{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T07:02:58Z","timestamp":1779692578728,"version":"3.53.1"},"reference-count":55,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T00:00:00Z","timestamp":1779667200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurorobot."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Adaptive lower-limb neurorobotics requires gaitd-state representations that preserve locomotor structure without reducing post-stroke walking to a single asymmetry score or opaque latent embedding. Because post-stroke gait is multimodal and side dependent, transparent side-aware representations may better support future adaptive-assistance design than modality-isolated summaries.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This secondary analysis used a public multimodal gait dataset comprising 138 able-bodied adults and 50 adults with stroke. The analytic space was restricted to 11 waveform domains shared across public exports: four sagittal kinematic waveforms and seven repository-normalized surface electromyography waveforms, each represented by 1,001 time-normalized points. Stroke waveforms were organized into paretic, non-paretic, bilateral-mean, and side-difference views, with side difference defined as paretic minus non-paretic. Domain-view functional principal component analysis retained 90% cumulative variance, capped at three components per block; family-level reduction retained 90% variance, capped at eight components. Candidate Ward hierarchical and K-means solutions from two to five states were screened in kinematics-only, sEMG-only, fused, paretic-only, and erector-spinae-excluded spaces.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The retained fused side-aware solution organized the strict complete-case stroke cohort (\n                      <jats:italic>n\u202f=<\/jats:italic>\n                      \u202f43) into three states: State 1 (\n                      <jats:italic>n\u202f=<\/jats:italic>\n                      \u202f12), State 2 (\n                      <jats:italic>n\u202f=<\/jats:italic>\n                      \u202f18), and State 3 (\n                      <jats:italic>n\u202f=<\/jats:italic>\n                      \u202f13). The strongest fused two-state K-means comparator showed higher compactness and resampling stability than the retained three-state solution [silhouette 0.189; bootstrap adjusted Rand index (ARI) 0.876 versus silhouette 0.155; bootstrap ARI 0.633]. However, the three-state solution was retained as a representation-level choice because it avoided trivial micro-clusters, preserved explicit multimodal side-aware structure, and enabled clearer waveform-level interpretation. Sensitivity analyses showed identical assignments after erector-spinae exclusion (ARI\u202f=\u202f1.000), partial concordance under robust scaling (ARI\u202f=\u202f0.785), and material reassignment when the block cap was reduced to two components (ARI\u202f=\u202f0.335). The strongest domain contributors were ankle angle (1.000), vastus lateralis sEMG (0.898), knee angle (0.866), gastrocnemius sEMG (0.851), and tibialis anterior sEMG (0.840).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Public waveform exports supported an internally interpretable, side-aware multimodal representation of post-stroke gait relevant to neurorobotic state-representation design. This contribution remains exploratory and representational, not clinical, interventional, real-time, or controller-validating; for future studies, it should be interpreted as a hypothesis-generating framework.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fnbot.2026.1863916","type":"journal-article","created":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T06:06:12Z","timestamp":1779689172000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Interpretable side-aware kinematic-sEMG gait-state representations relevant to adaptive neurorobotic assistance after stroke: a public-dataset study"],"prefix":"10.3389","volume":"20","author":[{"given":"Rocco Salvatore","family":"Calabr\u00f2","sequence":"first","affiliation":[{"name":"IRCCS Centro Neurolesi Bonino Pulejo","place":["Messina, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrea","family":"Calderone","sequence":"additional","affiliation":[{"name":"IRCCS Centro Neurolesi Bonino Pulejo","place":["Messina, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessio","family":"Baricich","sequence":"additional","affiliation":[{"name":"Department of Biomedical Sciences, Humanitas University","place":["Milan, Italy"]},{"name":"IRCCS Humanitas Research Hospital","place":["Milan, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrea","family":"Santamato","sequence":"additional","affiliation":[{"name":"Physical Medicine and Rehabilitative Unit-Riuniti Hospital, University of Foggia","place":["Foggia, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesca Antonia","family":"Arcadi","sequence":"additional","affiliation":[{"name":"IRCCS Centro Neurolesi Bonino Pulejo","place":["Messina, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessandro Marco","family":"De Nunzio","sequence":"additional","affiliation":[{"name":"Department of Research and Development, LUNEX International University of Health","place":["Differdange, Luxembourg"]},{"name":"Luxembourg Health and Sport Sciences Research Institute ASBL","place":["Differdange, Luxembourg"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Angelo","family":"Quartarone","sequence":"additional","affiliation":[{"name":"IRCCS Centro Neurolesi Bonino Pulejo","place":["Messina, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,5,25]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"1443010","DOI":"10.3389\/fnbot.2024.1443010","article-title":"Neuro-motor controlled wearable augmentations: current research and emerging trends","volume":"18","author":"Alsuradi","year":"2024","journal-title":"Front. Neurorobot."