{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T15:49:19Z","timestamp":1775144959567,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":16,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819512324","type":"print"},{"value":"9789819512331","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,8,19]],"date-time":"2025-08-19T00:00:00Z","timestamp":1755561600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,19]],"date-time":"2025-08-19T00:00:00Z","timestamp":1755561600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-95-1233-1_31","type":"book-chapter","created":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T06:05:08Z","timestamp":1755842708000},"page":"337-346","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Noise-Suppression Neural Network for\u00a0Upper Limb Continuous Motion Prediction"],"prefix":"10.1007","author":[{"given":"Kai","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Keping","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zenghui","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongbo","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhifei","family":"Zhai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,8,19]]},"reference":[{"key":"31_CR1","doi-asserted-by":"crossref","unstructured":"Li, H., Guo, S., Bu, D., Wang, H., Kawanishi, M.: Subject-independent estimation of continuous movements using CNN-LSTM for a home-based upper limb rehabilitation system. IEEE Robot. Autom. Lett. 8(10), 6403\u20136410 (2023)","DOI":"10.1109\/LRA.2023.3303701"},{"key":"31_CR2","doi-asserted-by":"crossref","unstructured":"Liu, L., Feng, J., Li, J., Chen, W., Mao, Z., Tan, X.: Multi-layer CNN-LSTM network with self-attention mechanism for robust estimation of nonlinear uncertain systems. Front. Neurosci. 18, 1379495 (2024)","DOI":"10.3389\/fnins.2024.1379495"},{"key":"31_CR3","doi-asserted-by":"crossref","unstructured":"Wei, Z., Li, M., Zhang, Z.-Q., Xie, S.Q.: Continuous prediction of wrist joint kinematics using surface electromyography from the perspective of muscle anatomy and muscle synergy feature extraction. IEEE J. Biomed. Health Inform. 29(1), 43\u201355 (2025)","DOI":"10.1109\/JBHI.2024.3484994"},{"key":"31_CR4","doi-asserted-by":"crossref","unstructured":"Sun, Z., Xu, C., Jin, L., Pang, Z., Yu, J.: Human-robot interaction force control of series elastic actuator-driven upper limb exoskeleton robot. IEEE Trans. Ind. Electron. 72(5), 5093\u20135104 (2025)","DOI":"10.1109\/TIE.2024.3468711"},{"key":"31_CR5","doi-asserted-by":"crossref","unstructured":"Li, H., Guo, S., Wang, H., Bu, D.: Subject-independent continuous estimation of SEMG-based joint angles using both multisource domain adaptation and BP neural network. IEEE Trans. Instrum. Meas. 72, 1\u201310 (2022)","DOI":"10.1109\/TIM.2022.3225015"},{"key":"31_CR6","doi-asserted-by":"crossref","unstructured":"Wang, L., et al.: A novel hybrid unsupervised domain adaptation method for cross-subject joint angle estimation from surface electromyography. IEEE Robot. Autom. Lett. 8(11), 7257\u20137264 (2023)","DOI":"10.1109\/LRA.2023.3317680"},{"key":"31_CR7","doi-asserted-by":"crossref","unstructured":"Bao, T., Zaidi, S.A.R., Xie, S., Yang, P., Zhang, Z.-Q.: Inter-subject domain adaptation for CNN-based wrist kinematics estimation using SEMG. IEEE Trans. Neural Syst. Rehabil. Eng. 29, 1068\u20131078 (2021)","DOI":"10.1109\/TNSRE.2021.3086401"},{"key":"31_CR8","doi-asserted-by":"crossref","unstructured":"Li, H., Guo, S., Bu, D., Wang, H.: A two-stage GA-based SEMG feature selection method for user-independent continuous estimation of elbow angles. IEEE Trans. Instrum. Meas. 72, 1\u20139 (2023)","DOI":"10.1109\/TIM.2023.3276522"},{"key":"31_CR9","doi-asserted-by":"crossref","unstructured":"Wang, G., et al.: Recurrent neural network enabled continuous motion estimation of lower limb joints from incomplete SEMG signals. IEEE Trans. Neural Syst. Rehabil. Eng. 32, 3577\u20133589 (2024)","DOI":"10.1109\/TNSRE.2024.3459924"},{"key":"31_CR10","doi-asserted-by":"crossref","unstructured":"Tang, S., Xiao, X., Sun, Z., Hu, Y., Chen, H.: An anti-noise disturbance fuzzy neural dynamics for manipulability optimization of omnidirectional mobile redundant manipulator. IEEE Trans. Fuzzy Syst. (2025). https:\/\/doi.org\/10.1109\/TFUZZ.2025.3554817","DOI":"10.1109\/TFUZZ.2025.3554817"},{"key":"31_CR11","doi-asserted-by":"crossref","unstructured":"Wang, J., Cao, D., Liu, H., Wu, Y.: Prediction of joint angles for human elbow motion based on SEMG. In: 2024 China Automation Congress, pp. 2252\u20132257 (2024)","DOI":"10.1109\/CAC63892.2024.10865675"},{"key":"31_CR12","doi-asserted-by":"crossref","unstructured":"Zeng, M., Gu, J., Feng, Y.: Motion prediction based on SEMG-transformer for lower limb exoskeleton robot control. In: 2023 International Conference on Advanced Robotics and Mechatronics, pp. 864\u2013869 (2023)","DOI":"10.1109\/ICARM58088.2023.10218920"},{"key":"31_CR13","doi-asserted-by":"crossref","unstructured":"Gao, S., Wen, S., Ren, C., Liu, J., Wu, Q., Han, L., Wang, F.: The transfer learning method for reducing the influence of electrode shift. In: 2023 IEEE 13th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, pp. 1241\u20131246 (2023)","DOI":"10.1109\/CYBER59472.2023.10256265"},{"key":"31_CR14","doi-asserted-by":"crossref","unstructured":"Zangene, A.R., Samuel, O.W., Abbasi, A., Nazarpour, K., McEwan, A.A., Li, G.: An attention-based bidirectional LSTM model for continuous cross-subject estimation of knee joint angle during running from SEMG signals. In: 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, pp. 1\u20134 (2023)","DOI":"10.1109\/EMBC40787.2023.10340791"},{"key":"31_CR15","unstructured":"Kumar, R., Gupta, A., Muthukrishnan, S.P., Kumar, L., Roy, S.: SEMG-driven physics-informed gated recurrent networks for modeling upper limb multi-joint movement dynamics. arXiv preprint: https:\/\/doi.org\/10.48550\/arXiv.2408.16599 (2024)"},{"key":"31_CR16","doi-asserted-by":"crossref","unstructured":"Stango, A., Negro, F., Farina, D.: Spatial correlation of high density EMG signals provides features robust to electrode number and shift in pattern recognition for Myocontrol. IEEE Trans. Neural Syst. Rehabil. Eng. 23(2), 189\u2013198 (2014)","DOI":"10.1109\/TNSRE.2014.2366752"}],"container-title":["Lecture Notes in Computer Science","Advances in Neural Networks \u2013 ISNN 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-1233-1_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T14:51:59Z","timestamp":1775141519000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-1233-1_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,19]]},"ISBN":["9789819512324","9789819512331"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-1233-1_31","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,19]]},"assertion":[{"value":"19 August 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISNN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Zhangye","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":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 August 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 August 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"isnn2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conference.cs.cityu.edu.hk\/isnn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}