{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T20:01:37Z","timestamp":1780603297042,"version":"3.54.1"},"reference-count":33,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Biomedical Signal Processing and Control"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.bspc.2026.110652","type":"journal-article","created":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T12:00:16Z","timestamp":1779364816000},"page":"110652","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Statistical and geometric metrics of separability for selecting optimal signal segment in motor intention recognition"],"prefix":"10.1016","volume":"124","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7395-4978","authenticated-orcid":false,"given":"Miao","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ronglei","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyue","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"2","key":"10.1016\/j.bspc.2026.110652_b1","doi-asserted-by":"crossref","DOI":"10.3390\/s19020253","article-title":"Gradient-based multi-objective feature selection for gait mode recognition of transfemoral amputees","volume":"19","author":"Khademi","year":"2019","journal-title":"Sensors"},{"issue":"5","key":"10.1016\/j.bspc.2026.110652_b2","doi-asserted-by":"crossref","first-page":"1032","DOI":"10.1109\/TNSRE.2019.2909585","article-title":"A CNN-based method for intent recognition using inertial measurement units and intelligent lower limb prosthesis","volume":"27","author":"Su","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"issue":"10","key":"10.1016\/j.bspc.2026.110652_b3","doi-asserted-by":"crossref","first-page":"2015","DOI":"10.1109\/TNSRE.2018.2870152","article-title":"Real-time on-board recognition of continuous locomotion modes for amputees with robotic transtibial prostheses","volume":"26","author":"Xu","year":"2018","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"issue":"1","key":"10.1016\/j.bspc.2026.110652_b4","doi-asserted-by":"crossref","DOI":"10.1088\/1741-2552\/aa92a8","article-title":"Online adaptive neural control of a robotic lower limb prosthesis","volume":"15","author":"Spanias","year":"2018","journal-title":"J. Neural Eng."},{"issue":"11","key":"10.1016\/j.bspc.2026.110652_b5","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1080\/01691864.2023.2197966","article-title":"Evaluation of CNN algorithm at locomotion mode identification for a knee-assisted exoskeleton","volume":"37","author":"Xu","year":"2023","journal-title":"Adv. Robot."},{"issue":"2","key":"10.1016\/j.bspc.2026.110652_b6","doi-asserted-by":"crossref","first-page":"3890","DOI":"10.1109\/LRA.2022.3148799","article-title":"Selective assist strategy by using lightweight carbon frame exoskeleton robot","volume":"7","author":"Furukawa","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"issue":"11","key":"10.1016\/j.bspc.2026.110652_b7","doi-asserted-by":"crossref","first-page":"8693","DOI":"10.1109\/TNNLS.2022.3152255","article-title":"Gait prediction and variable admittance control for lower limb exoskeleton with measurement delay and extended-state-observer","volume":"34","author":"Chen","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"1","key":"10.1016\/j.bspc.2026.110652_b8","doi-asserted-by":"crossref","DOI":"10.1186\/s12984-021-00906-3","article-title":"Review of control strategies for lower-limb exoskeletons to assist gait","volume":"18","author":"Baud","year":"2021","journal-title":"J. NeuroEng. Rehabil."},{"issue":"23","key":"10.1016\/j.bspc.2026.110652_b9","doi-asserted-by":"crossref","first-page":"26964","DOI":"10.1109\/JSEN.2021.3121422","article-title":"An improved greedy reduction algorithm based on neighborhood rough set model for sensors screening of exoskeleton","volume":"21","author":"Qi","year":"2021","journal-title":"IEEE Sens. J."},{"issue":"3","key":"10.1016\/j.bspc.2026.110652_b10","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1631\/FITEE.1800601","article-title":"Recognition of walking environments and gait period by surface electromyography","volume":"20","author":"Kyeong","year":"2019","journal-title":"Front. Inf. Technol. Electron. Eng."},{"key":"10.1016\/j.bspc.2026.110652_b11","series-title":"2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems","first-page":"8164","article-title":"Minimal sensor setup in lower limb exoskeletons for motion classification based on multi-modal sensor data","author":"Patzer","year":"2019"},{"key":"10.1016\/j.bspc.2026.110652_b12","series-title":"2019 IEEE-RAS 19th International Conference on Humanoid Robots","first-page":"636","article-title":"Feature space exploration for motion classification based on multi-modal sensor data for lower limb exoskeletons","author":"Daab","year":"2019"},{"key":"10.1016\/j.bspc.2026.110652_b13","article-title":"A flexible lower extremity exoskeleton robot with deep locomotion mode identification","author":"Wang","year":"2018","journal-title":"Complexity"},{"issue":"3","key":"10.1016\/j.bspc.2026.110652_b14","doi-asserted-by":"crossref","first-page":"1062","DOI":"10.1109\/TBME.2022.3208381","article-title":"Joint kinematics, kinetics and muscle synergy patterns during transitions between locomotion modes","volume":"70","author":"Liu","year":"2023","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"12","key":"10.1016\/j.bspc.2026.110652_b15","doi-asserted-by":"crossref","first-page":"13072","DOI":"10.1109\/JSEN.2023.3267490","article-title":"Taking locomotion mode as prior: One algorithm-enabled gait events and kinematics prediction on various terrains","volume":"23","author":"Wei","year":"2023","journal-title":"IEEE Sens. J."},{"key":"10.1016\/j.bspc.2026.110652_b16","article-title":"Surface electromyogram feature set optimization for lower limb activity classification","author":"Gupta","year":"2021","journal-title":"IETE J. Res."