},{"key":"ref2","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1186\/s12911-020-01332-6","article-title":"Explainability for artificial intelligence in healthcare: a multidisciplinary perspective","volume":"20","author":"Amann","year":"2020","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"ref3","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.gaitpost.2024.04.014","article-title":"Current practices in clinical gait analysis in Europe: a comprehensive survey-based study from the European Society for Movement Analysis in adults and children (ESMAC) standard initiative","volume":"111","author":"Armand","year":"2024","journal-title":"Gait Posture"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"119","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."},{"key":"ref5","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1097\/NPT.0000000000000523","article-title":"From research to the clinic: using technology to measure gait in neurorehabilitation","volume":"49","author":"Cain","year":"2025","journal-title":"J. Neurol. Phys. Ther."},{"key":"ref6","doi-asserted-by":"publisher","first-page":"187","DOI":"10.5535\/arm.250093","article-title":"Wearable robots for rehabilitation and assistance of gait: a narrative review","volume":"49","author":"Cha","year":"2025","journal-title":"Ann. Rehabil. 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Neurorobot."},{"key":"ref17","doi-asserted-by":"publisher","first-page":"e18473","DOI":"10.7717\/peerj.18473","article-title":"Comparative electromyography analysis of subphase gait disorder in chronic stroke survivors","volume":"12","author":"Hohsoh","year":"2024","journal-title":"PeerJ."},{"key":"ref18","doi-asserted-by":"publisher","first-page":"214156","DOI":"10.1155\/2014\/214156","article-title":"Assessment of waveform similarity in clinical gait data: the linear fit method","volume":"2014","author":"Iosa","year":"2014","journal-title":"Biomed. Res. Int."},{"key":"ref19","doi-asserted-by":"publisher","first-page":"8090","DOI":"10.3390\/s24248090","article-title":"Review on portable-powered lower limb exoskeletons","volume":"24","author":"Jiang","year":"2024","journal-title":"Sensors"},{"key":"ref20","doi-asserted-by":"publisher","first-page":"4304","DOI":"10.1109\/TNSRE.2024.3506974","article-title":"A relationship model between optimized exoskeleton assistance and gait conditions improves multi-gait human-in-the-loop optimization performance","volume":"32","author":"Jing","year":"2024","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref21","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1186\/s12984-025-01553-8","article-title":"Within-session propulsion asymmetry changes have a limited effect on gait asymmetry post-stroke","volume":"22","author":"Kettlety","year":"2025","journal-title":"J. Neuroeng. 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Rep."},{"key":"ref30","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1016\/S0966-6362(02)00165-0","article-title":"Use of cluster analysis for gait pattern classification of patients in the early and late recovery phases following stroke","volume":"18","author":"Mulroy","year":"2003","journal-title":"Gait Posture"},{"key":"ref31","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1186\/s12984-025-01605-z","article-title":"Systematic review of AI\/ML applications in multi-domain robotic rehabilitation: trends, gaps, and future directions","volume":"22","author":"Nicora","year":"2025","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref32","doi-asserted-by":"publisher","first-page":"3522","DOI":"10.1109\/TNSRE.2024.3457770","article-title":"Modeling the heterogeneity of post-stroke gait control in free-living environments using a personalized causal network","volume":"32","author":"Nishi","year":"2024","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref33","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1080\/10255842.2010.527837","article-title":"One-dimensional statistical parametric mapping in Python","volume":"15","author":"Pataky","year":"2012","journal-title":"Comput. Methods Biomech. Biomed. Engin."},{"key":"ref34","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1016\/j.gaitpost.2009.10.014","article-title":"Evaluation of gait symmetry after stroke: a comparison of current methods and recommendations for standardization","volume":"31","author":"Patterson","year":"2010","journal-title":"Gait Posture"},{"key":"ref35","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1016\/j.apmr.2007.08.142","article-title":"Gait asymmetry in community-ambulating stroke survivors","volume":"89","author":"Patterson","year":"2008","journal-title":"Arch. Phys. Med. Rehabil."