Many sweat-based wearable monitoring systems have been recently proposed, but the data provided by those systems often lack a reliable and meaningful relation to standardized blood values. One clear example is lactate, a relevant biomarker for both sports and health sectors, with a complex sweat-blood bioequivalence. This limitation decreases its individual significance as a sweat-based biomarker. Taking into account the insights of previous studies, a multiparametric methodology has been proposed to predict blood lactate from non-invasive independent sensors: sweat lactate, sweat rate, and heart rate. The bioequivalence study was performed with a large set of volunteers (>30 subjects) in collaboration with sports institutions (Institut Nacional d'Educació Física de Catalunya, INEFC, and Centre d'Alt Rendiment, CAR, located in Spain). A neural network algorithm was used to predict blood lactate values from the sensor data and subject metadata. The developed methodology reliably and accurately predicted blood lactate absolute values, only adding 0.3 mM of accumulated error when compared to portable blood lactate meters, the current gold standard for sports clinicians. The approach proposed in this work, along with an integrated platform for sweat monitoring, will have a strong impact on the sports and health fields as an autonomous, real-time, and continuous monitoring tool.
Keywords: lactate monitoring; machine learning; multiparametric; sport; sweat analysis; wearable sensors.