinson\u2019s disease patients in their homes using a waist-worn inertial measurement unit. Knowl. Based Syst. 139, 119\u2013131 (2018)","key":"5_CR7","DOI":"10.1016\/j.knosys.2017.10.017"},{"doi-asserted-by":"crossref","unstructured":"Thu, V.H.T., et al.: A review of gait phase detection algorithms for lower limb prostheses. Sensors 20.14, 3972 (2020)","key":"5_CR8","DOI":"10.3390\/s20143972"},{"doi-asserted-by":"crossref","unstructured":"Tze-Shen, C., Lin, T.Y., Peter Hong, Y.-W.: Gait phase segmentation using weighted dynamic time warping and k-nearest neighbors graph embedding. In: ICASSP 2020\u20132020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE (2020)","key":"5_CR9","DOI":"10.1109\/ICASSP40776.2020.9053270"},{"doi-asserted-by":"crossref","unstructured":"Long, L., et al.: Ambulatory human gait phase detection using wearable inertial sensors and hidden markov model. Sensors 21.4, 1347 (2021)","key":"5_CR10","DOI":"10.3390\/s21041347"},{"issue":"16","key":"5_CR11","doi-asserted-by":"publisher","first-page":"5633","DOI":"10.3390\/ijerph17165633","volume":"17","author":"T Zhen","year":"2020","unstructured":"Zhen, T., Yan, L., Kong, J.-L.: An acceleration based fusion of multiple spatiotemporal networks for gait phase detection. Int. J. Environ. Res. Public Health 17(16), 5633 (2020)","journal-title":"Int. J. Environ. Res. Public Health"},{"issue":"3","key":"5_CR12","doi-asserted-by":"publisher","first-page":"789","DOI":"10.3390\/s21030789","volume":"21","author":"D Kreuzer","year":"2021","unstructured":"Kreuzer, D., Munz, M.: Deep convolutional and lstm networks on multi-channel time series data for gait phase recognition. Sensors 21(3), 789 (2021)","journal-title":"Sensors"},{"unstructured":"Keehong, S., et al.: RNN-based on-line continuous gait phase estimation from shank-mounted IMUs to control ankle exoskeletons. In: 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR). IEEE (2019)","key":"5_CR13"},{"doi-asserted-by":"crossref","unstructured":"Jing, T., et al.: Self-tuning threshold method for real-time gait phase detection based on ground contact forces using FSRs. Sensors 18.2, 481 (2018)","key":"5_CR14","DOI":"10.3390\/s18020481"},{"doi-asserted-by":"crossref","unstructured":"Xu, D., et al. \u201cOnline estimation of continuous gait phase for robotic transtibial prostheses based on adaptive oscillators. In: 2020 IEEE\/ASME International Conference on Advanced Intelligent Mechatronics (AIM). IEEE (2020)","key":"5_CR15","DOI":"10.1109\/AIM43001.2020.9158968"},{"issue":"4","key":"5_CR16","first-page":"7","volume":"175","author":"K Potdar","year":"2017","unstructured":"Potdar, K., Pardawala, T.S., Pai, C.D.: A comparative study of categorical variable encoding techniques for neural network classifiers. Int. J. Comput. Appl. 175(4), 7\u20139 (2017)","journal-title":"Int. J. Comput. Appl."},{"unstructured":"Saad, A., Mohammed, T.A., Al-Zawi, S.: Understanding of a convolutional neural network. In: 2017 International Conference on Engineering and Technology (ICET). IEEE (2017)","key":"5_CR17"},{"unstructured":"Li, Y., Yuan, Y.: Convergence analysis of two-layer neural networks with relu activation. Adv. Neural Inf. Process. Syst. 30 (2017)","key":"5_CR18"},{"unstructured":"Vincent, D., Visin, F.: A guide to convolution arithmetic for deep learning. arXiv preprint arXiv:1603.07285 (2016)","key":"5_CR19"}],"container-title":["Lecture Notes in Computer Science","Intelligent Robotics and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-13835-5_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,15]],"date-time":"2022-08-15T23:15:21Z","timestamp":1660605321000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-13835-5_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031138348","9783031138355"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-13835-5_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"4 August 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIRA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Robotics and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Harbin","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":"1 August 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 August 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icira2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icira2022.org\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"442","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":"284","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":"64% - 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":"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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}