{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,11]],"date-time":"2026-10-11T17:46:36Z","timestamp":1791740796488,"version":"4.3.4"},"reference-count":55,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T00:00:00Z","timestamp":1791417600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Proc Inst Mech Eng H"],"abstract":"<jats:p>\n                    Muscle fatigue is recognized as a multi-muscle, coordination-driven phenomenon rather than an isolated single-muscle response. This study investigates whether joint and multiplex recurrence network (JMRN) features derived from surface electromyography (sEMG) signals capture fatigue-related changes in inter-muscle coordination during dynamic contractions. Signals are recorded from gastrocnemius medialis (GM), gastrocnemius lateralis (GL), and soleus (SOL) along with toe-force measurements of 40 male subjects (\n                    <jats:italic toggle=\"yes\">N<\/jats:italic>\n                    \u2009=\u200940) during dynamic calf raise test (CRT) to exhaustion. Un-truncated sEMG bursts across all repetitions (mean 32.65\u2009\u00b1\u20098.18 bursts; range: 18\u201353 bursts: 1306 bursts total) are mapped into phase space (\n                    <jats:italic toggle=\"yes\">\u03c4<\/jats:italic>\n                    \u2009=\u200910 samples\u2009=\u20095.0\u2009ms,\n                    <jats:italic toggle=\"yes\">m<\/jats:italic>\n                    \u2009=\u20094, downsampled to 100 nodes per burst) to construct fuzzy recurrence layers (kNN\u2009=\u200910). Network features including multiplex edge overlap, pairwise cosine coupling, joint strength, and density are extracted. A coordination fatigue index (CFI) is derived using leave-one-subject-out cross-validated (LOSO-CV) principal component analysis (PCA) to track fatigue progression, with the first principal component (PC1) explaining 84.06% of total variance. The proposed CFI showed statistically significant monotonic association with subjective fatigue onset (\n                    <jats:italic toggle=\"yes\">N<\/jats:italic>\n                    \u2009=\u200940: Spearman\n                    <jats:italic toggle=\"yes\">\u03c1<\/jats:italic>\n                    \u2009=\u20090.393,\n                    <jats:italic toggle=\"yes\">p<\/jats:italic>\n                    \u2009=\u20090.0122, mean absolute error (MAE) =\u20098.60 bursts, accuracy\u2009\u00b1\u20095\u2009=\u200967.50%, Bland-Altman bias: \u22120.11 bursts) and force-based fatigue onset (\n                    <jats:italic toggle=\"yes\">n<\/jats:italic>\n                    \u2009=\u200914:\n                    <jats:italic toggle=\"yes\">\u03c1<\/jats:italic>\n                    \u2009=\u20090.425,\n                    <jats:italic toggle=\"yes\">p<\/jats:italic>\n                    \u2009=\u20090.130, MAE\u2009=\u20093.64 bursts). Coordination features showed significant post-fatigue increases (\n                    <jats:italic toggle=\"yes\">\n                      p\n                      <jats:sub>FDR<\/jats:sub>\n                    <\/jats:italic>\n                    \u2009&lt;\u20090.005, Benjamini-Hochberg corrected), reflecting elevated spatial synchronization. The JMRN framework provides a non-invasive approach for studying multi-muscle interactions during dynamic fatigue progression.\n                  <\/jats:p>","DOI":"10.1177\/09544119261493766","type":"journal-article","created":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T10:58:06Z","timestamp":1791457086000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Joint and multiplex recurrence network analysis of muscle coordination during fatigue progression using sEMG"],"prefix":"10.1177","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-8350-3960","authenticated-orcid":false,"given":"Abhijith","family":"M.","sequence":"first","affiliation":[{"name":"Biomedical Instrumentation and Signal Processing Laboratory, Department of Instrumentation and Control Engineering, N.S.S. College of Engineering Palakkad, Affiliated to APJ Abdul Kalam Technological University, Thiruvananthapuram, Kerala, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2021-7824","authenticated-orcid":false,"given":"Venugopal","family":"Gopinath","sequence":"additional","affiliation":[{"name":"Biomedical Instrumentation and Signal Processing Laboratory, Department of Instrumentation and Control Engineering, N.S.S. College of Engineering Palakkad, Affiliated to APJ Abdul Kalam Technological University, Thiruvananthapuram, Kerala, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,10,8]]},"reference":[{"key":"e_1_3_4_2_2","doi-asserted-by":"crossref","unstructured":"F\u00e9asson L Camdessanch\u00e9 JP El Mhandi L et al. Fatigue and neuromuscular diseases. 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