},{"key":"10.1016\/j.bspc.2026.110652_b17","doi-asserted-by":"crossref","first-page":"33250","DOI":"10.1109\/ACCESS.2020.2971552","article-title":"Daily locomotion recognition and prediction: A kinematic data-based machine learning approach","volume":"8","author":"Figueiredo","year":"2020","journal-title":"IEEE Access"},{"issue":"1","key":"10.1016\/j.bspc.2026.110652_b18","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1109\/TNSRE.2019.2950096","article-title":"A low-cost end-to-end sEMG-based gait sub-phase recognition system","volume":"28","author":"Luo","year":"2020","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"issue":"3","key":"10.1016\/j.bspc.2026.110652_b19","doi-asserted-by":"crossref","first-page":"646","DOI":"10.1109\/TNSRE.2020.2966749","article-title":"Unsupervised cross-subject adaptation for predicting human locomotion intent","volume":"28","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"10.1016\/j.bspc.2026.110652_b20","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2020.105486","article-title":"Accurate recognition of lower limb ambulation mode based on surface electromyography and motion data using machine learning","volume":"193","author":"Zhou","year":"2020","journal-title":"Comput. Methods Programs Biomed."},{"issue":"9","key":"10.1016\/j.bspc.2026.110652_b21","doi-asserted-by":"crossref","first-page":"2556","DOI":"10.1109\/TBME.2019.2892084","article-title":"Evolving Gaussian process autoregression based learning of human motion intent using improved energy kernel method of EMG","volume":"66","author":"Zeng","year":"2019","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"22","key":"10.1016\/j.bspc.2026.110652_b22","doi-asserted-by":"crossref","DOI":"10.3390\/s19224887","article-title":"Subject- and environment-based sensor variability for wearable lower-limb assistive devices","volume":"19","author":"Krausz","year":"2019","journal-title":"Sensors"},{"issue":"3","key":"10.1016\/j.bspc.2026.110652_b23","doi-asserted-by":"crossref","first-page":"1282","DOI":"10.1109\/JSEN.2017.2782181","article-title":"Simultaneous Bayesian recognition of locomotion and gait phases with wearable sensors","volume":"18","author":"Martinez-Hernandez","year":"2018","journal-title":"IEEE Sens. J."},{"issue":"3","key":"10.1016\/j.bspc.2026.110652_b24","doi-asserted-by":"crossref","first-page":"1018","DOI":"10.1007\/s12555-020-0934-3","article-title":"Surface electromyography characteristics for motion intention recognition and implementation issues in lower-limb exoskeletons","volume":"20","author":"Kyeong","year":"2022","journal-title":"Int. J. Control Autom. Syst."},{"issue":"5","key":"10.1016\/j.bspc.2026.110652_b25","doi-asserted-by":"crossref","first-page":"1071","DOI":"10.1109\/TNSRE.2019.2911316","article-title":"Adaptive hybrid classifier for myoelectric pattern recognition against the interferences of outlier motion, muscle fatigue, and electrode doffing","volume":"27","author":"Ding","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"issue":"1","key":"10.1016\/j.bspc.2026.110652_b26","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1109\/TCDS.2020.2968845","article-title":"Exoskeleton online learning and estimation of human walking intention based on dynamical movement primitives","volume":"13","author":"Qiu","year":"2021","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"10.1016\/j.bspc.2026.110652_b27","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.106192","article-title":"An optimal signal selection method based on feature neighborhood using for human gait mode recognition","volume":"93","author":"Zhang","year":"2024","journal-title":"Biomed. Signal Process. Control"},{"issue":"3\u20134","key":"10.1016\/j.bspc.2026.110652_b28","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1093\/biomet\/24.3-4.471","article-title":"Certain generalizations in the analysis of variance","volume":"24","author":"Wilks","year":"1932","journal-title":"Biometrika"},{"issue":"1\u20132","key":"10.1016\/j.bspc.2026.110652_b29","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1093\/biomet\/35.1-2.58","article-title":"Tests of significance in multivariate analysis","volume":"35","author":"Rao","year":"1948","journal-title":"Biometrika"},{"key":"10.1016\/j.bspc.2026.110652_b30","series-title":"Linear Statistical Inference and its Applications","author":"Rao","year":"1973"},{"key":"10.1016\/j.bspc.2026.110652_b31","series-title":"Principles of Neural Science","author":"Kandel","year":"2021"},{"key":"10.1016\/j.bspc.2026.110652_b32","series-title":"Neumann\u2019s Kinesiology of the Musculoskeletal System: Foundations for Rehabilitation","author":"Neumann","year":"2010"},{"key":"10.1016\/j.bspc.2026.110652_b33","doi-asserted-by":"crossref","unstructured":"M. Yenugula, V.K. Kasula, A.R. Yadulla, B. Konda, S.R. Addula, C.M.M. Kotteti, Privacy-Preserving Decision Tree Classification Using Homomorphic Encryption in IoT Big Data Scenarios, in: 2025 IEEE 4th International Conference on Computing and Machine Intelligence, ICMI 2025 - Proceedings, ISBN: 979-833150913-2, 2025.","DOI":"10.1109\/ICMI65310.2025.11141083"}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426012061?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426012061?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T19:32:05Z","timestamp":1780601525000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809426012061"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":33,"alternative-id":["S1746809426012061"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110652","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Statistical and geometric metrics of separability for selecting optimal signal segment in motor intention recognition","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110652","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"110652"}}