The exponential growth of wearable healthcare devices market is fostered by the internetof- things (IoT) era. Connected smart biosensors enable a decentralized healthcare that does not constrain the user to be in a medical facility to get a real-time insight on his health status and a medical diagnosis from a doctor. Moreover, remote physiological monitoring is appealing in sport applications where athletes need real-time feedback on their level of dehydration and muscle fatigue in order to optimize their performances. Electrochemical sensors play a crucial role in physiology and healthcare monitoring since they provide information at molecular level, where the biosensor is in direct contact with bodily fluids such as sweat. A comprehensive healthcare diagnosis is achieved by continuously monitoring several types of biomarkers because of correlations between biological compounds. Namely, endogenous metabolites such as lactate, or potassium and ammonium ions, enable the quantification of muscle fatigue, hence, preventing muscle cramping. In therapeutic drug monitoring, exogenous compounds are continuously tracked so that the drug is maintained in its therapeutic range, in order to always be effective and not toxic for the patient. Besides multi-sensing and general-purpose capabilities, electrochemical platforms need to be correlated to the health and physiological status of the user, where the large amount of measured biological data must be accurately processed and interpreted by smart data analytic tools.

This thesis covers the design, implementation, characterization, and validation of hardware and software interfaces for multi-panel electrochemical sensing platforms. A multi-mode hardware front-end enabling voltammetric and potentiometric measurements is designed to provide a continuous and concurrent monitoring of endogenous metabolites, drugs, and electrolytes. This versatile and multi-sensing platform offers a portable solution for remote and comprehensive healthcare monitoring. Moreover, a multi-ion-sensing front-end is designed for accurate physiology in sweatsensing applications. The hardware is proposed as a solution for multiple electrolyte detection in artificial sweat samples. In such complex media, multi-ion-sensors are subject to interference from background electrolytes that considerably distorts sensor response. Therefore, a compact and analytical model of ion-sensing transduction mechanism is proposed to understand both qualitatively and quantitatively the non-linearity induced by these artifacts. The ion-sensor model is implemented at the core of an emulator of synthetic datasets that is built to simulate ion-sensor responses in artificial sweat samples. The emulator addresses the expensive time and chemical resources needed to acquire large database for training multivariate calibration models. Thus, the emulated data is used for the training and optimization of a multi-output support vector regressor that is proposed as an accurate, unbiased, robust, compact, low-complexity, and low-latency estimator for the multivariate calibration of multi-ion-sensors. Then, the multi-ion-sensing array, the analog front-end interface, and the chemometric model deployed on a Raspberry Pi, are seamlessly co-integrated for the monitoring of sodium, potassium, ammonium, and calcium ions in artificial sweat, within an IoT framework for real-time and accurate physiology.