Wearable sweat biosensing systems for continuous physiological monitoring in endurance and team sports: a review

作者
Yipeng Yao,Shanshan Xiang
出处
期刊:Tm-technisches Messen [R. Oldenbourg Verlag]
卷期号:93 (9-10): 461-479
标识
DOI:10.1515/teme-2026-0002
摘要

Abstract Wearable sweat biosensing is progressing from laboratory prototypes to skin-conformable platforms that can inform individualized hydration, exertion, and recovery decisions. This review synthesizes advances in microfluidic sampling architectures that stabilize analyte delivery and enable sweat-rate estimation, electrode materials and interface designs that reduce biofouling and motion artifacts, and system-level integration with wireless electronics and analytics. Across representative on-body demonstrations, lactate remains the most intensively explored metabolite, yet its physiological interpretation is debated because sweat levels may reflect local gland metabolism rather than blood filtration and are strongly modulated by sweat rate and time lag. Reported wearable lactate devices nevertheless achieve practical operating windows, with linear ranges of 1–25 mmol L −1 and up to 1–50 mmol L −1 , response times <55 s to <90 s, and typical limits of detection around 0.22 mmol L −1 ; exceptional approaches have pushed detection down to 8.9 × 10 −12 mol L −1 . Analytical validation against laboratory methods is strong, with correlation coefficients of R 2 ≈ 0.95–0.97 reported in comparative studies. In contrast, electrolyte monitoring is more mature and directly actionable for hydration management: potentiometric ion-selective sensors commonly cover 10 −4 –10 −1 mol L −1 , respond in <30 s, and show high agreement with reference assays ( R 2 > 0.98 versus ICP-MS for Na + /K + ). Beyond electrolytes and common metabolites, emerging targets such as cortisol and cytokines occur at picomolar-to-nanomolar levels, motivating miniaturized immunoassays and multi-analyte panels. Finally, we outline how data-driven fusion of biochemical signals with biomechanics can correct artifacts and enable decision support, while emphasizing that standardized, large-cohort, real-world validation remains the central bottleneck for translation.

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