{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T18:09:29Z","timestamp":1781114969236,"version":"3.54.1"},"reference-count":48,"publisher":"Public Library of Science (PLoS)","issue":"6","license":[{"start":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T00:00:00Z","timestamp":1781049600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52595111"],"award-info":[{"award-number":["52595111"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Mechanomyography (MMG) enables non-invasive monitoring of muscle mechanical activity, while its utility in time-resolved fatigue detection during dynamic human\u2013exoskeleton interaction remains underexplored. This pilot study explored the feasibility of combining MMG with machine learning to characterize neuromuscular fatigue and evaluate passive lower-limb exoskeleton assistance during repetitive 10\u2009kg squat-lifting tasks. MMG signals from five lower-limb muscles were extracted for time-domain, frequency-domain and nonlinear features, and fatigue identification was implemented via a spectral-based criterion and multi-muscle voting optimization. A radial basis function-enhanced random forest (RBF-RF) model integrated with data augmentation was validated through leave-one-subject-out cross-validation. The results demonstrated that the 1\/5 voting rule achieved optimal performance, with mean accuracy of 0.913\u2009\u00b1\u20090.057, AUC of 0.792\u2009\u00b1\u20090.073, and a low fatigue detection error of 1.4\u2009\u00b1\u20090.8\u2009s. Data augmentation steadily improved model robustness, and predicted fatigue levels were significantly correlated with subjective perceived exertion (\u03c1\u2009=\u20090.756, p\u2009&lt;\u20090.001). This pilot study demonstrates the feasibility of MMG-based fatigue monitoring for wearable assistive systems. The proposed framework supports objective, high-temporal-resolution fatigue monitoring, and may serve as a viable tool for assessing wearable assistive systems. Further large-cohort studies are required to validate its generalizability for practical applications.<\/jats:p>","DOI":"10.1371\/journal.pone.0350941","type":"journal-article","created":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T17:41:31Z","timestamp":1781113291000},"page":"e0350941","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Feasibility of mechanomyography-based fatigue classification for passive lower-limb exoskeleton evaluation: A pilot study"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-8874-8137","authenticated-orcid":true,"given":"Sijing","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5493-3699","authenticated-orcid":true,"given":"Xiaorong","family":"Guan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huibing","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Bai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiang","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,6,10]]},"reference":[{"key":"pone.0350941.ref001","doi-asserted-by":"crossref","first-page":"103582","DOI":"10.1016\/j.apergo.2021.103582","article-title":"Benchmarking occupational exoskeletons: An evidence mapping systematic review","volume":"98","author":"S De Bock","year":"2022","journal-title":"Appl Ergon"},{"key":"pone.0350941.ref002","volume-title":"5th European working conditions survey: overview report","author":"European Foundation for the Improvement of Living and Working Conditions","year":"2012"},{"issue":"5","key":"pone.0350941.ref003","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1080\/00140139.2015.1081988","article-title":"Exoskeletons for industrial application and their potential effects on physical work load","volume":"59","author":"MP de Looze","year":"2016","journal-title":"Ergonomics"},{"issue":"10","key":"pone.0350941.ref004","doi-asserted-by":"crossref","first-page":"1314","DOI":"10.1080\/00140130500274168","article-title":"Effect of concrete block weight and wall height on electromyographic activity and heart rate of masons","volume":"48","author":"D Anton","year":"2005","journal-title":"Ergonomics"},{"key":"pone.0350941.ref005","doi-asserted-by":"crossref","unstructured":"Memarian B, Mitropoulos P. 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