},{"key":"ref36","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1016\/j.gaitpost.2024.12.009","article-title":"Status of surface electromyography assessment as part of clinical gait analysis in the management of patients with cerebral palsy: outcomes of a Delphi process","volume":"117","author":"Reisig","year":"2025","journal-title":"Gait Posture"},{"key":"ref37","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1186\/s12984-026-01892-0","article-title":"Explainability in AI-enabled medical neurotechnology: a scoping review","volume":"23","author":"Schopp","year":"2026","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref38","doi-asserted-by":"publisher","first-page":"6585","DOI":"10.3390\/s24206585","article-title":"AI applications in adult stroke recovery and rehabilitation: a scoping review using AI","volume":"24","author":"Senadheera","year":"2024","journal-title":"Sensors"},{"key":"ref39","doi-asserted-by":"publisher","first-page":"2017","DOI":"10.1016\/j.jcmg.2020.07.015","article-title":"Proposed requirements for cardiovascular imaging-related machine learning evaluation (PRIME): a checklist: reviewed by the American College of Cardiology Healthcare Innovation Council","volume":"13","author":"Sengupta","year":"2020","journal-title":"JACC Cardiovasc. Imaging"},{"key":"ref40","doi-asserted-by":"publisher","first-page":"456","DOI":"10.1038\/s41551-022-00984-1","article-title":"Opportunities and challenges in the development of exoskeletons for locomotor assistance","volume":"7","author":"Siviy","year":"2023","journal-title":"Nat Biomed Eng."},{"key":"ref41","doi-asserted-by":"publisher","first-page":"511","DOI":"10.3390\/diagnostics15050511","article-title":"Typical changes in gait biomechanics in patients with subacute ischemic stroke","volume":"15","author":"Skvortsov","year":"2025","journal-title":"Diagnostics"},{"key":"ref42","doi-asserted-by":"publisher","first-page":"617","DOI":"10.3390\/biomimetics10090617","article-title":"Deep learning models optimization for gait phase identification from EMG data during exoskeleton-assisted walking","volume":"10","author":"Soldi","year":"2025","journal-title":"Biomimetics"},{"key":"ref43","doi-asserted-by":"publisher","first-page":"1186175","DOI":"10.3389\/fnbot.2023.1186175","article-title":"Review of adaptive control for stroke lower limb exoskeleton rehabilitation robot based on motion intention recognition","volume":"17","author":"Su","year":"2023","journal-title":"Front. Neurorobot."},{"key":"ref44","doi-asserted-by":"publisher","first-page":"102780","DOI":"10.1016\/j.artmed.2024.102780","article-title":"Designing explainable AI to improve human-AI team performance: a medical stakeholder-driven scoping review","volume":"149","author":"Subramanian","year":"2024","journal-title":"Artif. Intell. Med."},{"key":"ref45","doi-asserted-by":"publisher","first-page":"102693","DOI":"10.1016\/j.artmed.2023.102693","article-title":"Artificial intelligence in physical rehabilitation: a systematic review","volume":"146","author":"Sumner","year":"2023","journal-title":"Artif. Intell. Med."},{"key":"ref46","doi-asserted-by":"publisher","first-page":"852","DOI":"10.1038\/s41597-023-02767-y","article-title":"A full-body motion capture gait dataset of 138 able-bodied adults across the life span and 50 stroke survivors","volume":"10","author":"Van Criekinge","year":"2023","journal-title":"Sci. Data"},{"key":"ref47","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1016\/j.gaitpost.2017.03.004","article-title":"Trunk biomechanics during hemiplegic gait after stroke: a systematic review","volume":"54","author":"Van Criekinge","year":"2017","journal-title":"Gait Posture"},{"key":"ref48","doi-asserted-by":"publisher","first-page":"1453","DOI":"10.1016\/S0140-6736(07)61602-X","article-title":"The strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies","volume":"370","author":"von Elm","year":"2007","journal-title":"Lancet"},{"key":"ref49","doi-asserted-by":"publisher","first-page":"704226","DOI":"10.3389\/fnbot.2021.704226","article-title":"A novel sEMG-based gait phase-kinematics-coupled predictor and its interaction with exoskeletons","volume":"15","author":"Wei","year":"2021","journal-title":"Front. Neurorobot."},{"key":"ref50","doi-asserted-by":"publisher","first-page":"1671344","DOI":"10.3389\/fbioe.2025.1671344","article-title":"Explainable artificial intelligence for gait analysis: advances, pitfalls, and challenges - a systematic review","volume":"13","author":"Xiang","year":"2025","journal-title":"Front. Bioeng. Biotechnol."},{"key":"ref51","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1016\/j.gaitpost.2025.05.010","article-title":"Stroke-related factors associated with gait asymmetry in ambulatory stroke survivors: a systematic review and meta-analysis","volume":"121","author":"Xu","year":"2025","journal-title":"Gait Posture"},{"key":"ref52","doi-asserted-by":"publisher","first-page":"1068706","DOI":"10.3389\/fnbot.2022.1068706","article-title":"Continuous mode adaptation for cable-driven rehabilitation robot using reinforcement learning","volume":"16","author":"Yang","year":"2022","journal-title":"Front. 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