{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T05:04:51Z","timestamp":1789707891707,"version":"4.0.1"},"reference-count":46,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T00:00:00Z","timestamp":1789689600000},"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. Psychol."],"abstract":"\n Background<\/jats:title>\n Interpersonal autonomic physiology (IAP) research has established that physiological synchrony (PS), the temporal interdependence of autonomic nervous system (ANS) activity across individuals, is an established phenomenon in team-based settings. However, PS in athlete cohorts has been studied exclusively as a passive observational variable; no framework actively regulates autonomic coherence under the communication constraints of wireless body area networks. This study integrates IAP and distributed control theory by formulating team-level autonomic coherence as a leaderless multi-agent consensus problem.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n \n Each wearable unit was modeled as a two-dimensional linear agent whose state encodes cardiovascular autonomic tone (parasympathetic index: HRV-RMSSD) and sympathetic arousal load (EDA skin-conductance level, SCL), consistent with the Banister Fitness-Fatigue Model. Initial model states were derived from ECG\/EDA records of WESAD subjects S2-S7, used only to provide heterogeneous simulation initial conditions (correlations were directional, non-significant sanity checks:\n r<\/jats:italic>\n = \u22120.71,\n p<\/jats:italic>\n = 0.114 for RMSSD;\n r<\/jats:italic>\n = 0.68,\n p<\/jats:italic>\n = 0.137 for SCL;\n N<\/jats:italic>\n = 6). Three engineering challenges were addressed: (i) BLE communication delays\n h<\/jats:italic>\n \n c<\/jats:italic>\n <\/jats:sub>\n \u2208 [15, 150] ms; (ii) neuromuscular input delays\n h<\/jats:italic>\n \n i<\/jats:italic>\n <\/jats:sub>\n \u2208 [150, 300] ms; and (iii) switching directed topologies from dynamic team formations. A distributed dynamic event-triggered mechanism (DETM) drove all agents toward consensus while transmitting data only upon detection of meaningful state deviation. Stability was established via a dual-integral Lyapunov-Krasovskii functional, with LMI feasibility verified by CVXPY (\u03b5 = 0.087). Simulations ran for 300 s under three delay scenarios with five initial-condition perturbation folds; battery life was estimated from a four-component nRF52840 energy model.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n The proposed DETM reduced BLE transmissions by 38.38% in the nominal case while achieving convergence of the normalized model states within 4\u20137 s. A Zeno-free guarantee (\n T<\/jats:italic>\n min<\/jats:sub>\n = 0.06 s) and a theoretical battery-life estimate of about 36 days were obtained. The ablation analysis attributed the largest communication saving to the dynamic internal variable \u03c1\n \n i<\/jats:italic>\n <\/jats:sub>\n (\n t<\/jats:italic>\n ) and the largest convergence-time improvement to the dual-delay structure. Five initial-condition perturbation folds indicated numerical robustness to the starting states.\n <\/jats:p>\n <\/jats:sec>\n \n Discussion<\/jats:title>\n These findings support control-theoretic and network-level feasibility only. Athlete studies are required before physiological benefits, safety, or real-world effectiveness can be inferred.<\/jats:p>\n <\/jats:sec>","DOI":"10.3389\/fpsyg.2026.1858564","type":"journal-article","created":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T04:31:02Z","timestamp":1789705862000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A distributed event-triggered framework for coordinating athlete autonomic states via wearable sensor networks"],"prefix":"10.3389","volume":"17","author":[{"given":"Chao","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Physical Education, Dalian University of Science and Technology","place":["Dalian, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shixin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Dalian Jiaotong University","place":["Dalian, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,18]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"2526","DOI":"10.1002\/rnc.5404","article-title":"Distributed fixed-time event-triggered consensus of linear multi-agent systems with input delay","volume":"31","author":"Ai","year":"2021","journal-title":"Int. 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